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Yang S, Gao T, Wang J, Deng B, Azghadi MR, Lei T, Linares-Barranco B. Self-Adaptive Multicompartment: A Unified Self-Adaptive Multicompartmental Spiking Neuron Model for Learning With Working Memory. Front Neurosci 2022; 16:850945. [PMID: 35527819 PMCID: PMC9074872 DOI: 10.3389/fnins.2022.850945] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 03/15/2022] [Indexed: 11/13/2022] Open
Abstract
Working memory is a fundamental feature of biological brains for perception, cognition, and learning. In addition, learning with working memory, which has been show in conventional artificial intelligence systems through recurrent neural networks, is instrumental to advanced cognitive intelligence. However, it is hard to endow a simple neuron model with working memory, and to understand the biological mechanisms that have resulted in such a powerful ability at the neuronal level. This article presents a novel self-adaptive multicompartment spiking neuron model, referred to as SAM, for spike-based learning with working memory. SAM integrates four major biological principles including sparse coding, dendritic non-linearity, intrinsic self-adaptive dynamics, and spike-driven learning. We first describe SAM’s design and explore the impacts of critical parameters on its biological dynamics. We then use SAM to build spiking networks to accomplish several different tasks including supervised learning of the MNIST dataset using sequential spatiotemporal encoding, noisy spike pattern classification, sparse coding during pattern classification, spatiotemporal feature detection, meta-learning with working memory applied to a navigation task and the MNIST classification task, and working memory for spatiotemporal learning. Our experimental results highlight the energy efficiency and robustness of SAM in these wide range of challenging tasks. The effects of SAM model variations on its working memory are also explored, hoping to offer insight into the biological mechanisms underlying working memory in the brain. The SAM model is the first attempt to integrate the capabilities of spike-driven learning and working memory in a unified single neuron with multiple timescale dynamics. The competitive performance of SAM could potentially contribute to the development of efficient adaptive neuromorphic computing systems for various applications from robotics to edge computing.
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Affiliation(s)
- Shuangming Yang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
- *Correspondence: Shuangming Yang,
| | - Tian Gao
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Bin Deng
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | | | - Tao Lei
- School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an, China
- Tao Lei,
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Marvan T, Polák M, Bachmann T, Phillips WA. Apical amplification-a cellular mechanism of conscious perception? Neurosci Conscious 2021; 2021:niab036. [PMID: 34650815 PMCID: PMC8511476 DOI: 10.1093/nc/niab036] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 07/09/2021] [Accepted: 09/23/2021] [Indexed: 11/25/2022] Open
Abstract
We present a theoretical view of the cellular foundations for network-level processes involved in producing our conscious experience. Inputs to apical synapses in layer 1 of a large subset of neocortical cells are summed at an integration zone near the top of their apical trunk. These inputs come from diverse sources and provide a context within which the transmission of information abstracted from sensory input to their basal and perisomatic synapses can be amplified when relevant. We argue that apical amplification enables conscious perceptual experience and makes it more flexible, and thus more adaptive, by being sensitive to context. Apical amplification provides a possible mechanism for recurrent processing theory that avoids strong loops. It makes the broadcasting hypothesized by global neuronal workspace theories feasible while preserving the distinct contributions of the individual cells receiving the broadcast. It also provides mechanisms that contribute to the holistic aspects of integrated information theory. As apical amplification is highly dependent on cholinergic, aminergic, and other neuromodulators, it relates the specific contents of conscious experience to global mental states and to fluctuations in arousal when awake. We conclude that apical dendrites provide a cellular mechanism for the context-sensitive selective amplification that is a cardinal prerequisite of conscious perception.
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Affiliation(s)
- Tomáš Marvan
- Department of Analytic Philosophy, Institute of Philosophy, Czech Academy of Sciences, Jilská 1, Prague 110 00, Czech Republic
| | - Michal Polák
- Department of Philosophy, University of West Bohemia, Sedláčkova 19, Pilsen 306 14, Czech Republic
| | - Talis Bachmann
- School of Law and Cognitive Neuroscience Laboratory, University of Tartu (Tallinn branch), Kaarli pst 3, Tallinn 10119, Estonia
| | - William A Phillips
- Faculty of Natural Sciences, University of Stirling, Stirling FK9 4LA, UK
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Schubert F, Gros C. Nonlinear Dendritic Coincidence Detection for Supervised Learning. Front Comput Neurosci 2021; 15:718020. [PMID: 34421566 PMCID: PMC8372750 DOI: 10.3389/fncom.2021.718020] [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: 05/31/2021] [Accepted: 07/13/2021] [Indexed: 11/25/2022] Open
Abstract
Cortical pyramidal neurons have a complex dendritic anatomy, whose function is an active research field. In particular, the segregation between its soma and the apical dendritic tree is believed to play an active role in processing feed-forward sensory information and top-down or feedback signals. In this work, we use a simple two-compartment model accounting for the nonlinear interactions between basal and apical input streams and show that standard unsupervised Hebbian learning rules in the basal compartment allow the neuron to align the feed-forward basal input with the top-down target signal received by the apical compartment. We show that this learning process, termed coincidence detection, is robust against strong distractions in the basal input space and demonstrate its effectiveness in a linear classification task.
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Affiliation(s)
- Fabian Schubert
- Institute for Theoretical Physics, Goethe University Frankfurt am Main, Frankfurt am Main, Germany
| | - Claudius Gros
- Institute for Theoretical Physics, Goethe University Frankfurt am Main, Frankfurt am Main, Germany
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Lipshutz D, Bahroun Y, Golkar S, Sengupta AM, Chklovskii DB. A Biologically Plausible Neural Network for Multichannel Canonical Correlation Analysis. Neural Comput 2021; 33:2309-2352. [PMID: 34412114 DOI: 10.1162/neco_a_01414] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 03/23/2021] [Indexed: 11/04/2022]
Abstract
Cortical pyramidal neurons receive inputs from multiple distinct neural populations and integrate these inputs in separate dendritic compartments. We explore the possibility that cortical microcircuits implement canonical correlation analysis (CCA), an unsupervised learning method that projects the inputs onto a common subspace so as to maximize the correlations between the projections. To this end, we seek a multichannel CCA algorithm that can be implemented in a biologically plausible neural network. For biological plausibility, we require that the network operates in the online setting and its synaptic update rules are local. Starting from a novel CCA objective function, we derive an online optimization algorithm whose optimization steps can be implemented in a single-layer neural network with multicompartmental neurons and local non-Hebbian learning rules. We also derive an extension of our online CCA algorithm with adaptive output rank and output whitening. Interestingly, the extension maps onto a neural network whose neural architecture and synaptic updates resemble neural circuitry and non-Hebbian plasticity observed in the cortex.
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Affiliation(s)
- David Lipshutz
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, U.S.A.
| | - Yanis Bahroun
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, U.S.A.
| | - Siavash Golkar
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, U.S.A.
| | - Anirvan M Sengupta
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, U.S.A., and Department of Physics and Astronomy, Rutgers University, Piscataway, NJ 08854 U.S.A.
| | - Dmitri B Chklovskii
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, U.S.A., and Neuroscience Institute, NYU Medical Center, New York, NY 10016, U.S.A.
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Matheus Gauy M, Lengler J, Einarsson H, Meier F, Weissenberger F, Yanik MF, Steger A. A Hippocampal Model for Behavioral Time Acquisition and Fast Bidirectional Replay of Spatio-Temporal Memory Sequences. Front Neurosci 2018; 12:961. [PMID: 30618583 PMCID: PMC6306028 DOI: 10.3389/fnins.2018.00961] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 12/03/2018] [Indexed: 01/09/2023] Open
Abstract
The hippocampus is known to play a crucial role in the formation of long-term memory. For this, fast replays of previously experienced activities during sleep or after reward experiences are believed to be crucial. But how such replays are generated is still completely unclear. In this paper we propose a possible mechanism for this: we present a model that can store experienced trajectories on a behavioral timescale after a single run, and can subsequently bidirectionally replay such trajectories, thereby omitting any specifics of the previous behavior like speed, etc, but allowing repetitions of events, even with different subsequent events. Our solution builds on well-known concepts, one-shot learning and synfire chains, enhancing them by additional mechanisms using global inhibition and disinhibition. For replays our approach relies on dendritic spikes and cholinergic modulation, as supported by experimental data. We also hypothesize a functional role of disinhibition as a pacemaker during behavioral time.
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Affiliation(s)
- Marcelo Matheus Gauy
- Department of Computer Science, Institute of Theoretical Computer Science, ETH Zurich, Zurich, Switzerland
| | - Johannes Lengler
- Department of Computer Science, Institute of Theoretical Computer Science, ETH Zurich, Zurich, Switzerland
| | - Hafsteinn Einarsson
- Department of Computer Science, Institute of Theoretical Computer Science, ETH Zurich, Zurich, Switzerland
| | - Florian Meier
- Department of Computer Science, Institute of Theoretical Computer Science, ETH Zurich, Zurich, Switzerland
| | - Felix Weissenberger
- Department of Computer Science, Institute of Theoretical Computer Science, ETH Zurich, Zurich, Switzerland
| | - Mehmet Fatih Yanik
- Department of Information Technology and Electrical Engineering, Institute for Neuroinformatics, ETH Zurich, Zurich, Switzerland
| | - Angelika Steger
- Department of Computer Science, Institute of Theoretical Computer Science, ETH Zurich, Zurich, Switzerland
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