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Andrade DS, Terrematte P, Rennó-Costa C, Zilberberg A, Efroni S. GENTLE: a novel bioinformatics tool for generating features and building classifiers from T cell repertoire cancer data. BMC Bioinformatics 2023; 24:32. [PMID: 36717789 PMCID: PMC9885559 DOI: 10.1186/s12859-023-05155-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 01/23/2023] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND In the global effort to discover biomarkers for cancer prognosis, prediction tools have become essential resources. TCR (T cell receptor) repertoires contain important features that differentiate healthy controls from cancer patients or differentiate outcomes for patients being treated with different drugs. Considering, tools that can easily and quickly generate and identify important features out of TCR repertoire data and build accurate classifiers to predict future outcomes are essential. RESULTS This paper introduces GENTLE (GENerator of T cell receptor repertoire features for machine LEarning): an open-source, user-friendly web-application tool that allows TCR repertoire researchers to discover important features; to create classifier models and evaluate them with metrics; and to quickly generate visualizations for data interpretations. We performed a case study with repertoires of TRegs (regulatory T cells) and TConvs (conventional T cells) from healthy controls versus patients with breast cancer. We showed that diversity features were able to distinguish between the groups. Moreover, the classifiers built with these features could correctly classify samples ('Healthy' or 'Breast Cancer')from the TRegs repertoire when trained with the TConvs repertoire, and from the TConvs repertoire when trained with the TRegs repertoire. CONCLUSION The paper walks through installing and using GENTLE and presents a case study and results to demonstrate the application's utility. GENTLE is geared towards any researcher working with TCR repertoire data and aims to discover predictive features from these data and build accurate classifiers. GENTLE is available on https://github.com/dhiego22/gentle and https://share.streamlit.io/dhiego22/gentle/main/gentle.py .
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Affiliation(s)
- Dhiego Souto Andrade
- grid.411233.60000 0000 9687 399XBioinformatics Multidisciplinary Environment (BioME), Metropole Digital Institute (IMD), Federal University of Rio Grande Do Norte (UFRN), Natal, 59078-970 Brazil
| | - Patrick Terrematte
- grid.411233.60000 0000 9687 399XBioinformatics Multidisciplinary Environment (BioME), Metropole Digital Institute (IMD), Federal University of Rio Grande Do Norte (UFRN), Natal, 59078-970 Brazil
| | - César Rennó-Costa
- grid.411233.60000 0000 9687 399XBioinformatics Multidisciplinary Environment (BioME), Metropole Digital Institute (IMD), Federal University of Rio Grande Do Norte (UFRN), Natal, 59078-970 Brazil
| | - Alona Zilberberg
- grid.22098.310000 0004 1937 0503The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel
| | - Sol Efroni
- grid.22098.310000 0004 1937 0503The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel
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2
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de Carvalho Souza AM, Barrocas R, Fischer MH, Arnaud E, Moeller K, Rennó-Costa C. Combining virtual reality and tactile stimulation to investigate embodied finger-based numerical representations. Front Psychol 2023; 14:1119561. [PMID: 37179854 PMCID: PMC10174462 DOI: 10.3389/fpsyg.2023.1119561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/31/2023] [Indexed: 05/15/2023] Open
Abstract
Finger-based representation of numbers is a high-level cognitive strategy to assist numerical and arithmetic processing in children and adults. It is unclear whether this paradigm builds on simple perceptual features or comprises several attributes through embodiment. Here we describe the development and initial testing of an experimental setup to study embodiment during a finger-based numerical task using Virtual Reality (VR) and a low-cost tactile stimulator that is easy to build. Using VR allows us to create new ways to study finger-based numerical representation using a virtual hand that can be manipulated in ways our hand cannot, such as decoupling tactile and visual stimuli. The goal is to present a new methodology that can allow researchers to study embodiment through this new approach, maybe shedding new light on the cognitive strategy behind the finger-based representation of numbers. In this case, a critical methodological requirement is delivering precisely targeted sensory stimuli to specific effectors while simultaneously recording their behavior and engaging the participant in a simulated experience. We tested the device's capability by stimulating users in different experimental configurations. Results indicate that our device delivers reliable tactile stimulation to all fingers of a participant's hand without losing motion tracking quality during an ongoing task. This is reflected by an accuracy of over 95% in participants detecting stimulation of a single finger or multiple fingers in sequential stimulation as indicated by experiments with sixteen participants. We discuss possible application scenarios, explain how to apply our methodology to study the embodiment of finger-based numerical representations and other high-level cognitive functions, and discuss potential further developments of the device based on the data obtained in our testing.
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Affiliation(s)
- Alyson Matheus de Carvalho Souza
- Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- Leibniz-Institut für Wissensmedien, Tübingen, Germany
| | | | - Martin H. Fischer
- Department of Psychology, University of Potsdam, Potsdam, Germany
- *Correspondence: Martin H. Fischer,
| | - Emanuel Arnaud
- Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Korbinian Moeller
- Leibniz-Institut für Wissensmedien, Tübingen, Germany
- Centre for Mathematical Cognition, School of Science, Loughborough University, Loughborough, United Kingdom
- LEAD Graduate School and Research Network, University of Tuebingen, Tübingen, Germany
| | - César Rennó-Costa
- Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
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3
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Ornelas IM, Cini FA, Wießner I, Marcos E, de Araújo D, Goto-Silva L, Nascimento J, Silva SRB, Costa MN, Falchi M, Olivieri R, Palhano-Fontes F, Sequerra E, Martins-de-Souza D, Feilding A, Rennó-Costa C, Tófoli LF, Rehen SK, Ribeiro S. Nootropic effects of LSD: Behavioral, molecular and computational evidence. Exp Neurol 2022; 356:114148. [PMID: 35732217 DOI: 10.1016/j.expneurol.2022.114148] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 06/03/2022] [Accepted: 06/14/2022] [Indexed: 11/26/2022]
Abstract
The therapeutic use of classical psychedelic substances such as d-lysergic acid diethylamide (LSD) surged in recent years. Studies in rodents suggest that these effects are produced by increased neural plasticity, including stimulation of the mTOR pathway, a key regulator of metabolism, plasticity, and aging. Could psychedelic-induced neural plasticity be harnessed to enhance cognition? Here we show that LSD treatment enhanced performance in a novel object recognition task in rats, and in a visuo-spatial memory task in humans. A proteomic analysis of human brain organoids showed that LSD affected metabolic pathways associated with neural plasticity, including mTOR. To gain insight into the relation of neural plasticity, aging and LSD-induced cognitive gains, we emulated the experiments in rats and humans with a neural network model of a cortico-hippocampal circuit. Using the baseline strength of plasticity as a proxy for age and assuming an increase in plasticity strength related to LSD dose, the simulations provided a good fit for the experimental data. Altogether, the results suggest that LSD has nootropic effects.
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Affiliation(s)
- Isis M Ornelas
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Felipe A Cini
- Brain Institute, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil
| | - Isabel Wießner
- Brain Institute, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil; Interdisciplinary Cooperation for Ayahuasca Research and Outreach (ICARO), School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
| | - Encarni Marcos
- Instituto de Neurociencias de Alicante, Consejo Superior de Investigaciones Científicas-Universidad Miguel Hernández de Elche, San Juan de Alicante, Spain
| | - Dráulio de Araújo
- Brain Institute, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil
| | - Livia Goto-Silva
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Juliana Nascimento
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil; Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
| | - Sergio R B Silva
- Brain Institute, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil
| | - Marcelo N Costa
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil; Institute of Biology, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Marcelo Falchi
- Interdisciplinary Cooperation for Ayahuasca Research and Outreach (ICARO), School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
| | - Rodolfo Olivieri
- Interdisciplinary Cooperation for Ayahuasca Research and Outreach (ICARO), School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
| | | | - Eduardo Sequerra
- Brain Institute, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil
| | - Daniel Martins-de-Souza
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil; Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil; Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBION), Conselho Nacional de Desenvolvimento Científico e Tecnológico, São Paulo, Brazil; Experimental Medicine Research Cluster (EMRC), University of Campinas, Campinas, Brazil
| | | | - César Rennó-Costa
- Digital Metropolis Institute, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil.
| | - Luis Fernando Tófoli
- Interdisciplinary Cooperation for Ayahuasca Research and Outreach (ICARO), School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil.
| | - Stevens K Rehen
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil; Institute of Biology, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil.
| | - Sidarta Ribeiro
- Brain Institute, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil.
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Lopes PH, Wellacott L, de Almeida L, Villavicencio LMM, Moreira ALDL, Andrade DS, Souza AMDC, de Sousa RKR, Silva PDS, Lima L, Lones M, do Nascimento JD, Vargas PA, Moioli RC, Blanco Figuerola W, Rennó-Costa C. Measuring the impact of nonpharmaceutical interventions on the SARS-CoV-2 pandemic at a city level: An agent-based computational modelling study of the City of Natal. PLOS Glob Public Health 2022; 2:e0000540. [PMID: 36962551 PMCID: PMC10021960 DOI: 10.1371/journal.pgph.0000540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 09/23/2022] [Indexed: 11/05/2022]
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic hit almost all cities in Brazil in early 2020 and lasted for several months. Despite the effort of local state and municipal governments, an inhomogeneous nationwide response resulted in a death toll amongst the highest recorded globally. To evaluate the impact of the nonpharmaceutical governmental interventions applied by different cities-such as the closure of schools and businesses in general-in the evolution and epidemic spread of SARS-CoV-2, we constructed a full-sized agent-based epidemiological model adjusted to the singularities of particular cities. The model incorporates detailed demographic information, mobility networks segregated by economic segments, and restricting bills enacted during the pandemic period. As a case study, we analyzed the early response of the City of Natal-a midsized state capital-to the pandemic. Although our results indicate that the government response could be improved, the restrictive mobility acts saved many lives. The simulations show that a detailed analysis of alternative scenarios can inform policymakers about the most relevant measures for similar pandemic surges and help develop future response protocols.
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Affiliation(s)
- Paulo Henrique Lopes
- Bioinformatics Multidisciplinary Environment of the Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Liam Wellacott
- Robotics Laboratory, Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, United Kingdom
| | - Leandro de Almeida
- Physics Department, Federal University of Rio Grande do Norte, Natal, Brazil
- Laboratório Nacional de Astrofísica, Itajubá, MG, Brazil
| | | | - André Luiz de Lucena Moreira
- Bioinformatics Multidisciplinary Environment of the Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Dhiego Souto Andrade
- Bioinformatics Multidisciplinary Environment of the Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Alyson Matheus de Carvalho Souza
- Bioinformatics Multidisciplinary Environment of the Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
| | | | | | - Luciana Lima
- Demography Graduate Program, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Michael Lones
- Robotics Laboratory, Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, United Kingdom
| | | | - Patricia A Vargas
- Robotics Laboratory, Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, United Kingdom
| | - Renan Cipriano Moioli
- Bioinformatics Multidisciplinary Environment of the Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- Robotics Laboratory, Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, United Kingdom
| | - Wilfredo Blanco Figuerola
- Bioinformatics Multidisciplinary Environment of the Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- Computer Science Department, State University of Rio Grande do Norte, Natal, Brazil
| | - César Rennó-Costa
- Bioinformatics Multidisciplinary Environment of the Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- Robotics Laboratory, Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, United Kingdom
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5
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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6
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Santos-Pata D, Amil AF, Raikov IG, Rennó-Costa C, Mura A, Soltesz I, Verschure PF. Entorhinal mismatch: A model of self-supervised learning in the hippocampus. iScience 2021; 24:102364. [PMID: 33997671 PMCID: PMC8091892 DOI: 10.1016/j.isci.2021.102364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 01/30/2021] [Accepted: 03/24/2021] [Indexed: 12/03/2022] Open
Abstract
The hippocampal formation displays a wide range of physiological responses to different spatial manipulations of the environment. However, very few attempts have been made to identify core computational principles underlying those hippocampal responses. Here, we capitalize on the observation that the entorhinal-hippocampal complex (EHC) forms a closed loop and projects inhibitory signals "countercurrent" to the trisynaptic pathway to build a self-supervised model that learns to reconstruct its own inputs by error backpropagation. The EHC is then abstracted as an autoencoder, with the hidden layers acting as an information bottleneck. With the inputs mimicking the firing activity of lateral and medial entorhinal cells, our model is shown to generate place cells and to respond to environmental manipulations as observed in rodent experiments. Altogether, we propose that the hippocampus builds conjunctive compressed representations of the environment by learning to reconstruct its own entorhinal inputs via gradient descent.
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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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7
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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9
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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10
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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: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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12
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Rennó-Costa C, Lisman JE, Verschure PFMJ. A signature of attractor dynamics in the CA3 region of the hippocampus. PLoS Comput Biol 2014; 10:e1003641. [PMID: 24854425 PMCID: PMC4031055 DOI: 10.1371/journal.pcbi.1003641] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2014] [Accepted: 04/09/2014] [Indexed: 12/02/2022] Open
Abstract
The notion of attractor networks is the leading hypothesis for how associative memories are stored and recalled. A defining anatomical feature of such networks is excitatory recurrent connections. These "attract" the firing pattern of the network to a stored pattern, even when the external input is incomplete (pattern completion). The CA3 region of the hippocampus has been postulated to be such an attractor network; however, the experimental evidence has been ambiguous, leading to the suggestion that CA3 is not an attractor network. In order to resolve this controversy and to better understand how CA3 functions, we simulated CA3 and its input structures. In our simulation, we could reproduce critical experimental results and establish the criteria for identifying attractor properties. Notably, under conditions in which there is continuous input, the output should be "attracted" to a stored pattern. However, contrary to previous expectations, as a pattern is gradually "morphed" from one stored pattern to another, a sharp transition between output patterns is not expected. The observed firing patterns of CA3 meet these criteria and can be quantitatively accounted for by our model. Notably, as morphing proceeds, the activity pattern in the dentate gyrus changes; in contrast, the activity pattern in the downstream CA3 network is attracted to a stored pattern and thus undergoes little change. We furthermore show that other aspects of the observed firing patterns can be explained by learning that occurs during behavioral testing. The CA3 thus displays both the learning and recall signatures of an attractor network. These observations, taken together with existing anatomical and behavioral evidence, make the strong case that CA3 constructs associative memories based on attractor dynamics.
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Affiliation(s)
- César Rennó-Costa
- Universitat Pompeu Fabra, Synthetic, Perceptive, Emotive and Cognitive Systems group (SPECS), Barcelona, Spain
- Federal University of Rio Grande do Norte (UFRN), Brain Institute (ICe), Natal, Rio Grande do Norte, Brazil
| | - John E. Lisman
- Brandeis University, Biology Department & Volen Center for Complex Systems, Waltham, Massachusetts, United States of America
| | - Paul F. M. J. Verschure
- Universitat Pompeu Fabra, Synthetic, Perceptive, Emotive and Cognitive Systems group (SPECS), Barcelona, Spain
- Catalan Institute of Advanced Research (ICREA), Passeig Lluís Companys 23, Barcelona, Spain
- Universitat Pompeu Fabra, Center of Autonomous Systems and Neurorobotics (NRAS), Barcelona, Spain
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Luvizotto A, Rennó-Costa C, Verschure PFMJ. A wavelet-based neural model to optimize and read out a temporal population code. Front Comput Neurosci 2012; 6:21. [PMID: 22563314 PMCID: PMC3342589 DOI: 10.3389/fncom.2012.00021] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2011] [Accepted: 03/20/2012] [Indexed: 12/22/2022] Open
Abstract
It has been proposed that the dense excitatory local connectivity of the neo-cortex plays a specific role in the transformation of spatial stimulus information into a temporal representation or a temporal population code (TPC). TPC provides for a rapid, robust, and high-capacity encoding of salient stimulus features with respect to position, rotation, and distortion. The TPC hypothesis gives a functional interpretation to a core feature of the cortical anatomy: its dense local and sparse long-range connectivity. Thus far, the question of how the TPC encoding can be decoded in downstream areas has not been addressed. Here, we present a neural circuit that decodes the spectral properties of the TPC using a biologically plausible implementation of a Haar transform. We perform a systematic investigation of our model in a recognition task using a standardized stimulus set. We consider alternative implementations using either regular spiking or bursting neurons and a range of spectral bands. Our results show that our wavelet readout circuit provides for the robust decoding of the TPC and further compresses the code without loosing speed or quality of decoding. We show that in the TPC signal the relevant stimulus information is present in the frequencies around 100 Hz. Our results show that the TPC is constructed around a small number of coding components that can be well decoded by wavelet coefficients in a neuronal implementation. The solution to the TPC decoding problem proposed here suggests that cortical processing streams might well consist of sequential operations where spatio-temporal transformations at lower levels forming a compact stimulus encoding using TPC that are subsequently decoded back to a spatial representation using wavelet transforms. In addition, the results presented here show that different properties of the stimulus might be transmitted to further processing stages using different frequency components that are captured by appropriately tuned wavelet-based decoders.
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Affiliation(s)
- Andre Luvizotto
- Synthetic Perceptive Emotive and Cognitive Systems (SPECS), Universitat Pompeu Fabra Barcelona, Spain
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Abstract
Rate remapping is a recently revealed neural code in which sensory information modulates the firing rate of hippocampal place cells. The mechanism underlying rate remapping is unknown. Its characteristic modulation, however, must arise from the interaction of the two major inputs to the hippocampus, the medial entorhinal cortex (MEC), in which grid cells represent the spatial position of the rat, and the lateral entorhinal cortex (LEC), in which cells represent the sensory properties of the environment. We have used computational methods to elucidate the mechanism by which this interaction produces rate remapping. We show that the convergence of LEC and MEC inputs, in conjunction with a competitive network process mediated by feedback inhibition, can account quantitatively for this phenomenon. The same principle accounts for why different place fields of the same cell vary independently as sensory information is altered. Our results show that rate remapping can be explained in terms of known mechanisms.
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Affiliation(s)
- César Rennó-Costa
- Laboratory of Synthetic Perceptive, Emotive and Cognitive Systems (SPECS), Technology Department, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain
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