1
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Rolls ET, Treves A. A theory of hippocampal function: New developments. Prog Neurobiol 2024; 238:102636. [PMID: 38834132 DOI: 10.1016/j.pneurobio.2024.102636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/15/2024] [Accepted: 05/30/2024] [Indexed: 06/06/2024]
Abstract
We develop further here the only quantitative theory of the storage of information in the hippocampal episodic memory system and its recall back to the neocortex. The theory is upgraded to account for a revolution in understanding of spatial representations in the primate, including human, hippocampus, that go beyond the place where the individual is located, to the location being viewed in a scene. This is fundamental to much primate episodic memory and navigation: functions supported in humans by pathways that build 'where' spatial view representations by feature combinations in a ventromedial visual cortical stream, separate from those for 'what' object and face information to the inferior temporal visual cortex, and for reward information from the orbitofrontal cortex. Key new computational developments include the capacity of the CA3 attractor network for storing whole charts of space; how the correlations inherent in self-organizing continuous spatial representations impact the storage capacity; how the CA3 network can combine continuous spatial and discrete object and reward representations; the roles of the rewards that reach the hippocampus in the later consolidation into long-term memory in part via cholinergic pathways from the orbitofrontal cortex; and new ways of analysing neocortical information storage using Potts networks.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.
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2
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Jiang Y. A theory of the neural mechanisms underlying negative cognitive bias in major depression. Front Psychiatry 2024; 15:1348474. [PMID: 38532986 PMCID: PMC10963437 DOI: 10.3389/fpsyt.2024.1348474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 02/16/2024] [Indexed: 03/28/2024] Open
Abstract
The widely acknowledged cognitive theory of depression, developed by Aaron Beck, focused on biased information processing that emphasizes the negative aspects of affective and conceptual information. Current attempts to discover the neurological mechanism underlying such cognitive and affective bias have successfully identified various brain regions associated with severally biased functions such as emotion, attention, rumination, and inhibition control. However, the neurobiological mechanisms of how individuals in depression develop this selective processing toward negative is still under question. This paper introduces a neurological framework centered around the frontal-limbic circuit, specifically analyzing and synthesizing the activity and functional connectivity within the amygdala, hippocampus, and medial prefrontal cortex. Firstly, a possible explanation of how the positive feedback loop contributes to the persistent hyperactivity of the amygdala in depression at an automatic level is established. Building upon this, two hypotheses are presented: hypothesis 1 revolves around the bidirectional amygdalohippocampal projection facilitating the amplification of negative emotions and memories while concurrently contributing to the impediment of the retrieval of opposing information in the hippocampus attractor network. Hypothesis 2 highlights the involvement of the ventromedial prefrontal cortex in the establishment of a negative cognitive framework through the generalization of conceptual and emotional information in conjunction with the amygdala and hippocampus. The primary objective of this study is to improve and complement existing pathological models of depression, pushing the frontiers of current understanding in neuroscience of affective disorders, and eventually contributing to successful recovery from the debilitating affective disorders.
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Affiliation(s)
- Yuyue Jiang
- University of California, Santa Barbara, Santa Barbara, CA, United States
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3
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Boscaglia M, Gastaldi C, Gerstner W, Quian Quiroga R. A dynamic attractor network model of memory formation, reinforcement and forgetting. PLoS Comput Biol 2023; 19:e1011727. [PMID: 38117859 PMCID: PMC10766193 DOI: 10.1371/journal.pcbi.1011727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 01/04/2024] [Accepted: 12/02/2023] [Indexed: 12/22/2023] Open
Abstract
Empirical evidence shows that memories that are frequently revisited are easy to recall, and that familiar items involve larger hippocampal representations than less familiar ones. In line with these observations, here we develop a modelling approach to provide a mechanistic understanding of how hippocampal neural assemblies evolve differently, depending on the frequency of presentation of the stimuli. For this, we added an online Hebbian learning rule, background firing activity, neural adaptation and heterosynaptic plasticity to a rate attractor network model, thus creating dynamic memory representations that can persist, increase or fade according to the frequency of presentation of the corresponding memory patterns. Specifically, we show that a dynamic interplay between Hebbian learning and background firing activity can explain the relationship between the memory assembly sizes and their frequency of stimulation. Frequently stimulated assemblies increase their size independently from each other (i.e. creating orthogonal representations that do not share neurons, thus avoiding interference). Importantly, connections between neurons of assemblies that are not further stimulated become labile so that these neurons can be recruited by other assemblies, providing a neuronal mechanism of forgetting.
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Affiliation(s)
- Marta Boscaglia
- Centre for Systems Neuroscience, University of Leicester, United Kingdom
- School of Psychology and Vision Sciences, University of Leicester, United Kingdom
| | - Chiara Gastaldi
- School of Computer and Communication Sciences and School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
| | - Wulfram Gerstner
- School of Computer and Communication Sciences and School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
| | - Rodrigo Quian Quiroga
- Centre for Systems Neuroscience, University of Leicester, United Kingdom
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Ruijin hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
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4
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Milstein AD, Tran S, Ng G, Soltesz I. Offline memory replay in recurrent neuronal networks emerges from constraints on online dynamics. J Physiol 2023; 601:3241-3264. [PMID: 35907087 PMCID: PMC9885000 DOI: 10.1113/jp283216] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 07/22/2022] [Indexed: 02/01/2023] Open
Abstract
During spatial exploration, neural circuits in the hippocampus store memories of sequences of sensory events encountered in the environment. When sensory information is absent during 'offline' resting periods, brief neuronal population bursts can 'replay' sequences of activity that resemble bouts of sensory experience. These sequences can occur in either forward or reverse order, and can even include spatial trajectories that have not been experienced, but are consistent with the topology of the environment. The neural circuit mechanisms underlying this variable and flexible sequence generation are unknown. Here we demonstrate in a recurrent spiking network model of hippocampal area CA3 that experimental constraints on network dynamics such as population sparsity, stimulus selectivity, rhythmicity and spike rate adaptation, as well as associative synaptic connectivity, enable additional emergent properties, including variable offline memory replay. In an online stimulus-driven state, we observed the emergence of neuronal sequences that swept from representations of past to future stimuli on the timescale of the theta rhythm. In an offline state driven only by noise, the network generated both forward and reverse neuronal sequences, and recapitulated the experimental observation that offline memory replay events tend to include salient locations like the site of a reward. These results demonstrate that biological constraints on the dynamics of recurrent neural circuits are sufficient to enable memories of sensory events stored in the strengths of synaptic connections to be flexibly read out during rest and sleep, which is thought to be important for memory consolidation and planning of future behaviour. KEY POINTS: A recurrent spiking network model of hippocampal area CA3 was optimized to recapitulate experimentally observed network dynamics during simulated spatial exploration. During simulated offline rest, the network exhibited the emergent property of generating flexible forward, reverse and mixed direction memory replay events. Network perturbations and analysis of model diversity and degeneracy identified associative synaptic connectivity and key features of network dynamics as important for offline sequence generation. Network simulations demonstrate that population over-representation of salient positions like the site of reward results in biased memory replay.
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Affiliation(s)
- Aaron D. Milstein
- Department of Neurosurgery, Stanford University School of Medicine, Stanford CA
- Department of Neuroscience and Cell Biology, Robert Wood Johnson Medical School and Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, NJ
| | - Sarah Tran
- Department of Neurosurgery, Stanford University School of Medicine, Stanford CA
| | - Grace Ng
- Department of Neurosurgery, Stanford University School of Medicine, Stanford CA
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University School of Medicine, Stanford CA
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5
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Yoon HG, Kim P. STDP-based associative memory formation and retrieval. J Math Biol 2023; 86:49. [PMID: 36826758 DOI: 10.1007/s00285-023-01883-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 12/11/2022] [Accepted: 01/31/2023] [Indexed: 02/25/2023]
Abstract
Spike-timing-dependent plasticity (STDP) is a biological process in which the precise order and timing of neuronal spikes affect the degree of synaptic modification. While there has been numerous research focusing on the role of STDP in neural coding, the functional implications of STDP at the macroscopic level in the brain have not been fully explored yet. In this work, we propose a neurodynamical model based on STDP that renders storage and retrieval of a group of associative memories. We showed that the function of STDP at the macroscopic level is to form a "memory plane" in the neural state space which dynamically encodes high dimensional data. We derived the analytic relation between the input, the memory plane, and the induced macroscopic neural oscillations around the memory plane. Such plane produces a limit cycle in reaction to a similar memory cue, which can be used for retrieval of the original input.
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Affiliation(s)
- Hong-Gyu Yoon
- Department of Mathematical Sciences, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City, 44919, Republic of Korea
| | - Pilwon Kim
- Department of Mathematical Sciences, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City, 44919, Republic of Korea.
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6
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Redman WT, Wolcott NS, Montelisciani L, Luna G, Marks TD, Sit KK, Yu CH, Smith S, Goard MJ. Long-term transverse imaging of the hippocampus with glass microperiscopes. eLife 2022; 11:75391. [PMID: 35775393 PMCID: PMC9249394 DOI: 10.7554/elife.75391] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 06/12/2022] [Indexed: 11/19/2022] Open
Abstract
The hippocampus consists of a stereotyped neuronal circuit repeated along the septal-temporal axis. This transverse circuit contains distinct subfields with stereotyped connectivity that support crucial cognitive processes, including episodic and spatial memory. However, comprehensive measurements across the transverse hippocampal circuit in vivo are intractable with existing techniques. Here, we developed an approach for two-photon imaging of the transverse hippocampal plane in awake mice via implanted glass microperiscopes, allowing optical access to the major hippocampal subfields and to the dendritic arbor of pyramidal neurons. Using this approach, we tracked dendritic morphological dynamics on CA1 apical dendrites and characterized spine turnover. We then used calcium imaging to quantify the prevalence of place and speed cells across subfields. Finally, we measured the anatomical distribution of spatial information, finding a non-uniform distribution of spatial selectivity along the DG-to-CA1 axis. This approach extends the existing toolbox for structural and functional measurements of hippocampal circuitry.
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Affiliation(s)
- William T Redman
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, United States
| | - Nora S Wolcott
- Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, United States
| | - Luca Montelisciani
- Cognitive and Systems Neuroscience Group, University of Amsterdam, Amsterdam, Netherlands
| | - Gabriel Luna
- Neuroscience Research Institute, University of California, Santa Barbara, United States
| | - Tyler D Marks
- Neuroscience Research Institute, University of California, Santa Barbara, United States
| | - Kevin K Sit
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, United States
| | - Che-Hang Yu
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, United States
| | - Spencer Smith
- Neuroscience Research Institute, University of California, Santa Barbara, United States.,Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, United States
| | - Michael J Goard
- Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, United States.,Neuroscience Research Institute, University of California, Santa Barbara, United States.,Department of Psychological and Brain Sciences, University of California, Santa Barbara, United States
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7
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Larner A. Transient global amnesia: model, mechanism, hypothesis. Cortex 2022; 149:137-147. [DOI: 10.1016/j.cortex.2022.01.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Revised: 11/13/2021] [Accepted: 01/19/2022] [Indexed: 01/03/2023]
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8
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Gastaldi C, Schwalger T, De Falco E, Quiroga RQ, Gerstner W. When shared concept cells support associations: Theory of overlapping memory engrams. PLoS Comput Biol 2021; 17:e1009691. [PMID: 34968383 PMCID: PMC8754331 DOI: 10.1371/journal.pcbi.1009691] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 01/12/2022] [Accepted: 11/29/2021] [Indexed: 12/02/2022] Open
Abstract
Assemblies of neurons, called concepts cells, encode acquired concepts in human Medial Temporal Lobe. Those concept cells that are shared between two assemblies have been hypothesized to encode associations between concepts. Here we test this hypothesis in a computational model of attractor neural networks. We find that for concepts encoded in sparse neural assemblies there is a minimal fraction cmin of neurons shared between assemblies below which associations cannot be reliably implemented; and a maximal fraction cmax of shared neurons above which single concepts can no longer be retrieved. In the presence of a periodically modulated background signal, such as hippocampal oscillations, recall takes the form of association chains reminiscent of those postulated by theories of free recall of words. Predictions of an iterative overlap-generating model match experimental data on the number of concepts to which a neuron responds. Experimental evidence suggests that associations between concepts are encoded in the hippocampus by cells shared between neuronal assemblies (“overlap” of concepts). What is the necessary overlap that ensures a reliable encoding of associations? Under which conditions can associations induce a simultaneous or a chain-like activation of concepts? Our theoretical model shows that the ideal overlap presents a tradeoff: the overlap should be larger than a minimum value in order to reliably encode associations, but lower than a maximum value to prevent loss of individual memories. Our theory explains experimental data from human Medial Temporal Lobe and provides a mechanism for chain-like recall in presence of inhibition, while still allowing for simultaneous recall if inhibition is weak.
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Affiliation(s)
- Chiara Gastaldi
- School of Computer and Communication Sciences and School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- * E-mail:
| | - Tilo Schwalger
- Institut für Mathematik, Technische Universität Berlin, Berlin, Germany
| | - Emanuela De Falco
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Rodrigo Quian Quiroga
- Centre for Systems Neuroscience, University of Leicester, Leicester, United Kingdom
- Peng Cheng Laboratory, Shenzhen, China
| | - Wulfram Gerstner
- School of Computer and Communication Sciences and School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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9
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Sutton NM, Ascoli GA. Spiking Neural Networks and Hippocampal Function: A Web-Accessible Survey of Simulations, Modeling Methods, and Underlying Theories. COGN SYST RES 2021; 70:80-92. [PMID: 34504394 DOI: 10.1016/j.cogsys.2021.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Computational modeling has contributed to hippocampal research in a wide variety of ways and through a large diversity of approaches, reflecting the many advanced cognitive roles of this brain region. The intensively studied neuron type circuitry of the hippocampus is a particularly conducive substrate for spiking neural models. Here we present an online knowledge base of spiking neural network simulations of hippocampal functions. First, we overview theories involving the hippocampal formation in subjects such as spatial representation, learning, and memory. Then we describe an original literature mining process to organize published reports in various key aspects, including: (i) subject area (e.g., navigation, pattern completion, epilepsy); (ii) level of modeling detail (Hodgkin-Huxley, integrate-and-fire, etc.); and (iii) theoretical framework (attractor dynamics, oscillatory interference, self-organizing maps, and others). Moreover, every peer-reviewed publication is also annotated to indicate the specific neuron types represented in the network simulation, establishing a direct link with the Hippocampome.org portal. The web interface of the knowledge base enables dynamic content browsing and advanced searches, and consistently presents evidence supporting every annotation. Moreover, users are given access to several types of statistical reports about the collection, a selection of which is summarized in this paper. This open access resource thus provides an interactive platform to survey spiking neural network models of hippocampal functions, compare available computational methods, and foster ideas for suitable new directions of research.
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Affiliation(s)
- Nate M Sutton
- Department of Bioengineering, 4400 University Drive, George Mason University, Fairfax, Virginia, 22030 (USA)
| | - Giorgio A Ascoli
- Department of Bioengineering, 4400 University Drive, George Mason University, Fairfax, Virginia, 22030 (USA).,Interdepartmental Neuroscience Program, 4400 University Drive, George Mason University, Fairfax, Virginia, 22030 (USA)
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10
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Santos-Pata D, Amil AF, Raikov IG, Rennó-Costa C, Mura A, Soltesz I, Verschure PFMJ. Epistemic Autonomy: Self-supervised Learning in the Mammalian Hippocampus. Trends Cogn Sci 2021; 25:582-595. [PMID: 33906817 PMCID: PMC10631471 DOI: 10.1016/j.tics.2021.03.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 03/23/2021] [Accepted: 03/26/2021] [Indexed: 01/05/2023]
Abstract
Biological cognition is based on the ability to autonomously acquire knowledge, or epistemic autonomy. Such self-supervision is largely absent in artificial neural networks (ANN) because they depend on externally set learning criteria. Yet training ANN using error backpropagation has created the current revolution in artificial intelligence, raising the question of whether the epistemic autonomy displayed in biological cognition can be achieved with error backpropagation-based learning. We present evidence suggesting that the entorhinal-hippocampal complex combines epistemic autonomy with error backpropagation. Specifically, we propose that the hippocampus minimizes the error between its input and output signals through a modulatory counter-current inhibitory network. We further discuss the computational emulation of this principle and analyze it in the context of autonomous cognitive systems.
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Affiliation(s)
- Diogo Santos-Pata
- Laboratory of Synthetic, Perceptive, Emotive and Cognitive Systems (SPECS), Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain
| | - Adrián F Amil
- Laboratory of Synthetic, Perceptive, Emotive and Cognitive Systems (SPECS), Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | | | - César Rennó-Costa
- Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Anna Mura
- Laboratory of Synthetic, Perceptive, Emotive and Cognitive Systems (SPECS), Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Paul F M J Verschure
- Laboratory of Synthetic, Perceptive, Emotive and Cognitive Systems (SPECS), Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain; Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain.
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11
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Yang C, Naya Y. Hippocampal cells integrate past memory and present perception for the future. PLoS Biol 2020; 18:e3000876. [PMID: 33206640 PMCID: PMC7673575 DOI: 10.1371/journal.pbio.3000876] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 09/22/2020] [Indexed: 11/18/2022] Open
Abstract
The ability to use stored information in a highly flexible manner is a defining feature of the declarative memory system. However, the neuronal mechanisms underlying this flexibility are poorly understood. To address this question, we recorded single-unit activity from the hippocampus of 2 nonhuman primates performing a newly devised task requiring the monkeys to retrieve long-term item-location association memory and then use it flexibly in different circumstances. We found that hippocampal neurons signaled both mnemonic information representing the retrieved location and perceptual information representing the external circumstance. The 2 signals were combined at a single-neuron level to construct goal-directed information by 3 sequentially occurring neuronal operations (e.g., convergence, transference, and targeting) in the hippocampus. Thus, flexible use of knowledge may be supported by the hippocampal constructive process linking memory and perception, which may fit the mnemonic information into the current situation to present manageable information for a subsequent action. This study reveals that three neuronal operations in the macaque hippocampus combine retrieved memory and incoming perceptual information to construct goal-directed information; this constructive memory process may equip us to use past knowledge flexibly according to the current situation.
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Affiliation(s)
- Cen Yang
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Center for Life Sciences, Peking University, Beijing, China
| | - Yuji Naya
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China
- Center for Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
- Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
- * E-mail:
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12
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Sampath S, Srivastava V. On stability and associative recall of memories in attractor neural networks. PLoS One 2020; 15:e0238054. [PMID: 32941475 PMCID: PMC7498056 DOI: 10.1371/journal.pone.0238054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 08/10/2020] [Indexed: 11/21/2022] Open
Abstract
Attractor neural networks such as the Hopfield model can be used to model associative memory. An efficient associative memory should be able to store a large number of patterns which must all be stable. We study in detail the meaning and definition of stability of network states. We reexamine the meanings of retrieval, recognition and recall and assign precise mathematical meanings to each of these terms. We also examine the relation between them and how they relate to memory capacity of the network. We have shown earlier in this journal that orthogonalization scheme provides an effective way of overcoming catastrophic interference that limits the memory capacity of the Hopfield model. It is not immediately apparent whether the improvement made by orthgonalization affects the processes of retrieval, recognition and recall equally. We show that this influence occurs to different degrees and hence affects the relations between them. We then show that the conditions for pattern stability can be split into a necessary condition (recognition) and a sufficient one (recall). We interpret in cognitive terms the information being stored in the Hopfield model and also after it is orthogonalized. We also study the alterations in the network dynamics of the Hopfield network upon the introduction of orthogonalization, and their effects on the efficiency of the network as an associative memory.
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Affiliation(s)
- Suchitra Sampath
- Centre for Neural and Cognitive Sciences, University of Hyderabad, Hyderabad, Telangana, India
- * E-mail:
| | - Vipin Srivastava
- School of Physics, University of Hyderabad, Hyderabad, Telangana, India
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13
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On the boundary conditions of avoidance memory reconsolidation: An attractor network perspective. Neural Netw 2020; 127:96-109. [DOI: 10.1016/j.neunet.2020.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 04/09/2020] [Accepted: 04/14/2020] [Indexed: 11/21/2022]
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14
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Tyrtyshnaia A, Manzhulo I, Konovalova S, Zagliadkina A. Neuropathic Pain Causes a Decrease in the Dendritic Tree Complexity of Hippocampal CA3 Pyramidal Neurons. Cells Tissues Organs 2020; 208:89-100. [DOI: 10.1159/000506812] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Accepted: 02/26/2020] [Indexed: 11/19/2022] Open
Abstract
The International Pain Association defines neuropathic pain as “an unpleasant sensory and emotional experience associated with actual or potential tissue damage.” Recent studies show that chronic neuropathic pain causes both morphological and functional changes within brain structures. Due to the impact of supraspinal centers on pain signal processing, patients with chronic pain often suffer from depression, anxiety, memory impairment, and learning disabilities. Changes in hippocampal neuronal and glial plasticity can play a substantial role in the development of these symptoms. Given the special role of the CA3 hippocampal area in chronic stress reactions, we suggested that this region may undergo significant morphological changes as a result of persistent pain. Since the CA3 area is involved in the implementation of hippocampus-dependent memory, changes in the neuronal morphology can cause cognitive impairment observed in chronic neuropathic pain. This study aimed to elucidate the structural and plastic changes within the hippocampus associated with dendritic tree atrophy of CA3 pyramidal neurons in mice with chronic sciatic nerve constriction. Behavioral testing revealed impaired working and long-term memory in mice with a chronic constriction injury. Using the Golgi-Cox method, we revealed a decrease in the number of branches and dendritic length of CA3 pyramidal neurons. The dendritic spine number was decreased, predominantly due to a reduction in mushroom spines. An immunohistochemical study showed changes in astro- and microglial activity, which could affect the morphology of neurons both directly and indirectly via the regulation of neurotrophic factor synthesis. Using ELISA, we found a decrease in brain-derived neurotrophic factor production and an increase in neurotrophin-3 production. Morphological and biochemical changes in the CA3 area are accompanied by impaired working and long-term memory of animals. Thus, we can conclude that morphological and biochemical changes within the CA3 hippocampal area may underlie the cognitive impairment in neuropathic pain.
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15
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Curtin P, Austin C, Curtin A, Gennings C, Figueroa-Romero C, Mikhail KA, Botero TM, Goutman SA, Feldman EL, Arora M. Dysregulated biodynamics in metabolic attractor systems precede the emergence of amyotrophic lateral sclerosis. PLoS Comput Biol 2020; 16:e1007773. [PMID: 32294079 PMCID: PMC7159190 DOI: 10.1371/journal.pcbi.1007773] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 03/04/2020] [Indexed: 11/19/2022] Open
Abstract
Evolutionarily conserved mechanisms maintain homeostasis of essential elements, and are believed to be highly time-variant. However, current approaches measure elemental biomarkers at a few discrete time-points, ignoring complex higher-order dynamical features. To study dynamical properties of elemental homeostasis, we apply laser ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) to tooth samples to generate 500 temporally sequential measurements of elemental concentrations from birth to 10 years. We applied dynamical system and Information Theory-based analyses to reveal the longest-known attractor system in mammalian biology underlying the metabolism of nutrient elements, and identify distinct and consistent transitions between stable and unstable states throughout development. Extending these dynamical features to disease prediction, we find that attractor topography of nutrient metabolism is altered in amyotrophic lateral sclerosis (ALS), as early as childhood, suggesting these pathways are involved in disease risk. Mechanistic analysis was undertaken in a transgenic mouse model of ALS, where we find similar marked disruptions in elemental attractor systems as in humans. Our results demonstrate the application of a phenomological analysis of dynamical systems underlying elemental metabolism, and emphasize the utility of these measures in characterizing risk of disease. The metabolism of essential elements in early life is essential to healthy growth and development. Elemental homeostasis is typically studied by characterizing distributions of elemental concentrations at the level of the population. Here, we introduce a new method of characterizing elemental metabolism at the level of the individual. Using tooth-based biomarkers, we tracked the longitudinal trajectory of essential elements throughout childhood at weekly temporal resolution from birth through approximately 10 years of life. We analyzed these trajectories to identify the formation of stable dynamic states (attractors) and transitions between these states throughout development. We found that metabolic dynamics were specific to discrete elemental pathways; copper metabolism typically involved the formation of multiple discrete states throughout childhood, whereas other elements, such as zinc, tended to persist in a single stable dynamic throughout development. Next, we compared elemental biodynamics in neurologically healthy cases and subjects that were later diagnosed with amyotrophic lateral sclerosis (ALS). We found these patterns were dysregulated in ALS, and also found similar results in a mouse model of ALS. Overall, our results provide a novel approach to characterize elemental biodynamics throughout development, and emphasize that the dysregulation of these processes may be predictive of later onset of disease.
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Affiliation(s)
- Paul Curtin
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- * E-mail: (PC); (MA)
| | - Christine Austin
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Austen Curtin
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Chris Gennings
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | | | - Kristen A. Mikhail
- Department of Neurology, University of Michigan, Ann Arbor, MI, United States of America
| | - Tatiana M. Botero
- Department of Cariology, Restorative Sciences and Endodontics, School of Dentistry University of Michigan, Ann Arbor, MI, United States of America
| | - Stephen A. Goutman
- Department of Neurology, University of Michigan, Ann Arbor, MI, United States of America
| | - Eva L. Feldman
- Department of Neurology, University of Michigan, Ann Arbor, MI, United States of America
| | - Manish Arora
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- * E-mail: (PC); (MA)
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16
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Wu X, Mel GC, Strouse DJ, Mel BW. How Dendrites Affect Online Recognition Memory. PLoS Comput Biol 2019; 15:e1006892. [PMID: 31050662 PMCID: PMC6527246 DOI: 10.1371/journal.pcbi.1006892] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 05/20/2019] [Accepted: 02/18/2019] [Indexed: 11/18/2022] Open
Abstract
In order to record the stream of autobiographical information that defines our unique personal history, our brains must form durable memories from single brief exposures to the patterned stimuli that impinge on them continuously throughout life. However, little is known about the computational strategies or neural mechanisms that underlie the brain's ability to perform this type of "online" learning. Based on increasing evidence that dendrites act as both signaling and learning units in the brain, we developed an analytical model that relates online recognition memory capacity to roughly a dozen dendritic, network, pattern, and task-related parameters. We used the model to determine what dendrite size maximizes storage capacity under varying assumptions about pattern density and noise level. We show that over a several-fold range of both of these parameters, and over multiple orders-of-magnitude of memory size, capacity is maximized when dendrites contain a few hundred synapses-roughly the natural number found in memory-related areas of the brain. Thus, in comparison to entire neurons, dendrites increase storage capacity by providing a larger number of better-sized learning units. Our model provides the first normative theory that explains how dendrites increase the brain's capacity for online learning; predicts which combinations of parameter settings we should expect to find in the brain under normal operating conditions; leads to novel interpretations of an array of existing experimental results; and provides a tool for understanding which changes associated with neurological disorders, aging, or stress are most likely to produce memory deficits-knowledge that could eventually help in the design of improved clinical treatments for memory loss.
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Affiliation(s)
- Xundong Wu
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Gabriel C. Mel
- Computer Science Department, University of Southern California, Los Angeles, CA, United States
| | - D. J. Strouse
- Physics Department, Princeton University, Princeton, NJ, United States
| | - Bartlett W. Mel
- Biomedical Engineering Department and Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States
- * E-mail:
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17
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Rennó-Costa C, Teixeira DG, Soltesz I. Regulation of gamma-frequency oscillation by feedforward inhibition: A computational modeling study. Hippocampus 2019; 29:957-970. [PMID: 30990954 DOI: 10.1002/hipo.23093] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2018] [Revised: 03/07/2019] [Accepted: 03/30/2019] [Indexed: 11/05/2022]
Abstract
Throughout the brain, reciprocally connected excitatory and inhibitory neurons interact to produce gamma-frequency oscillations. The emergent gamma rhythm synchronizes local neural activity and helps to select which cells should fire in each cycle. We previously found that such excitation-inhibition microcircuits, however, have a potentially significant caveat: the frequency of the gamma oscillation and the level of selection (i.e., the percentage of cells that are allowed to fire) vary with the magnitude of the input signal. In networks with varying levels of brain activity, such a feature may produce undesirable instability on the time and spatial structure of the neural signal with a potential for adversely impacting important neural processing mechanisms. Here we propose that feedforward inhibition solves the latter instability problem of the excitation-inhibition microcircuit. Using computer simulations, we show that the feedforward inhibitory signal reduces the dependence of both the frequency of population oscillation and the level of selection on the magnitude of the input excitation. Such a mechanism can produce stable gamma oscillations with its frequency determined only by the properties of the feedforward network, as observed in the hippocampus. As feedforward and feedback inhibition motifs commonly appear together in the brain, we hypothesize that their interaction underlies a robust implementation of general computational principles of neural processing involved in several cognitive tasks, including the formation of cell assemblies and the routing of information between brain areas.
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Affiliation(s)
- César Rennó-Costa
- Bioinformatics Multidisciplinary Environment (BioME), Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil.,Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Daniel Garcia Teixeira
- Bioinformatics Multidisciplinary Environment (BioME), Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil.,Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil.,Federal Institute of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, California
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18
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Idiart MAP, Villavicencio A, Katz B, Rennó-Costa C, Lisman J. How the Brain Represents Language and Answers Questions? Using an AI System to Understand the Underlying Neurobiological Mechanisms. Front Comput Neurosci 2019; 13:12. [PMID: 30930761 PMCID: PMC6430033 DOI: 10.3389/fncom.2019.00012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 02/14/2019] [Indexed: 11/13/2022] Open
Abstract
To understand the computations that underlie high-level cognitive processes we propose a framework of mechanisms that could in principle implement START, an AI program that answers questions using natural language. START organizes a sentence into a series of triplets, each containing three elements (subject, verb, object). We propose that the brain similarly defines triplets and then chunks the three elements into a spatial pattern. A complete sentence can be represented using up to 7 triplets in a working memory buffer organized by theta and gamma oscillations. This buffer can transfer information into long-term memory networks where a second chunking operation converts the serial triplets into a single spatial pattern in a network, with each triplet (with corresponding elements) represented in specialized subregions. The triplets that define a sentence become synaptically linked, thereby encoding the sentence in synaptic weights. When a question is posed, there is a search for the closest stored memory (having the greatest number of shared triplets). We have devised a search process that does not require that the question and the stored memory have the same number of triplets or have triplets in the same order. Once the most similar memory is recalled and undergoes 2-level dechunking, the sought for information can be obtained by element-by-element comparison of the key triplet in the question to the corresponding triplet in the retrieved memory. This search may require a reordering to align corresponding triplets, the use of pointers that link different triplets, or the use of semantic memory. Our framework uses 12 network processes; existing models can implement many of these, but in other cases we can only suggest neural implementations. Overall, our scheme provides the first view of how language-based question answering could be implemented by the brain.
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Affiliation(s)
- Marco A. P. Idiart
- Department of Physics, Institute of Physics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil,*Correspondence: Marco A. P. Idiart
| | - Aline Villavicencio
- Department of Theoretical Informatics, Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil,School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
| | - Boris Katz
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - César Rennó-Costa
- Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
| | - John Lisman
- Volen Center for Complex Systems, Brandeis University, Waltham, MA, United States
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19
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Rennó-Costa C, da Silva ACC, Blanco W, Ribeiro S. Computational models of memory consolidation and long-term synaptic plasticity during sleep. Neurobiol Learn Mem 2018; 160:32-47. [PMID: 30321652 DOI: 10.1016/j.nlm.2018.10.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 09/18/2018] [Accepted: 10/11/2018] [Indexed: 12/19/2022]
Abstract
The brain stores memories by persistently changing the connectivity between neurons. Sleep is known to be critical for these changes to endure. Research on the neurobiology of sleep and the mechanisms of long-term synaptic plasticity has provided data in support of various theories of how brain activity during sleep affects long-term synaptic plasticity. The experimental findings - and therefore the theories - are apparently quite contradictory, with some evidence pointing to a role of sleep in the forgetting of irrelevant memories, whereas other results indicate that sleep supports the reinforcement of the most valuable recollections. A unified theoretical framework is in need. Computational modeling and simulation provide grounds for the quantitative testing and comparison of theoretical predictions and observed data, and might serve as a strategy to organize the rather complicated and diverse pool of data and methodologies used in sleep research. This review article outlines the emerging progress in the computational modeling and simulation of the main theories on the role of sleep in memory consolidation.
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Affiliation(s)
- César Rennó-Costa
- BioMe - Bioinformatics Multidisciplinary Environment, Federal University of Rio Grande do Norte, Natal, Brazil; Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Ana Cláudia Costa da Silva
- BioMe - Bioinformatics Multidisciplinary Environment, Federal University of Rio Grande do Norte, Natal, Brazil; Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil; Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil; Federal University of Paraiba, João Pessoa, Brazil
| | - Wilfredo Blanco
- BioMe - Bioinformatics Multidisciplinary Environment, Federal University of Rio Grande do Norte, Natal, Brazil; Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil; State University of Rio Grande do Norte, Natal, Brazil
| | - Sidarta Ribeiro
- Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil.
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20
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A novel pyramidal cell type promotes sharp-wave synchronization in the hippocampus. Nat Neurosci 2018; 21:985-995. [PMID: 29915194 DOI: 10.1038/s41593-018-0172-7] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Accepted: 04/26/2018] [Indexed: 01/05/2023]
Abstract
To support cognitive function, the CA3 region of the hippocampus performs computations involving attractor dynamics. Understanding how cellular and ensemble activities of CA3 neurons enable computation is critical for elucidating the neural correlates of cognition. Here we show that CA3 comprises not only classically described pyramid cells with thorny excrescences, but also includes previously unidentified 'athorny' pyramid cells that lack mossy-fiber input. Moreover, the two neuron types have distinct morphological and physiological phenotypes and are differentially modulated by acetylcholine. To understand the contribution of these athorny pyramid neurons to circuit function, we measured cell-type-specific firing patterns during sharp-wave synchronization events in vivo and recapitulated these dynamics with an attractor network model comprising two principal cell types. Our data and simulations reveal a key role for athorny cell bursting in the initiation of sharp waves: transient network attractor states that signify the execution of pattern completion computations vital to cognitive function.
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21
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Kumaran D, Hassabis D, McClelland JL. What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated. Trends Cogn Sci 2018; 20:512-534. [PMID: 27315762 DOI: 10.1016/j.tics.2016.05.004] [Citation(s) in RCA: 222] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Revised: 04/22/2016] [Accepted: 05/03/2016] [Indexed: 12/17/2022]
Abstract
We update complementary learning systems (CLS) theory, which holds that intelligent agents must possess two learning systems, instantiated in mammalians in neocortex and hippocampus. The first gradually acquires structured knowledge representations while the second quickly learns the specifics of individual experiences. We broaden the role of replay of hippocampal memories in the theory, noting that replay allows goal-dependent weighting of experience statistics. We also address recent challenges to the theory and extend it by showing that recurrent activation of hippocampal traces can support some forms of generalization and that neocortical learning can be rapid for information that is consistent with known structure. Finally, we note the relevance of the theory to the design of artificial intelligent agents, highlighting connections between neuroscience and machine learning.
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Affiliation(s)
- Dharshan Kumaran
- Google DeepMind, 5 New Street Square, London EC4A 3TW, UK; Institute of Cognitive Neuroscience, University College London, 17 Queen Square, WC1N 3AR, UK.
| | - Demis Hassabis
- Google DeepMind, 5 New Street Square, London EC4A 3TW, UK; Gatsby Computational Neuroscience Unit, 17 Queen Square, London WC1N 3AR, UK.
| | - James L McClelland
- Department of Psychology and Center for Mind, Brain, and Computation, Stanford University, 450 Serra Mall, CA 94305, USA.
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22
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Poli D, Wheeler BC, DeMarse TB, Brewer GJ. Pattern separation and completion of distinct axonal inputs transmitted via micro-tunnels between co-cultured hippocampal dentate, CA3, CA1 and entorhinal cortex networks. J Neural Eng 2018; 15:046009. [PMID: 29623900 DOI: 10.1088/1741-2552/aabc20] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
OBJECTIVE Functions ascribed to the hippocampal sub-regions for encoding episodic memories include the separation of activity patterns propagated from the entorhinal cortex (EC) into the dentate gyrus (DG) and pattern completion in CA3 region. Since a direct assessment of these functions is lacking at the level of specific axonal inputs, our goal is to directly measure the separation and completion of distinct axonal inputs in engineered pairs of hippocampal sub-regional circuits. APPROACH We co-cultured EC-DG, DG-CA3, CA3-CA1 or CA1-EC neurons in a two-chamber PDMS device over a micro-electrode array (MEA60), inter-connected via distinct axons that grow through the micro-tunnels between the compartments. Taking advantage of the axonal accessibility, we quantified pattern separation and completion of the evoked activity transmitted through the tunnels from source into target well. Since pattern separation can be inferred when inputs are more correlated than outputs, we first compared the correlations among axonal inputs with those of target somata outputs. We then compared, in an analog approach, the distributions of correlation distances between rate patterns of the axonal inputs inside the tunnels with those of the somata outputs evoked in the target well. Finally, in a digital approach, we measured the spatial population distances between binary patterns of the same axonal inputs and somata outputs. MAIN RESULTS We found the strongest separation of the propagated axonal inputs when EC was axonally connected to DG, with a decline in separation to CA3 and to CA1 for both rate and digital approaches. Furthermore, the digital approach showed stronger pattern completion in CA3, then CA1 and EC. SIGNIFICANCE To the best of our knowledge, these are the first direct measures of pattern separation and completion for axonal transmission to the somata target outputs at the rate and digital population levels in each of four stages of the EC-DG-CA3-CA1 circuit.
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Affiliation(s)
- Daniele Poli
- Department of Biomedical Engineering, University of California, Irvine, CA, United States of America. Research Center 'Enrico Piaggio', University of Pisa, Pisa, Italy
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23
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24
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Scharfman HE, MacLusky NJ. Sex differences in hippocampal area CA3 pyramidal cells. J Neurosci Res 2017; 95:563-575. [PMID: 27870399 DOI: 10.1002/jnr.23927] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2016] [Revised: 08/14/2016] [Accepted: 08/24/2016] [Indexed: 11/07/2022]
Abstract
Numerous studies have demonstrated differences between males and females in hippocampal structure, function, and plasticity. There also are many studies about the different predisposition of a males and females for disorders where the hippocampus plays an important role. Many of these reports focus on area CA1, but other subfields are also very important, and unlikely to be the same as area CA1 based on what is known. Here we review basic studies of male and female structure, function, and plasticity of area CA3 pyramidal cells of adult rats. The data suggest that the CA3 pyramidal cells of males and females are distinct in structure, function, and plasticity. These sex differences cannot be simply explained by the effects of circulating gonadal hormones. This view agrees with previous studies showing that there are substantial sex differences in the brain that cannot be normalized by removing the gonads and depleting peripheral gonadal hormones. Implications of these comparisons for understanding sex differences in hippocampal function and dysfunction are discussed. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Helen E Scharfman
- Department of Child and Adolescent Psychiatry, Physiology and Neuroscience, and Psychiatry, New York University Langone Medical Center, New York, New York.,Department of Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York
| | - Neil J MacLusky
- Department of Biomedical Sciences, University of Guelph, Guelph, Ontario, Canada
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25
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Abstract
UNLABELLED The hippocampus is thought to contribute to episodic memory by creating, storing, and reactivating patterns that are unique to each experience, including different experiences that happen at the same location. Hippocampus can combine spatial and contextual/episodic information using a dual coding scheme known as "global" and "rate" remapping. Global remapping selects which set of neurons can activate at a given location. Rate remapping readjusts the firing rates of this set depending on current experience, thus expressing experience-unique patterns at each location. But can the experience-unique component be retrieved spontaneously? Whereas reactivation of recent, spatially selective patterns in hippocampus is well established, it is never perfect, raising the issue of whether the experiential component might be absent. This question is key to the hypothesis that hippocampus can assist memory consolidation by reactivating and broadcasting experience-specific "index codes" to neocortex. In CA3, global remapping exhibits attractor-like dynamics, whereas rate remapping apparently does not, leading to the hypothesis that only the former can be retrieved associatively and casting doubt on the general consolidation hypothesis. Therefore, we studied whether the rate component is reactivated spontaneously during sleep. We conducted neural ensemble recordings from CA3 while rats ran on a circular track in different directions (in different sessions) and while they slept. It was shown previously that the two directions of running result in strong rate remapping. During sleep, the most recent rate distribution was reactivated preferentially. Therefore, CA3 can retrieve patterns spontaneously that are unique to both the location and the content of recent experience. SIGNIFICANCE STATEMENT The hippocampus is required for memory of events and their spatial contexts. The primary correlate of hippocampal activity is location in space, but multiple memories can occur in the same location. To be useful for distinguishing these memories, the hippocampus must be able, not only to express, but also to retrieve both spatial and nonspatial information about events. Whether it can retrieve nonspatial information has been challenged recently. We exposed rats to two different experiences (running in different directions) in the same locations and showed that even the nonspatial components of hippocampal cell firing are reactivated spontaneously during sleep, supporting the conclusion that both types of information about a recent experience can be retrieved.
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26
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Place and Grid Cells in a Loop: Implications for Memory Function and Spatial Coding. J Neurosci 2017; 37:8062-8076. [PMID: 28701481 DOI: 10.1523/jneurosci.3490-16.2017] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Revised: 05/25/2017] [Accepted: 05/27/2017] [Indexed: 11/21/2022] Open
Abstract
Place cells in the hippocampus and grid cells in the medial entorhinal cortex have different codes for space. However, how one code relates to the other is ill understood. Based on the anatomy of the entorhinal-hippocampal circuitry, we constructed a model of place and grid cells organized in a loop to investigate their mutual influence in the establishment of their codes for space. Using computer simulations, we first replicated experiments in rats that measured place and grid cell activity in different environments, and then assessed which features of the model account for different phenomena observed in neurophysiological data, such as pattern completion and pattern separation, global and rate remapping of place cells, and realignment of grid cells. We found that (1) the interaction between grid and place cells converges quickly; (2) the spatial code of place cells does not require, but is altered by, grid cell input; (3) plasticity in sensory inputs to place cells is key for pattern completion but not pattern separation; (4) grid realignment can be explained in terms of place cell remapping as opposed to the other way around; (5) the switch between global and rate remapping is self-organized; and (6) grid cell input to place cells helps stabilize their code under noisy and/or inconsistent sensory input. We conclude that the hippocampus-entorhinal circuit uses the mutual interaction of place and grid cells to encode the surrounding environment and propose a theory on how such interdependence underlies the formation and use of the cognitive map.SIGNIFICANCE STATEMENT The mammalian brain implements a positional system with two key pieces: place and grid cells. To gain insight into the dynamics of place and grid cell interaction, we built a computational model with the two cell types organized in a loop. The proposed model accounts for differences in how place and grid cells represent different environments and provides a new interpretation in which place and grid cells mutually interact to form a coupled code for space.
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27
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Rebola N, Carta M, Mulle C. Operation and plasticity of hippocampal CA3 circuits: implications for memory encoding. Nat Rev Neurosci 2017; 18:208-220. [DOI: 10.1038/nrn.2017.10] [Citation(s) in RCA: 96] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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28
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Zuckerman A, Ram O, Ifergane G, Matar MA, Sagi R, Ostfeld I, Hoffman JR, Kaplan Z, Sadot O, Cohen H. Controlled Low-Pressure Blast-Wave Exposure Causes Distinct Behavioral and Morphological Responses Modelling Mild Traumatic Brain Injury, Post-Traumatic Stress Disorder, and Comorbid Mild Traumatic Brain Injury–Post-Traumatic Stress Disorder. J Neurotrauma 2017; 34:145-164. [DOI: 10.1089/neu.2015.4310] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Affiliation(s)
- Amitai Zuckerman
- Faculty of Health Sciences, Ministry of Health, Anxiety and Stress Research Unit, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Omri Ram
- Department of Mechanical Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Gal Ifergane
- Headache Clinic, Department of Neurology, Soroka Medical Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Michael A. Matar
- Faculty of Health Sciences, Ministry of Health, Anxiety and Stress Research Unit, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Ram Sagi
- Israel Defense Forces, Medical Corps, Tel-Aviv, Israel
| | - Ishay Ostfeld
- Israel Defense Forces, Medical Corps, Tel-Aviv, Israel
| | - Jay R. Hoffman
- Institute of Exercise Physiology and Wellness, Sport and Exercise Science, University of Central Florida, Orlando, Florida
| | - Zeev Kaplan
- Faculty of Health Sciences, Ministry of Health, Anxiety and Stress Research Unit, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Oren Sadot
- Department of Mechanical Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Hagit Cohen
- Faculty of Health Sciences, Ministry of Health, Anxiety and Stress Research Unit, Ben-Gurion University of the Negev, Beer-Sheva, Israel
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29
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Hedrick KR, Zhang K. Megamap: flexible representation of a large space embedded with nonspatial information by a hippocampal attractor network. J Neurophysiol 2016; 116:868-91. [PMID: 27193320 DOI: 10.1152/jn.00856.2015] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Accepted: 05/09/2016] [Indexed: 11/22/2022] Open
Abstract
The problem of how the hippocampus encodes both spatial and nonspatial information at the cellular network level remains largely unresolved. Spatial memory is widely modeled through the theoretical framework of attractor networks, but standard computational models can only represent spaces that are much smaller than the natural habitat of an animal. We propose that hippocampal networks are built on a basic unit called a "megamap," or a cognitive attractor map in which place cells are flexibly recombined to represent a large space. Its inherent flexibility gives the megamap a huge representational capacity and enables the hippocampus to simultaneously represent multiple learned memories and naturally carry nonspatial information at no additional cost. On the other hand, the megamap is dynamically stable, because the underlying network of place cells robustly encodes any location in a large environment given a weak or incomplete input signal from the upstream entorhinal cortex. Our results suggest a general computational strategy by which a hippocampal network enjoys the stability of attractor dynamics without sacrificing the flexibility needed to represent a complex, changing world.
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Affiliation(s)
- Kathryn R Hedrick
- Biomedical Engineering; Johns Hopkins University; Baltimore, Maryland
| | - Kechen Zhang
- Biomedical Engineering; Johns Hopkins University; Baltimore, Maryland
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30
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Sosa M, Gillespie AK, Frank LM. Neural Activity Patterns Underlying Spatial Coding in the Hippocampus. Curr Top Behav Neurosci 2016; 37:43-100. [PMID: 27885550 DOI: 10.1007/7854_2016_462] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The hippocampus is well known as a central site for memory processing-critical for storing and later retrieving the experiences events of daily life so they can be used to shape future behavior. Much of what we know about the physiology underlying hippocampal function comes from spatial navigation studies in rodents, which have allowed great strides in understanding how the hippocampus represents experience at the cellular level. However, it remains a challenge to reconcile our knowledge of spatial encoding in the hippocampus with its demonstrated role in memory-dependent tasks in both humans and other animals. Moreover, our understanding of how networks of neurons coordinate their activity within and across hippocampal subregions to enable the encoding, consolidation, and retrieval of memories is incomplete. In this chapter, we explore how information may be represented at the cellular level and processed via coordinated patterns of activity throughout the subregions of the hippocampal network.
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Affiliation(s)
- Marielena Sosa
- Kavli Institute for Fundamental Neuroscience and Department of Physiology, University of California, San Francisco, USA
| | | | - Loren M Frank
- Kavli Institute for Fundamental Neuroscience and Department of Physiology, University of California, San Francisco, USA. .,Howard Hughes Medical Institute, Maryland, USA.
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31
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Sanders H, Rennó-Costa C, Idiart M, Lisman J. Grid Cells and Place Cells: An Integrated View of their Navigational and Memory Function. Trends Neurosci 2015; 38:763-775. [PMID: 26616686 DOI: 10.1016/j.tins.2015.10.004] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2015] [Revised: 09/25/2015] [Accepted: 10/18/2015] [Indexed: 12/16/2022]
Abstract
Much has been learned about the hippocampal/entorhinal system, but an overview of how its parts work in an integrated way is lacking. One question regards the function of entorhinal grid cells. We propose here that their fundamental function is to provide a coordinate system for producing mind-travel in the hippocampus, a process that accesses associations with upcoming positions. We further propose that mind-travel occurs during the second half of each theta cycle. By contrast, the first half of each theta cycle is devoted to computing current position using sensory information from the lateral entorhinal cortex (LEC) and path integration information from the medial entorhinal cortex (MEC). This model explains why MEC lesions can abolish hippocampal phase precession but not place fields.
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Affiliation(s)
- Honi Sanders
- Volen Center for Complex Systems, Brandeis University, Waltham, MA 02454, USA
| | - César Rennó-Costa
- Brain Institute, Federal University of Rio Grande do Norte, Natal, RN 59066, Brazil
| | - Marco Idiart
- Physics Institute, Federal University of Rio Grande do Sul, Avenida Bento Gonçalves 9500, Porto Alegre, RS, 91501-970, Brazil
| | - John Lisman
- Volen Center for Complex Systems, Brandeis University, Waltham, MA 02454, USA.
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32
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33
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Abstract
Starting with the work of Cajal more than 100 years ago, neuroscience has sought to understand how the cells of the brain give rise to cognitive functions. How far has neuroscience progressed in this endeavor? This Perspective assesses progress in elucidating five basic brain processes: visual recognition, long-term memory, short-term memory, action selection, and motor control. Each of these processes entails several levels of analysis: the behavioral properties, the underlying computational algorithm, and the cellular/network mechanisms that implement that algorithm. At this juncture, while many questions remain unanswered, achievements in several areas of research have made it possible to relate specific properties of brain networks to cognitive functions. What has been learned reveals, at least in rough outline, how cognitive processes can be an emergent property of neurons and their connections.
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Affiliation(s)
- John Lisman
- Biology Department and Volen Center for Complex Systems, Brandeis University, 415 South Street, Waltham, MA 02454-9110, USA.
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Trieu BH, Kramár EA, Cox CD, Jia Y, Wang W, Gall CM, Lynch G. Pronounced differences in signal processing and synaptic plasticity between piriform-hippocampal network stages: a prominent role for adenosine. J Physiol 2015; 593:2889-907. [PMID: 25902928 DOI: 10.1113/jp270398] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2015] [Accepted: 04/17/2015] [Indexed: 01/02/2023] Open
Abstract
KEY POINTS Extended trains of theta rhythm afferent activity lead to a biphasic response facilitation in field CA1 but not in the lateral perforant path input to the dentate gyrus. Processes that reverse long-term potentiation in field CA1 are not operative in the lateral perforant path: multiple lines of evidence indicate that this reflects differences in adenosine signalling. Adenosine A1 receptors modulate baseline synaptic transmission in the lateral olfactory tract but not the associational afferents of the piriform cortex. Levels of ecto-5'-nucleotidase (CD73), an enzyme that converts extracellular ATP into adenosine, are markedly different between regions and correlate with adenosine signalling and the efficacy of theta pulse stimulation in reversing long-term potentiation. Variations in transmitter mobilization, CD73 levels, and afferent divergence result in multivariate differences in signal processing through nodes in the cortico-hippocampal network. ABSTRACT The present study evaluated learning-related synaptic operations across the serial stages of the olfactory cortex-hippocampus network. Theta frequency stimulation produced very different time-varying responses in the Schaffer-commissural projections than in the lateral perforant path (LPP), an effect associated with distinctions in transmitter mobilization. Long-term potentiation (LTP) had a higher threshold in LPP field potential studies but not in voltage clamped neurons; coupled with input/output relationships, these results suggest that LTP threshold differences reflect the degree of input divergence. Theta pulse stimulation erased LTP in CA1 but not in the dentate gyrus (DG), although adenosine eliminated potentiation in both areas, suggesting that theta increases extracellular adenosine to a greater degree in CA1. Moreover, adenosine A1 receptor antagonism had larger effects on theta responses in CA1 than in the DG, and concentrations of ecto-5'-nucleotidase (CD73) were much higher in CA1. Input/output curves for two connections in the piriform cortex were similar to those for the LPP, whereas adenosine modulation again correlated with levels of CD73. In sum, multiple relays in a network extending from the piriform cortex through the hippocampus can be differentiated along three dimensions (input divergence, transmitter mobilization, adenosine modulation) that potently influence throughput and plasticity. A model that incorporates the regional differences, supplemented with data for three additional links, suggests that network output goes through three transitions during the processing of theta input. It is proposed that individuated relays allow the circuit to deal with different types of behavioural problems.
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Affiliation(s)
- Brian H Trieu
- Department of Anatomy and Neurobiology, University of California, Irvine, CA, USA
| | - Enikö A Kramár
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA
| | - Conor D Cox
- Department of Anatomy and Neurobiology, University of California, Irvine, CA, USA
| | - Yousheng Jia
- Department of Anatomy and Neurobiology, University of California, Irvine, CA, USA
| | - Weisheng Wang
- Department of Anatomy and Neurobiology, University of California, Irvine, CA, USA
| | - Christine M Gall
- Department of Anatomy and Neurobiology, University of California, Irvine, CA, USA.,Department of Neurobiology and Behavior, University of California, Irvine, CA, USA
| | - Gary Lynch
- Department of Anatomy and Neurobiology, University of California, Irvine, CA, USA.,Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
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Blanco W, Pereira CM, Cota VR, Souza AC, Rennó-Costa C, Santos S, Dias G, Guerreiro AMG, Tort ABL, Neto AD, Ribeiro S. Synaptic Homeostasis and Restructuring across the Sleep-Wake Cycle. PLoS Comput Biol 2015; 11:e1004241. [PMID: 26020963 PMCID: PMC4447375 DOI: 10.1371/journal.pcbi.1004241] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2014] [Accepted: 03/14/2015] [Indexed: 01/12/2023] Open
Abstract
Sleep is critical for hippocampus-dependent memory consolidation. However, the underlying mechanisms of synaptic plasticity are poorly understood. The central controversy is on whether long-term potentiation (LTP) takes a role during sleep and which would be its specific effect on memory. To address this question, we used immunohistochemistry to measure phosphorylation of Ca2+/calmodulin-dependent protein kinase II (pCaMKIIα) in the rat hippocampus immediately after specific sleep-wake states were interrupted. Control animals not exposed to novel objects during waking (WK) showed stable pCaMKIIα levels across the sleep-wake cycle, but animals exposed to novel objects showed a decrease during subsequent slow-wave sleep (SWS) followed by a rebound during rapid-eye-movement sleep (REM). The levels of pCaMKIIα during REM were proportional to cortical spindles near SWS/REM transitions. Based on these results, we modeled sleep-dependent LTP on a network of fully connected excitatory neurons fed with spikes recorded from the rat hippocampus across WK, SWS and REM. Sleep without LTP orderly rescaled synaptic weights to a narrow range of intermediate values. In contrast, LTP triggered near the SWS/REM transition led to marked swaps in synaptic weight ranking. To better understand the interaction between rescaling and restructuring during sleep, we implemented synaptic homeostasis and embossing in a detailed hippocampal-cortical model with both excitatory and inhibitory neurons. Synaptic homeostasis was implemented by weakening potentiation and strengthening depression, while synaptic embossing was simulated by evoking LTP on selected synapses. We observed that synaptic homeostasis facilitates controlled synaptic restructuring. The results imply a mechanism for a cognitive synergy between SWS and REM, and suggest that LTP at the SWS/REM transition critically influences the effect of sleep: Its lack determines synaptic homeostasis, its presence causes synaptic restructuring.
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Affiliation(s)
- Wilfredo Blanco
- Brain Institute, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
- Department of Computer and Automation, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
- Department of Computer Science, State University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Catia M. Pereira
- Edmond and Lily Safra International Institute of Neuroscience of Natal (ELS-IINN), Natal, Rio Grande do Norte, Brazil
| | - Vinicius R. Cota
- Brain Institute, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
- Laboratory of Neuroengineerging and Neuroscience, Federal University of São João Del-Rei, São João Del-Rei, Minas Gerais, Brazil
| | - Annie C. Souza
- Brain Institute, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - César Rennó-Costa
- Brain Institute, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Sharlene Santos
- Brain Institute, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Gabriella Dias
- Brain Institute, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Ana M. G. Guerreiro
- Department of Biomedical Engineering, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Adriano B. L. Tort
- Brain Institute, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Adrião D. Neto
- Department of Computer and Automation, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Sidarta Ribeiro
- Brain Institute, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
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Verschure PFMJ, Pennartz CMA, Pezzulo G. The why, what, where, when and how of goal-directed choice: neuronal and computational principles. Philos Trans R Soc Lond B Biol Sci 2014; 369:20130483. [PMID: 25267825 PMCID: PMC4186236 DOI: 10.1098/rstb.2013.0483] [Citation(s) in RCA: 90] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
The central problems that goal-directed animals must solve are: 'What do I need and Why, Where and When can this be obtained, and How do I get it?' or the H4W problem. Here, we elucidate the principles underlying the neuronal solutions to H4W using a combination of neurobiological and neurorobotic approaches. First, we analyse H4W from a system-level perspective by mapping its objectives onto the Distributed Adaptive Control embodied cognitive architecture which sees the generation of adaptive action in the real world as the primary task of the brain rather than optimally solving abstract problems. We next map this functional decomposition to the architecture of the rodent brain to test its consistency. Following this approach, we propose that the mammalian brain solves the H4W problem on the basis of multiple kinds of outcome predictions, integrating central representations of needs and drives (e.g. hypothalamus), valence (e.g. amygdala), world, self and task state spaces (e.g. neocortex, hippocampus and prefrontal cortex, respectively) combined with multi-modal selection (e.g. basal ganglia). In our analysis, goal-directed behaviour results from a well-structured architecture in which goals are bootstrapped on the basis of predefined needs, valence and multiple learning, memory and planning mechanisms rather than being generated by a singular computation.
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Affiliation(s)
- Paul F M J Verschure
- Laboratory of Synthetic, Perceptive, Emotive and Cognitive Systems (SPECS), Center of Autonomous Systems and Neurorobotics, Universitat Pompeu Fabra (UPF), Barcelona, Spain Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | | | - Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
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