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Zhu Z, Qi Y, Lu W, Feng J. Learning to integrate parts for whole through correlated neural variability. PLoS Comput Biol 2024; 20:e1012401. [PMID: 39226329 PMCID: PMC11398653 DOI: 10.1371/journal.pcbi.1012401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 09/13/2024] [Accepted: 08/08/2024] [Indexed: 09/05/2024] Open
Abstract
Neural activity in the cortex exhibits a wide range of firing variability and rich correlation structures. Studies on neural coding indicate that correlated neural variability can influence the quality of neural codes, either beneficially or adversely. However, the mechanisms by which correlated neural variability is transformed and processed across neural populations to achieve meaningful computation remain largely unclear. Here we propose a theory of covariance computation with spiking neurons which offers a unifying perspective on neural representation and computation with correlated noise. We employ a recently proposed computational framework known as the moment neural network to resolve the nonlinear coupling of correlated neural variability with a task-driven approach to constructing neural network models for performing covariance-based perceptual tasks. In particular, we demonstrate how perceptual information initially encoded entirely within the covariance of upstream neurons' spiking activity can be passed, in a near-lossless manner, to the mean firing rate of downstream neurons, which in turn can be used to inform inference. The proposed theory of covariance computation addresses an important question of how the brain extracts perceptual information from noisy sensory stimuli to generate a stable perceptual whole and indicates a more direct role that correlated variability plays in cortical information processing.
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Affiliation(s)
- Zhichao Zhu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Yang Qi
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Wenlian Lu
- School of Mathematical Sciences, Fudan University, Shanghai, China
- Shanghai Center for Mathematical Sciences, Shanghai, China
- Shanghai Key Laboratory for Contemporary Applied Mathematics, Shanghai, China
- Key Laboratory of Mathematics for Nonlinear Science, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
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2
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Chen Y, Luan P, Liu J, Wei Y, Wang C, Wu R, Wu Z, Jing M. Spatiotemporally selective astrocytic ATP dynamics encode injury information sensed by microglia following brain injury in mice. Nat Neurosci 2024; 27:1522-1533. [PMID: 38862791 DOI: 10.1038/s41593-024-01680-w] [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: 05/10/2023] [Accepted: 05/13/2024] [Indexed: 06/13/2024]
Abstract
Injuries to the brain result in tunable cell responses paired with stimulus properties, suggesting the existence of intrinsic processes that encode and transmit injury information; however, the molecular mechanism of injury information encoding is unclear. Here, using ATP fluorescent indicators, we identify injury-evoked spatiotemporally selective ATP dynamics, Inflares, in adult mice of both sexes. Inflares are actively released from astrocytes and act as the internal representations of injury. Inflares encode injury intensity and position at their population level through frequency changes and are further decoded by microglia, driving changes in their activation state. Mismatches between Inflares and injury severity lead to microglia dysfunction and worsening of injury outcome. Blocking Inflares in ischemic stroke in mice reduces secondary damage and improves recovery of function. Our results suggest that astrocytic ATP dynamics encode injury information and are sensed by microglia.
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Affiliation(s)
- Yue Chen
- Chinese Institute for Brain Research, Beijing, China
| | - Pengwei Luan
- Chinese Institute for Brain Research, Beijing, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Juan Liu
- Chinese Institute for Brain Research, Beijing, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Yelan Wei
- Chinese Institute for Brain Research, Beijing, China
- Department of College of Physical Education and Sport, Beijing Normal University, Beijing, China
| | - Chenyu Wang
- Chinese Institute for Brain Research, Beijing, China
- Capital Medical University, Basic Medical Sciences, Beijing, China
| | - Rui Wu
- Chinese Institute for Brain Research, Beijing, China
- China Agricultural University, Beijing, China
| | - Zhaofa Wu
- State Key Laboratory of Membrane Biology, Peking University School of Life Sciences, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Miao Jing
- Chinese Institute for Brain Research, Beijing, China.
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3
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Greenwood PE, Ward LM. Attentional selection and communication through coherence: Scope and limitations. PLoS Comput Biol 2024; 20:e1011431. [PMID: 39102437 PMCID: PMC11326628 DOI: 10.1371/journal.pcbi.1011431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 08/15/2024] [Accepted: 07/22/2024] [Indexed: 08/07/2024] Open
Abstract
Synchronous neural oscillations are strongly associated with a variety of perceptual, cognitive, and behavioural processes. It has been proposed that the role of the synchronous oscillations in these processes is to facilitate information transmission between brain areas, the 'communication through coherence,' or CTC hypothesis. The details of how this mechanism would work, however, and its causal status, are still unclear. Here we investigate computationally a proposed mechanism for selective attention that directly implicates the CTC as causal. The mechanism involves alpha band (about 10 Hz) oscillations, originating in the pulvinar nucleus of the thalamus, being sent to communicating cortical areas, organizing gamma (about 40 Hz) oscillations there, and thus facilitating phase coherence and communication between them. This is proposed to happen contingent on control signals sent from higher-level cortical areas to the thalamic reticular nucleus, which controls the alpha oscillations sent to cortex by the pulvinar. We studied the scope of this mechanism in parameter space, and limitations implied by this scope, using a computational implementation of our conceptual model. Our results indicate that, although the CTC-based mechanism can account for some effects of top-down and bottom-up attentional selection, its limitations indicate that an alternative mechanism, in which oscillatory coherence is caused by communication between brain areas rather than being a causal factor for it, might operate in addition to, or even instead of, the CTC mechanism.
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Affiliation(s)
| | - Lawrence M Ward
- Department of Psychology and Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
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4
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Wang ZJ, Sun L, Heinbockel T. Firing Patterns of Mitral Cells and Their Transformation in the Main Olfactory Bulb. Brain Sci 2024; 14:678. [PMID: 39061419 PMCID: PMC11275187 DOI: 10.3390/brainsci14070678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 06/27/2024] [Accepted: 06/28/2024] [Indexed: 07/28/2024] Open
Abstract
Mitral cells (MCs) in the main olfactory bulb relay odor information to higher-order olfactory centers by encoding the information in the form of action potentials. The firing patterns of these cells are influenced by both their intrinsic properties and their synaptic connections within the neural network. However, reports on MC firing patterns have been inconsistent, and the mechanisms underlying these patterns remain unclear. Using whole-cell patch-clamp recordings in mouse brain slices, we discovered that MCs exhibit two types of integrative behavior: regular/rhythmic firing and bursts of action potentials. These firing patterns could be transformed both spontaneously and chemically. MCs with regular firing maintained their pattern even in the presence of blockers of fast synaptic transmission, indicating this was an intrinsic property. However, regular firing could be transformed into bursting by applying GABAA receptor antagonists to block inhibitory synaptic transmission. Burst firing could be reverted to regular firing by blocking ionotropic glutamate receptors, rather than applying a GABAA receptor agonist, indicating that ionotropic glutamatergic transmission mediated this transformation. Further experiments on long-lasting currents (LLCs), which generated burst firing, also supported this mechanism. In addition, cytoplasmic Ca2+ in MCs was involved in the transformation of firing patterns mediated by glutamatergic transmission. Metabotropic glutamate receptors also played a role in LLCs in MCs. These pieces of evidence indicate that odor information can be encoded on a mitral cell (MC) platform, where it can be relayed to higher-order olfactory centers through intrinsic and dendrodendritic mechanisms in MCs.
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Affiliation(s)
- Ze-Jun Wang
- Department of Anatomy, Howard University College of Medicine, Washington, DC 20059, USA
- Department of Pharmacology and Physiology, Georgetown University Medical Center, Washington, DC 20057, USA
| | - Liqin Sun
- Department of Anatomy, Howard University College of Medicine, Washington, DC 20059, USA
| | - Thomas Heinbockel
- Department of Anatomy, Howard University College of Medicine, Washington, DC 20059, USA
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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] [MESH Headings] [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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Jáidar O, Albarran E, Albarran EN, Wu YW, Ding JB. Refinement of efficient encodings of movement in the dorsolateral striatum throughout learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.06.596654. [PMID: 38895486 PMCID: PMC11185645 DOI: 10.1101/2024.06.06.596654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
The striatum is required for normal action selection, movement, and sensorimotor learning. Although action-specific striatal ensembles have been well documented, it is not well understood how these ensembles are formed and how their dynamics may evolve throughout motor learning. Here we used longitudinal 2-photon Ca2+ imaging of dorsal striatal neurons in head-fixed mice as they learned to self-generate locomotion. We observed a significant activation of both direct- and indirect-pathway spiny projection neurons (dSPNs and iSPNs, respectively) during early locomotion bouts and sessions that gradually decreased over time. For dSPNs, onset- and offset-ensembles were gradually refined from active motion-nonspecific cells. iSPN ensembles emerged from neurons initially active during opponent actions before becoming onset- or offset-specific. Our results show that as striatal ensembles are progressively refined, the number of active nonspecific striatal neurons decrease and the overall efficiency of the striatum information encoding for learned actions increases.
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Affiliation(s)
- Omar Jáidar
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
| | - Eddy Albarran
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Current address: Columbia University
| | | | - Yu-Wei Wu
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
- Current address: Institute of Molecular Biology, Academia Sinica
| | - Jun B. Ding
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
- The Phil & Penny Knight Initiative for Brain Resilience at the Wu Tsai Neurosciences Institute, Stanford University
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Rolls ET. Two what, two where, visual cortical streams in humans. Neurosci Biobehav Rev 2024; 160:105650. [PMID: 38574782 DOI: 10.1016/j.neubiorev.2024.105650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 03/25/2024] [Accepted: 03/31/2024] [Indexed: 04/06/2024]
Abstract
ROLLS, E. T. Two What, Two Where, Visual Cortical Streams in Humans. NEUROSCI BIOBEHAV REV 2024. Recent cortical connectivity investigations lead to new concepts about 'What' and 'Where' visual cortical streams in humans, and how they connect to other cortical systems. A ventrolateral 'What' visual stream leads to the inferior temporal visual cortex for object and face identity, and provides 'What' information to the hippocampal episodic memory system, the anterior temporal lobe semantic system, and the orbitofrontal cortex emotion system. A superior temporal sulcus (STS) 'What' visual stream utilising connectivity from the temporal and parietal visual cortex responds to moving objects and faces, and face expression, and connects to the orbitofrontal cortex for emotion and social behaviour. A ventromedial 'Where' visual stream builds feature combinations for scenes, and provides 'Where' inputs via the parahippocampal scene area to the hippocampal episodic memory system that are also useful for landmark-based navigation. The dorsal 'Where' visual pathway to the parietal cortex provides for actions in space, but also provides coordinate transforms to provide inputs to the parahippocampal scene area for self-motion update of locations in scenes in the dark or when the view is obscured.
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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; Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China.
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Ghazinouri B, Nejad MM, Cheng S. Navigation and the efficiency of spatial coding: insights from closed-loop simulations. Brain Struct Funct 2024; 229:577-592. [PMID: 37029811 PMCID: PMC10978723 DOI: 10.1007/s00429-023-02637-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 03/28/2023] [Indexed: 04/09/2023]
Abstract
Spatial learning is critical for survival and its underlying neuronal mechanisms have been studied extensively. These studies have revealed a wealth of information about the neural representations of space, such as place cells and boundary cells. While many studies have focused on how these representations emerge in the brain, their functional role in driving spatial learning and navigation has received much less attention. We extended an existing computational modeling tool-chain to study the functional role of spatial representations using closed-loop simulations of spatial learning. At the heart of the model agent was a spiking neural network that formed a ring attractor. This network received inputs from place and boundary cells and the location of the activity bump in this network was the output. This output determined the movement directions of the agent. We found that the navigation performance depended on the parameters of the place cell input, such as their number, the place field sizes, and peak firing rate, as well as, unsurprisingly, the size of the goal zone. The dependence on the place cell parameters could be accounted for by just a single variable, the overlap index, but this dependence was nonmonotonic. By contrast, performance scaled monotonically with the Fisher information of the place cell population. Our results therefore demonstrate that efficiently encoding spatial information is critical for navigation performance.
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Affiliation(s)
- Behnam Ghazinouri
- Faculty of Computer Science, Institute for Neural Computation, Ruhr University Bochum, Universitätsstrasse 150, 44801, Bochum, Germany
| | - Mohammadreza Mohagheghi Nejad
- Faculty of Computer Science, Institute for Neural Computation, Ruhr University Bochum, Universitätsstrasse 150, 44801, Bochum, Germany
| | - Sen Cheng
- Faculty of Computer Science, Institute for Neural Computation, Ruhr University Bochum, Universitätsstrasse 150, 44801, Bochum, Germany.
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Cotteret M, Greatorex H, Ziegler M, Chicca E. Vector Symbolic Finite State Machines in Attractor Neural Networks. Neural Comput 2024; 36:549-595. [PMID: 38457766 DOI: 10.1162/neco_a_01638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 10/19/2023] [Indexed: 03/10/2024]
Abstract
Hopfield attractor networks are robust distributed models of human memory, but they lack a general mechanism for effecting state-dependent attractor transitions in response to input. We propose construction rules such that an attractor network may implement an arbitrary finite state machine (FSM), where states and stimuli are represented by high-dimensional random vectors and all state transitions are enacted by the attractor network's dynamics. Numerical simulations show the capacity of the model, in terms of the maximum size of implementable FSM, to be linear in the size of the attractor network for dense bipolar state vectors and approximately quadratic for sparse binary state vectors. We show that the model is robust to imprecise and noisy weights, and so a prime candidate for implementation with high-density but unreliable devices. By endowing attractor networks with the ability to emulate arbitrary FSMs, we propose a plausible path by which FSMs could exist as a distributed computational primitive in biological neural networks.
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Affiliation(s)
- Madison Cotteret
- Micro- and Nanoelectronic Systems, Institute of Micro- and Nanotechnologies (IMN) MacroNano, Technische Universität Ilmenau, 98693 Ilmenau, Germany
- Bio-Inspired Circuits and Systems Lab, Zernike Institute for Advanced Materials, and Groningen Cognitive Systems and Materials Center, University of Groningen, 9747 AG Groningen, Netherlands
| | - Hugh Greatorex
- Bio-Inspired Circuits and Systems Lab, Zernike Institute for Advanced Materials, and Groningen Cognitive Systems and Materials Center, University of Groningen, 9747 AG Groningen, Netherlands
| | - Martin Ziegler
- Micro- and Nanoelectronic Systems, Institute of Micro- and Nanotechnologies (IMN) MacroNano, Technische Universität Ilmenau, 98693 Ilmenau, Germany
| | - Elisabetta Chicca
- Bio-Inspired Circuits and Systems Lab, Zernike Institute for Advanced Materials, and Groningen Cognitive Systems and Materials Center, University of Groningen, 9747 AG Groningen, Netherlands
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10
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Ouelhazi A, Bharmauria V, Molotchnikoff S. Adaptation-induced sharpening of orientation tuning curves in the mouse visual cortex. Neuroreport 2024; 35:291-298. [PMID: 38407865 DOI: 10.1097/wnr.0000000000002012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
OBJECTIVE Orientation selectivity is an emergent property of visual neurons across species with columnar and noncolumnar organization of the visual cortex. The emergence of orientation selectivity is more established in columnar cortical areas than in noncolumnar ones. Thus, how does orientation selectivity emerge in noncolumnar cortical areas after an adaptation protocol? Adaptation refers to the constant presentation of a nonoptimal stimulus (adapter) to a neuron under observation for a specific time. Previously, it had been shown that adaptation has varying effects on the tuning properties of neurons, such as orientation, spatial frequency, motion and so on. BASIC METHODS We recorded the mouse primary visual neurons (V1) at different orientations in the control (preadaptation) condition. This was followed by adapting neurons uninterruptedly for 12 min and then recording the same neurons postadaptation. An orientation selectivity index (OSI) for neurons was computed to compare them pre- and post-adaptation. MAIN RESULTS We show that 12-min adaptation increases the OSI of visual neurons ( n = 113), that is, sharpens their tuning. Moreover, the OSI postadaptation increases linearly as a function of the OSI preadaptation. CONCLUSION The increased OSI postadaptation may result from a specific dendritic neural mechanism, potentially facilitating the rapid learning of novel features.
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Affiliation(s)
- Afef Ouelhazi
- Département de Sciences Biologiques, Neurophysiology of the Visual system, Université de Montréal, Montréal, Québec
| | - Vishal Bharmauria
- Department of Psychology, Centre for Vision Research and Vision: Science to Applications (VISTA) Program, York University, Toronto, Ontario, Canada
| | - Stéphane Molotchnikoff
- Département de Sciences Biologiques, Neurophysiology of the Visual system, Université de Montréal, Montréal, Québec
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Dong X, Gui X, Klich S, Zhu L, Chen D, Sun Z, Shi Y, Chen A. The effects of football juggling learning on executive function and brain functional connectivity. Front Hum Neurosci 2024; 18:1362418. [PMID: 38516307 PMCID: PMC10954781 DOI: 10.3389/fnhum.2024.1362418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 02/20/2024] [Indexed: 03/23/2024] Open
Abstract
This study aimed to explore the relationship between motor skill learning and executive function (EF), with an emphasis on the potential effects of football juggling learning. A randomized controlled trial involving 111 participants aged 17-19 years was conducted. Participants were randomly assigned to either the football juggling learning (FJL) group or a control group. The FJL group underwent 70 sessions of football juggling learning, while the control group engaged in their normal daily activities without any exercise intervention during the same time frame. Both groups were assessed for EF performance and underwent functional magnetic resonance imaging (fMRI) scans before and after the experiment. The executive function test included three tasks, namely, inhibition, working memory, and shifting. The results showed significant improvement in inhibition and shifting in both groups, and the FJL group showed greater improvement in these aspects of EF compared to the control group. Additionally, in comparison to the control group, the FJL group exhibited increased functional connectivity within the frontal, temporal, and cerebellar regions from the pre-test to the post-test. Notably, enhanced functional connectivity between the right superior temporal gyrus (posterior division) and left cerebellum 6 was identified in the FJL group and was associated with improved EF performance induced by football juggling learning. These findings shed light on the potential causal relationship between motor skill learning, EF, and brain plasticity. Importantly, our study provides preliminary evidence supporting the use of motor skill learning, such as football juggling, as a potential avenue for cognitive enhancement.
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Affiliation(s)
| | - Xiang Gui
- Yangzhou University, Yangzhou, China
| | - Sebastian Klich
- Department of Paralympic Sport, Wroclaw University of Health and Sport Sciences, Wroclaw, Poland
| | - Lina Zhu
- Yangzhou University, Yangzhou, China
| | | | | | - Yifan Shi
- Yangzhou University, Yangzhou, China
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12
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Niu X, Peng Y, Jiang Z, Huang S, Liu R, Zhu M, Shi L. Gamma-band-based dynamic functional connectivity in pigeon entopallium during sample presentation in a delayed color matching task. Cogn Neurodyn 2024; 18:37-47. [PMID: 38406198 PMCID: PMC10881935 DOI: 10.1007/s11571-022-09916-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 10/12/2022] [Accepted: 11/17/2022] [Indexed: 01/09/2023] Open
Abstract
Birds have developed visual cognitions, especially in discriminating colors due to their four types of cones in the retina. The entopallium of birds is thought to be involved in the processing of color information during visual cognition. However, there is a lack of understanding about how functional connectivity in the entopallium region of birds changes during color cognition, which is related to various input colors. We therefore trained pigeons to perform a delayed color matching task, in which two colors were randomly presented in sample stimuli phrases, and the neural activity at individual recording site and the gamma band functional connectivity among local population in entopallium during sample presentation were analyzed. Both gamma band energy and gamma band functional connectivity presented dynamics as the stimulus was presented and persisted. The response features in the early-stimulus phase were significantly different from those of baseline and the late-stimulus phase. Furthermore, gamma band energy showed significant differences between different colors during the early-stimulus phase, but the global feature of the gamma band functional network did not. Further decoding results showed that decoding accuracy was significantly enhanced by adding functional connectivity features, suggesting the global feature of the gamma band functional network did not directly contain color information, but was related to it. These results provided insight into information processing rules among local neuronal populations in the entopallium of birds during color cognition, which is important for their daily life.
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Affiliation(s)
- Xiaoke Niu
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical and Information Engineering, ZhengZhou University, Zhengzhou, 450001 China
| | - Yanyan Peng
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical and Information Engineering, ZhengZhou University, Zhengzhou, 450001 China
| | - Zhenyang Jiang
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical and Information Engineering, ZhengZhou University, Zhengzhou, 450001 China
| | - Shuman Huang
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical and Information Engineering, ZhengZhou University, Zhengzhou, 450001 China
| | - Ruibin Liu
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical and Information Engineering, ZhengZhou University, Zhengzhou, 450001 China
| | - Minjie Zhu
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical and Information Engineering, ZhengZhou University, Zhengzhou, 450001 China
| | - Li Shi
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical and Information Engineering, ZhengZhou University, Zhengzhou, 450001 China
- Department of Automation, Tsinghua University, Beijing, 100000 China
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13
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Dibot NM, Tieo S, Mendelson TC, Puech W, Renoult JP. Sparsity in an artificial neural network predicts beauty: Towards a model of processing-based aesthetics. PLoS Comput Biol 2023; 19:e1011703. [PMID: 38048323 PMCID: PMC10721202 DOI: 10.1371/journal.pcbi.1011703] [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: 05/23/2023] [Revised: 12/14/2023] [Accepted: 11/20/2023] [Indexed: 12/06/2023] Open
Abstract
Generations of scientists have pursued the goal of defining beauty. While early scientists initially focused on objective criteria of beauty ('feature-based aesthetics'), philosophers and artists alike have since proposed that beauty arises from the interaction between the object and the individual who perceives it. The aesthetic theory of fluency formalizes this idea of interaction by proposing that beauty is determined by the efficiency of information processing in the perceiver's brain ('processing-based aesthetics'), and that efficient processing induces a positive aesthetic experience. The theory is supported by numerous psychological results, however, to date there is no quantitative predictive model to test it on a large scale. In this work, we propose to leverage the capacity of deep convolutional neural networks (DCNN) to model the processing of information in the brain by studying the link between beauty and neuronal sparsity, a measure of information processing efficiency. Whether analyzing pictures of faces, figurative or abstract art paintings, neuronal sparsity explains up to 28% of variance in beauty scores, and up to 47% when combined with a feature-based metric. However, we also found that sparsity is either positively or negatively correlated with beauty across the multiple layers of the DCNN. Our quantitative model stresses the importance of considering how information is processed, in addition to the content of that information, when predicting beauty, but also suggests an unexpectedly complex relationship between fluency and beauty.
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Affiliation(s)
- Nicolas M. Dibot
- CEFE, Univ. Montpellier, CNRS, EPHE, IRD, Montpellier, France
- LIRMM, Univ. Montpellier, CNRS, Montpellier, France
| | - Sonia Tieo
- CEFE, Univ. Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Tamra C. Mendelson
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland, United States of America
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Li Y, Zhang B, Liu Z, Wang R. Neural energy computations based on Hodgkin-Huxley models bridge abnormal neuronal activities and energy consumption patterns of major depressive disorder. Comput Biol Med 2023; 166:107500. [PMID: 37797488 DOI: 10.1016/j.compbiomed.2023.107500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/07/2023] [Accepted: 09/15/2023] [Indexed: 10/07/2023]
Abstract
Limited by the current experimental techniques and neurodynamical models, the dysregulation mechanisms of decision-making related neural circuits in major depressive disorder (MDD) are still not clear. In this paper, we proposed a neural coding methodology using energy to further investigate it, which has been proven to strongly complement the neurodynamical methodology. We augmented the previous neural energy calculation method, and applied it to our VTA-NAc-mPFC neurodynamical H-H models. We particularly focused on the peak power and energy consumption of abnormal ion channel (ionic) currents under different concentrations of dopamine input, and investigated the abnormal energy consumption patterns for the MDD group. The results revealed that the energy consumption of medium spiny neurons (MSNs) in the NAc region were lower in the MDD group than that of the normal control group despite having the same firing frequencies, peak action potentials, and average membrane potentials in both groups. Dopamine concentration was also positively correlated with the energy consumption of the pyramidal neurons, but the patterns of different interneuron types were distinct. Additionally, the ratio of mPFC's energy consumption to total energy consumption of the whole network in MDD group was lower than that in normal control group, revealing that the mPFC region in MDD group encoded less neural information, which matched the energy consumption patterns of BOLD-fMRI results. It was also in line with the behavioral characteristics that MDD patients demonstrated in the form of reward insensitivity during decision-making tasks. In conclusion, the model in this paper was the first neural network energy computational model for MDD, which showed success in explaining its dynamical mechanisms with an energy consumption perspective. To build on this, we demonstrated that energy consumption levels can be used as a potential indicator for MDD, which also showed a promising pipeline to use an energy methodology for studying other neuropsychiatric disorders.
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Affiliation(s)
- Yuanxi Li
- Institute for Cognitive Neurodynamics, School of Mathematics, East China University of Science and Technology, Shanghai, China; Department of Neurology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA.
| | - Bing Zhang
- Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China.
| | - Zhiqiang Liu
- Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China; Anesthesia and Brain Function Research Institute, Tongji University School of Medicine, Shanghai, China.
| | - Rubin Wang
- Institute for Cognitive Neurodynamics, School of Mathematics, East China University of Science and Technology, Shanghai, China.
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15
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Craft MF, Barreiro AK, Gautam SH, Shew WL, Ly C. Odor modality is transmitted to cortical brain regions from the olfactory bulb. J Neurophysiol 2023; 130:1226-1242. [PMID: 37791383 PMCID: PMC10994644 DOI: 10.1152/jn.00101.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 10/02/2023] [Accepted: 10/02/2023] [Indexed: 10/05/2023] Open
Abstract
Odor perception is the impetus for important animal behaviors with two predominate modes of processing: odors pass through the front of the nose (orthonasal) while inhaling and sniffing, or through the rear (retronasal) during exhalation and while eating. Despite the importance of olfaction for an animal's well-being and that ortho and retro naturally occur, it is unknown how the modality (ortho vs. retro) is even transmitted to cortical brain regions, which could significantly affect how odors are processed and perceived. Using multielectrode array recordings in tracheotomized anesthetized rats, which decouples ortho-retro modality from breathing, we show that mitral cells in rat olfactory bulb can reliably and directly transmit orthonasal versus retronasal modality with ethyl butyrate, a common food odor. Drug manipulations affecting synaptic inhibition via GABAA lead to worse decoding of ortho versus retro, independent of whether overall inhibition increases or decreases, suggesting that the olfactory bulb circuit may naturally favor encoding this important aspect of odors. Detailed data analysis paired with a firing rate model that captures population trends in spiking statistics shows how this circuit can encode odor modality. We have not only demonstrated that ortho/retro information is encoded to downstream brain regions but also used modeling to demonstrate a plausible mechanism for this encoding; due to synaptic adaptation, it is the slower time course of the retronasal stimulation that causes retronasal responses to be stronger and less sensitive to inhibitory drug manipulations than orthonasal responses.NEW & NOTEWORTHY Whether ortho (sniffing odors) versus retro (exhalation and eating) is encoded from the olfactory bulb to other brain areas is not completely known. Using multielectrode array recordings in anesthetized rats, we show that the olfactory bulb transmits this information downstream via spikes. Altering inhibition degrades ortho/retro information on average. We use theory and computation to explain our results, which should have implications on cortical processing considering that only food odors occur retronasally.
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Affiliation(s)
- Michelle F Craft
- Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, Virginia, United States
| | - Andrea K Barreiro
- Department of Mathematics, Southern Methodist University, Dallas, Texas, United States
| | - Shree Hari Gautam
- Department of Physics, University of Arkansas, Fayetteville, Arkansas, United States
| | - Woodrow L Shew
- Department of Physics, University of Arkansas, Fayetteville, Arkansas, United States
| | - Cheng Ly
- Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, Virginia, United States
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16
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Gentile F. The effective enhancement of information in 3D small-world networks of biological neuronal cells. Biomed Phys Eng Express 2023; 9:065019. [PMID: 37802049 DOI: 10.1088/2057-1976/ad00c0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 10/06/2023] [Indexed: 10/08/2023]
Abstract
The cardiovascular system, the kidney, or the brain, are examples of complex systems - where the properties of the systems arise because of the layout of cells in those systems. One way to characterize systems is using networks, where elements and interactions of the systems are represented as nodes and links of a graph. Network's topology can be, in turn, measured by the small-world coefficient. Small world networks feature increased clustering and shorter paths compared to random or periodic networks of the same size. This suggests that systems with small world attributes can also efficiently transport signals, nutrients, or information within their body. While several reports in literature have illustrated that real biological systems are small-world, yet little is known about how information varies as a function of the small-world-ness (sw) of three dimensional graphs. Here, we used a model of the brain to estimate quantitatively the information processed in 3D networks. In the model, nodes of the graph are neuronal units capable to receive, integrate and transmit signals to other neurons of the system in parallel. The information encoded in the signals was then extracted using the techniques of information theory. In simulations where the topology of networks of400nodes was varied over large intervals, we found that in the0-9swrange information scales linearly with the small world coefficient, with a five-fold increase. Results of the paper and review of the existing literature on model organisms suggest that a small-world architecture may offer an evolutionary advantage to biological systems.
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Affiliation(s)
- F Gentile
- Nanotechnology Research Center, Department of Experimental and Clinical Medicine, University of Magna Graecia, 88100 Catanzaro, Italy
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17
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Mitskopoulos L, Onken A. Discovering Low-Dimensional Descriptions of Multineuronal Dependencies. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1026. [PMID: 37509973 PMCID: PMC10378554 DOI: 10.3390/e25071026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/12/2023] [Accepted: 07/04/2023] [Indexed: 07/30/2023]
Abstract
Coordinated activity in neural populations is crucial for information processing. Shedding light on the multivariate dependencies that shape multineuronal responses is important to understand neural codes. However, existing approaches based on pairwise linear correlations are inadequate at capturing complicated interaction patterns and miss features that shape aspects of the population function. Copula-based approaches address these shortcomings by extracting the dependence structures in the joint probability distribution of population responses. In this study, we aimed to dissect neural dependencies with a C-Vine copula approach coupled with normalizing flows for estimating copula densities. While this approach allows for more flexibility compared to fitting parametric copulas, drawing insights on the significance of these dependencies from large sets of copula densities is challenging. To alleviate this challenge, we used a weighted non-negative matrix factorization procedure to leverage shared latent features in neural population dependencies. We validated the method on simulated data and applied it on copulas we extracted from recordings of neurons in the mouse visual cortex as well as in the macaque motor cortex. Our findings reveal that neural dependencies occupy low-dimensional subspaces, but distinct modules are synergistically combined to give rise to diverse interaction patterns that may serve the population function.
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Affiliation(s)
| | - Arno Onken
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK
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18
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Rolls ET, Rauschecker JP, Deco G, Huang CC, Feng J. Auditory cortical connectivity in humans. Cereb Cortex 2023; 33:6207-6227. [PMID: 36573464 PMCID: PMC10422925 DOI: 10.1093/cercor/bhac496] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 11/27/2022] [Accepted: 11/29/2022] [Indexed: 12/28/2022] Open
Abstract
To understand auditory cortical processing, the effective connectivity between 15 auditory cortical regions and 360 cortical regions was measured in 171 Human Connectome Project participants, and complemented with functional connectivity and diffusion tractography. 1. A hierarchy of auditory cortical processing was identified from Core regions (including A1) to Belt regions LBelt, MBelt, and 52; then to PBelt; and then to HCP A4. 2. A4 has connectivity to anterior temporal lobe TA2, and to HCP A5, which connects to dorsal-bank superior temporal sulcus (STS) regions STGa, STSda, and STSdp. These STS regions also receive visual inputs about moving faces and objects, which are combined with auditory information to help implement multimodal object identification, such as who is speaking, and what is being said. Consistent with this being a "what" ventral auditory stream, these STS regions then have effective connectivity to TPOJ1, STV, PSL, TGv, TGd, and PGi, which are language-related semantic regions connecting to Broca's area, especially BA45. 3. A4 and A5 also have effective connectivity to MT and MST, which connect to superior parietal regions forming a dorsal auditory "where" stream involved in actions in space. Connections of PBelt, A4, and A5 with BA44 may form a language-related dorsal stream.
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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
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200602, China
| | - Josef P Rauschecker
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC 20057, USA
- Institute for Advanced Study, Technical University, Munich, Germany
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Brain and Cognition, Pompeu Fabra University, Barcelona 08018, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200602, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
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19
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Rolls ET. Hippocampal spatial view cells for memory and navigation, and their underlying connectivity in humans. Hippocampus 2023; 33:533-572. [PMID: 36070199 PMCID: PMC10946493 DOI: 10.1002/hipo.23467] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 08/16/2022] [Accepted: 08/16/2022] [Indexed: 01/08/2023]
Abstract
Hippocampal and parahippocampal gyrus spatial view neurons in primates respond to the spatial location being looked at. The representation is allocentric, in that the responses are to locations "out there" in the world, and are relatively invariant with respect to retinal position, eye position, head direction, and the place where the individual is located. The underlying connectivity in humans is from ventromedial visual cortical regions to the parahippocampal scene area, leading to the theory that spatial view cells are formed by combinations of overlapping feature inputs self-organized based on their closeness in space. Thus, although spatial view cells represent "where" for episodic memory and navigation, they are formed by ventral visual stream feature inputs in the parahippocampal gyrus in what is the parahippocampal scene area. A second "where" driver of spatial view cells are parietal inputs, which it is proposed provide the idiothetic update for spatial view cells, used for memory recall and navigation when the spatial view details are obscured. Inferior temporal object "what" inputs and orbitofrontal cortex reward inputs connect to the human hippocampal system, and in macaques can be associated in the hippocampus with spatial view cell "where" representations to implement episodic memory. Hippocampal spatial view cells also provide a basis for navigation to a series of viewed landmarks, with the orbitofrontal cortex reward inputs to the hippocampus providing the goals for navigation, which can then be implemented by hippocampal connectivity in humans to parietal cortex regions involved in visuomotor actions in space. The presence of foveate vision and the highly developed temporal lobe for object and scene processing in primates including humans provide a basis for hippocampal spatial view cells to be key to understanding episodic memory in the primate and human hippocampus, and the roles of this system in primate including human navigation.
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Affiliation(s)
- Edmund T. Rolls
- Oxford Centre for Computational NeuroscienceOxfordUK
- Department of Computer ScienceUniversity of WarwickCoventryUK
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20
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Rolls ET, Deco G, Huang CC, Feng J. The human posterior parietal cortex: effective connectome, and its relation to function. Cereb Cortex 2023; 33:3142-3170. [PMID: 35834902 PMCID: PMC10401905 DOI: 10.1093/cercor/bhac266] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/10/2022] [Accepted: 06/11/2022] [Indexed: 01/04/2023] Open
Abstract
The effective connectivity between 21 regions in the human posterior parietal cortex, and 360 cortical regions was measured in 171 Human Connectome Project (HCP) participants using the HCP atlas, and complemented with functional connectivity and diffusion tractography. Intraparietal areas LIP, VIP, MIP, and AIP have connectivity from early cortical visual regions, and to visuomotor regions such as the frontal eye fields, consistent with functions in eye saccades and tracking. Five superior parietal area 7 regions receive from similar areas and from the intraparietal areas, but also receive somatosensory inputs and connect with premotor areas including area 6, consistent with functions in performing actions to reach for, grasp, and manipulate objects. In the anterior inferior parietal cortex, PFop, PFt, and PFcm are mainly somatosensory, and PF in addition receives visuo-motor and visual object information, and is implicated in multimodal shape and body image representations. In the posterior inferior parietal cortex, PFm and PGs combine visuo-motor, visual object, and reward input and connect with the hippocampal system. PGi in addition provides a route to motion-related superior temporal sulcus regions involved in social interactions. PGp has connectivity with intraparietal regions involved in coordinate transforms and may be involved in idiothetic update of hippocampal visual scene representations.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, United Kingdom
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
| | - Gustavo Deco
- Computational Neuroscience Group, Department of Information and Communication Technologies, Center for Brain and Cognition, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain
- Brain and Cognition, Pompeu Fabra University, Barcelona 08018, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, Institute of Brain and Education Innovation, East China Normal University, Shanghai 200602, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 200602, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
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21
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Rolls ET. The orbitofrontal cortex, food reward, body weight and obesity. Soc Cogn Affect Neurosci 2023; 18:nsab044. [PMID: 33830272 PMCID: PMC9997078 DOI: 10.1093/scan/nsab044] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 03/17/2021] [Accepted: 04/08/2021] [Indexed: 12/23/2022] Open
Abstract
In primates including humans, the orbitofrontal cortex is the key brain region representing the reward value and subjective pleasantness of the sight, smell, taste and texture of food. At stages of processing before this, in the insular taste cortex and inferior temporal visual cortex, the identity of the food is represented, but not its affective value. In rodents, the whole organisation of reward systems appears to be different, with reward value reflected earlier in processing systems. In primates and humans, the amygdala is overshadowed by the great development of the orbitofrontal cortex. Social and cognitive factors exert a top-down influence on the orbitofrontal cortex, to modulate the reward value of food that is represented in the orbitofrontal cortex. Recent evidence shows that even in the resting state, with no food present as a stimulus, the liking for food, and probably as a consequence of that body mass index, is correlated with the functional connectivity of the orbitofrontal cortex and ventromedial prefrontal cortex. This suggests that individual differences in these orbitofrontal cortex reward systems contribute to individual differences in food pleasantness and obesity. Implications of how these reward systems in the brain operate for understanding, preventing and treating obesity are described.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK
- Department of Computer Science, University of Warwick, Coventry, UK
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22
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Verhagen JV, Baker KL, Vasan G, Pieribone VA, Rolls ET. Odor encoding by signals in the olfactory bulb. J Neurophysiol 2023; 129:431-444. [PMID: 36598147 PMCID: PMC9925169 DOI: 10.1152/jn.00449.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/21/2022] [Accepted: 12/22/2022] [Indexed: 01/05/2023] Open
Abstract
To understand the operation of the olfactory system, it is essential to know how information is encoded in the olfactory bulb. We applied Shannon information theoretic methods to address this, with signals from up to 57 glomeruli simultaneously optically imaged from presynaptic inputs in glomeruli in the mouse dorsal (dOB) and lateral (lOB) olfactory bulb, in response to six exemplar pure chemical odors. We discovered that, first, the tuning of these signals from glomeruli to a set of odors is remarkably broad, with a mean sparseness of 0.83 and a mean signal correlation of 0.64. Second, both of these factors contribute to the low information that is available from the responses of even populations of many tens of glomeruli, which was only 1.35 bits across 33 glomeruli on average, compared with the 2.58 bits required to perfectly encode these six odors. Third, although there is considerable interest in the possibility of temporal encoding of stimulus including odor identity, the amount of information in the temporal aspects of the presynaptic glomerular responses was low (mean 0.11 bits) and, importantly, was redundant with respect to the information available from the rates. Fourth, the information from simultaneously recorded glomeruli asymptotes very gradually and nonlinearly, showing that glomeruli do not have independent responses. Fifth, the information from a population became available quite rapidly, within 100 ms of sniff onset, and the peak of the glomerular response was at 200 ms. Sixth, the information from the lOB was not additive with that of the dOB.NEW & NOTEWORTHY We report broad tuning and low odor information available across the lateral and dorsal bulb populations of glomeruli. Even though response latencies can be significantly predictive of stimulus identity, such contained very little information and none that was not redundant with information based on rate coding alone. Last, in line with the emerging notion of the important role of earliest stages of responses ("primacy"), we report a very rapid rise in information after each inhalation.
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Affiliation(s)
- Justus V Verhagen
- The John B. Pierce Laboratory, New Haven, Connecticut
- Department of Neuroscience, Yale University, New Haven, Connecticut
| | - Keeley L Baker
- The John B. Pierce Laboratory, New Haven, Connecticut
- Department of Neuroscience, Yale University, New Haven, Connecticut
| | - Ganesh Vasan
- The John B. Pierce Laboratory, New Haven, Connecticut
- Department of Neuroscience, Yale University, New Haven, Connecticut
| | - Vincent A Pieribone
- The John B. Pierce Laboratory, New Haven, Connecticut
- Department of Neuroscience, Yale University, New Haven, Connecticut
- Department of Cellular and Molecular Physiology, Yale School of Medicine, New Haven, Connecticut
| | - Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, United Kingdom
- University of Warwick, Coventry, United Kingdom
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23
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Rolls ET, Wirth S, Deco G, Huang C, Feng J. The human posterior cingulate, retrosplenial, and medial parietal cortex effective connectome, and implications for memory and navigation. Hum Brain Mapp 2023; 44:629-655. [PMID: 36178249 PMCID: PMC9842927 DOI: 10.1002/hbm.26089] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 09/05/2022] [Accepted: 09/07/2022] [Indexed: 01/25/2023] Open
Abstract
The human posterior cingulate, retrosplenial, and medial parietal cortex are involved in memory and navigation. The functional anatomy underlying these cognitive functions was investigated by measuring the effective connectivity of these Posterior Cingulate Division (PCD) regions in the Human Connectome Project-MMP1 atlas in 171 HCP participants, and complemented with functional connectivity and diffusion tractography. First, the postero-ventral parts of the PCD (31pd, 31pv, 7m, d23ab, and v23ab) have effective connectivity with the temporal pole, inferior temporal visual cortex, cortex in the superior temporal sulcus implicated in auditory and semantic processing, with the reward-related vmPFC and pregenual anterior cingulate cortex, with the inferior parietal cortex, and with the hippocampal system. This connectivity implicates it in hippocampal episodic memory, providing routes for "what," reward and semantic schema-related information to access the hippocampus. Second, the antero-dorsal parts of the PCD (especially 31a and 23d, PCV, and also RSC) have connectivity with early visual cortical areas including those that represent spatial scenes, with the superior parietal cortex, with the pregenual anterior cingulate cortex, and with the hippocampal system. This connectivity implicates it in the "where" component for hippocampal episodic memory and for spatial navigation. The dorsal-transitional-visual (DVT) and ProStriate regions where the retrosplenial scene area is located have connectivity from early visual cortical areas to the parahippocampal scene area, providing a ventromedial route for spatial scene information to reach the hippocampus. These connectivities provide important routes for "what," reward, and "where" scene-related information for human hippocampal episodic memory and navigation. The midcingulate cortex provides a route from the anterior dorsal parts of the PCD and the supracallosal part of the anterior cingulate cortex to premotor regions.
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Affiliation(s)
- Edmund T. Rolls
- Oxford Centre for Computational NeuroscienceOxfordUK
- Department of Computer ScienceUniversity of WarwickCoventryUK
- Institute of Science and Technology for Brain Inspired IntelligenceFudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain Inspired IntelligenceFudan University, Ministry of EducationShanghaiChina
- Fudan ISTBI—ZJNU Algorithm Centre for Brain‐Inspired IntelligenceZhejiang Normal UniversityJinhuaChina
| | - Sylvia Wirth
- Institut des Sciences Cognitives Marc Jeannerod, UMR 5229CNRS and University of LyonBronFrance
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain
- Brain and CognitionPompeu Fabra UniversityBarcelonaSpain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA)Universitat Pompeu FabraBarcelonaSpain
| | - Chu‐Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive ScienceEast China Normal UniversityShanghaiChina
| | - Jianfeng Feng
- Department of Computer ScienceUniversity of WarwickCoventryUK
- Institute of Science and Technology for Brain Inspired IntelligenceFudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain Inspired IntelligenceFudan University, Ministry of EducationShanghaiChina
- Fudan ISTBI—ZJNU Algorithm Centre for Brain‐Inspired IntelligenceZhejiang Normal UniversityJinhuaChina
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24
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Rolls ET, Deco G, Huang CC, Feng J. Prefrontal and somatosensory-motor cortex effective connectivity in humans. Cereb Cortex 2022; 33:4939-4963. [PMID: 36227217 DOI: 10.1093/cercor/bhac391] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/07/2022] [Accepted: 09/08/2022] [Indexed: 11/12/2022] Open
Abstract
Effective connectivity, functional connectivity, and tractography were measured between 57 cortical frontal and somatosensory regions and the 360 cortical regions in the Human Connectome Project (HCP) multimodal parcellation atlas for 171 HCP participants. A ventral somatosensory stream connects from 3b and 3a via 1 and 2 and then via opercular and frontal opercular regions to the insula, which then connects to inferior parietal PF regions. This stream is implicated in "what"-related somatosensory processing of objects and of the body and in combining with visual inputs in PF. A dorsal "action" somatosensory stream connects from 3b and 3a via 1 and 2 to parietal area 5 and then 7. Inferior prefrontal regions have connectivity with the inferior temporal visual cortex and orbitofrontal cortex, are implicated in working memory for "what" processing streams, and provide connectivity to language systems, including 44, 45, 47l, TPOJ1, and superior temporal visual area. The dorsolateral prefrontal cortex regions that include area 46 have connectivity with parietal area 7 and somatosensory inferior parietal regions and are implicated in working memory for actions and planning. The dorsal prefrontal regions, including 8Ad and 8Av, have connectivity with visual regions of the inferior parietal cortex, including PGs and PGi, and are implicated in visual and auditory top-down attention.
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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.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
| | - Gustavo Deco
- Computational Neuroscience Group, Department of Information and Communication Technologies, Center for Brain and Cognition, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain.,Brain and Cognition, Pompeu Fabra University, Barcelona 08018, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai 200602, China.,Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 200602, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
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25
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Digital computing through randomness and order in neural networks. Proc Natl Acad Sci U S A 2022; 119:e2115335119. [PMID: 35947616 PMCID: PMC9388095 DOI: 10.1073/pnas.2115335119] [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] [Indexed: 11/18/2022] Open
Abstract
We propose that coding and decoding in the brain are achieved through digital computation using three principles: relative ordinal coding of inputs, random connections between neurons, and belief voting. Due to randomization and despite the coarseness of the relative codes, we show that these principles are sufficient for coding and decoding sequences with error-free reconstruction. In particular, the number of neurons needed grows linearly with the size of the input repertoire growing exponentially. We illustrate our model by reconstructing sequences with repertoires on the order of a billion items. From this, we derive the Shannon equations for the capacity limit to learn and transfer information in the neural population, which is then generalized to any type of neural network. Following the maximum entropy principle of efficient coding, we show that random connections serve to decorrelate redundant information in incoming signals, creating more compact codes for neurons and therefore, conveying a larger amount of information. Henceforth, despite the unreliability of the relative codes, few neurons become necessary to discriminate the original signal without error. Finally, we discuss the significance of this digital computation model regarding neurobiological findings in the brain and more generally with artificial intelligence algorithms, with a view toward a neural information theory and the design of digital neural networks.
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Abstract
Humans have the remarkable ability to continually store new memories, while maintaining old memories for a lifetime. How the brain avoids catastrophic forgetting of memories due to interference between encoded memories is an open problem in computational neuroscience. Here we present a model for continual learning in a recurrent neural network combining Hebbian learning, synaptic decay and a novel memory consolidation mechanism: memories undergo stochastic rehearsals with rates proportional to the memory's basin of attraction, causing self-amplified consolidation. This mechanism gives rise to memory lifetimes that extend much longer than the synaptic decay time, and retrieval probability of memories that gracefully decays with their age. The number of retrievable memories is proportional to a power of the number of neurons. Perturbations to the circuit model cause temporally-graded retrograde and anterograde deficits, mimicking observed memory impairments following neurological trauma.
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Rolls ET. The hippocampus, ventromedial prefrontal cortex, and episodic and semantic memory. Prog Neurobiol 2022; 217:102334. [PMID: 35870682 DOI: 10.1016/j.pneurobio.2022.102334] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/07/2022] [Accepted: 07/19/2022] [Indexed: 11/24/2022]
Abstract
The human ventromedial prefrontal cortex (vmPFC)/anterior cingulate cortex is implicated in reward and emotion, but also in memory. It is shown how the human orbitofrontal cortex connecting with the vmPFC and anterior cingulate cortex provide a route to the hippocampus for reward and emotional value to be incorporated into episodic memory, enabling memory of where a reward was seen. It is proposed that this value component results in primarily episodic memories with some value component to be repeatedly recalled from the hippocampus so that they are more likely to become incorporated into neocortical semantic and autobiographical memories. The same orbitofrontal and anterior cingulate regions also connect in humans to the septal and basal forebrain cholinergic nuclei, thereby helping to consolidate memory, and helping to account for why damage to the vMPFC impairs memory. The human hippocampus and vmPFC thus contribute in complementary ways to forming episodic and semantic memories.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK; University of Warwick, Department of Computer Science, Coventry, UK.
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Rolls ET, Deco G, Huang CC, Feng J. Multiple cortical visual streams in humans. Cereb Cortex 2022; 33:3319-3349. [PMID: 35834308 DOI: 10.1093/cercor/bhac276] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 06/16/2022] [Accepted: 06/17/2022] [Indexed: 11/14/2022] Open
Abstract
The effective connectivity between 55 visual cortical regions and 360 cortical regions was measured in 171 HCP participants using the HCP-MMP atlas, and complemented with functional connectivity and diffusion tractography. A Ventrolateral Visual "What" Stream for object and face recognition projects hierarchically to the inferior temporal visual cortex, which projects to the orbitofrontal cortex for reward value and emotion, and to the hippocampal memory system. A Ventromedial Visual "Where" Stream for scene representations connects to the parahippocampal gyrus and hippocampus. An Inferior STS (superior temporal sulcus) cortex Semantic Stream receives from the Ventrolateral Visual Stream, from visual inferior parietal PGi, and from the ventromedial-prefrontal reward system and connects to language systems. A Dorsal Visual Stream connects via V2 and V3A to MT+ Complex regions (including MT and MST), which connect to intraparietal regions (including LIP, VIP and MIP) involved in visual motion and actions in space. It performs coordinate transforms for idiothetic update of Ventromedial Stream scene representations. A Superior STS cortex Semantic Stream receives visual inputs from the Inferior STS Visual Stream, PGi, and STV, and auditory inputs from A5, is activated by face expression, motion and vocalization, and is important in social behaviour, and connects to language systems.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, United Kingdom.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
| | - Gustavo Deco
- Computational Neuroscience Group, Department of Information and Communication Technologies, Center for Brain and Cognition, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain.,Brain and Cognition, Pompeu Fabra University, Barcelona 08018, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai 200602, China.,Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 200602, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
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Zeltser G, Sukhanov IM, Nevorotin AJ. MMM - The molecular model of memory. J Theor Biol 2022; 549:111219. [PMID: 35810778 DOI: 10.1016/j.jtbi.2022.111219] [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: 12/04/2021] [Revised: 07/01/2022] [Accepted: 07/04/2022] [Indexed: 11/17/2022]
Abstract
Identifying mechanisms underlying neurons ability to process information including acquisition, storage, and retrieval plays an important role in the understanding of the different types of memory, pathogenesis of many neurological diseases affecting memory and therapeutic target discovery. However, the traditional understanding of the mechanisms of memory associated with the electrical signals having a unique combination of frequency and amplitude does not answer the question how the memories can survive for life-long periods of time, while exposed to synaptic noise. Recent evidence suggests that, apart from neuronal circuits, a diversity of the molecular memory (MM) carriers, are essential for memory performance. The molecular model of memory (MMM) is proposed, according to which each item of incoming information (the elementary memory item - eMI) is encoded by both circuitries, with the unique for a given MI electrical parameters, and also the MM carriers, unique by its molecular composition. While operating as the carriers of incoming information, the MMs, are functioning within the neuron plasma membrane. Inactive (latent) initially, during acquisition each of the eMIs is activated to become a virtual copy of some real fact or events bygone. This activation is accompanied by the considerable remodeling of the MM molecule associated with the resonance effect.
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Affiliation(s)
| | - Ilya M Sukhanov
- Lab. Behavioral Pharmacology, Dept. Psychopharmacology, Valdman Institute of Pharmacology, I.P. Pavlov Medical University, Leo Tolstoi Street 6/8, St. Petersburg 197022, The Russian Federation
| | - Alexey J Nevorotin
- Laboratory of Electron Microscopy, I.P. Pavlov Medical University, Leo Tolstoi Street 6/8, St. Petersburg 197022, The Russian Federation
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Ward LM, Guevara R. Qualia and Phenomenal Consciousness Arise From the Information Structure of an Electromagnetic Field in the Brain. Front Hum Neurosci 2022; 16:874241. [PMID: 35860400 PMCID: PMC9289677 DOI: 10.3389/fnhum.2022.874241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 06/17/2022] [Indexed: 11/17/2022] Open
Abstract
In this paper we address the following problems and provide realistic answers to them: (1) What could be the physical substrate for subjective, phenomenal, consciousness (P-consciousness)? Our answer: the electromagnetic (EM) field generated by the movement and changes of electrical charges in the brain. (2) Is this substrate generated in some particular part of the brains of conscious entities or does it comprise the entirety of the brain/body? Our answer: a part of the thalamus in mammals, and homologous parts of other brains generates the critical EM field. (3) From whence arise the qualia experienced in P-consciousness? Our answer, the relevant EM field is “structured” by emulating in the brain the information in EM fields arising from both external (the environment) and internal (the body) sources. (4) What differentiates the P-conscious EM field from other EM fields, e.g., the flux of photons scattered from object surfaces, the EM field of an electro-magnet, or the EM fields generated in the brain that do not enter P-consciousness, such as those generated in the retina or occipital cortex, or those generated in brain areas that guide behavior through visual information in persons exhibiting “blindsight”? Our answer: living systems express a boundary between themselves and the environment, requiring them to model (coarsely emulate) information from their environment in order to control through actions, to the extent possible, the vast sea of variety in which they are immersed. This model, expressed in an EM field, is P-consciousness. The model is the best possible representation of the moment-to-moment niche-relevant (action-relevant: affordance) information an organism can generate (a Gestalt). Information that is at a lower level than niche-relevant, such as the unanalyzed retinal vector-field, is not represented in P-consciousness because it is not niche-relevant. Living organisms have sensory and other systems that have evolved to supply such information, albeit in a coarse form.
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Affiliation(s)
- Lawrence M. Ward
- Department of Psychology and Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
- *Correspondence: Lawrence M. Ward,
| | - Ramón Guevara
- Department of Physics and Astronomy, University of Padua, Padua, Italy
- Department of Developmental Psychology and Socialization, Padova Neuroscience Center, University of Padua, Padua, Italy
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Hee Lee J, Lee S, Kim D, Jae Lee K. Implantable Micro-Light-Emitting Diode (µLED)-based optogenetic interfaces toward human applications. Adv Drug Deliv Rev 2022; 187:114399. [PMID: 35716898 DOI: 10.1016/j.addr.2022.114399] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 05/29/2022] [Accepted: 06/10/2022] [Indexed: 11/25/2022]
Abstract
Optogenetics has received wide attention in biomedical fields because of itsadvantages in temporal precision and spatial resolution. Beyond contributions to important advances in fundamental research, optogenetics is inspiring a shift towards new methods of improving human well-being and treating diseases. Soft, flexible and biocompatible systems using µLEDs as a light source have been introduced to realize brain-compatible optogenetic implants, but there are still many technical challenges to overcome before their human applications. In this review, we address progress in the development of implantable µLED probes and recent achievements in (i) device engineering design, (ii) driving power, (iii) multifunctionality and (iv) closed-loop systems. (v) Expanded optogenetic applications based on remarkable advances in µLED implants will also be discussed.
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Affiliation(s)
- Jae Hee Lee
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Sinjeong Lee
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Daesoo Kim
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
| | - Keon Jae Lee
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
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Rolls ET, Deco G, Huang CC, Feng J. The human language effective connectome. Neuroimage 2022; 258:119352. [PMID: 35659999 DOI: 10.1016/j.neuroimage.2022.119352] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 05/31/2022] [Indexed: 01/07/2023] Open
Abstract
To advance understanding of brain networks involved in language, the effective connectivity between 26 cortical regions implicated in language by a community analysis and 360 cortical regions was measured in 171 humans from the Human Connectome Project, and complemented with functional connectivity and diffusion tractography, all using the HCP multimodal parcellation atlas. A (semantic) network (Group 1) involving inferior cortical regions of the superior temporal sulcus cortex (STS) with the adjacent inferior temporal visual cortex TE1a and temporal pole TG, and the connected parietal PGi region, has effective connectivity with inferior temporal visual cortex (TE) regions; with parietal PFm which also has visual connectivity; with posterior cingulate cortex memory-related regions; with the frontal pole, orbitofrontal cortex, and medial prefrontal cortex; with the dorsolateral prefrontal cortex; and with 44 and 45 for output regions. It is proposed that this system can build in its temporal lobe (STS and TG) and parietal parts (PGi and PGs) semantic representations of objects incorporating especially their visual and reward properties. Another (semantic) network (Group 3) involving superior regions of the superior temporal sulcus cortex and more superior temporal lobe regions including STGa, auditory A5, TPOJ1, the STV and the Peri-Sylvian Language area (PSL) has effective connectivity with auditory areas (A1, A4, A5, Pbelt); with relatively early visual areas involved in motion, e.g., MT and MST, and faces/words (FFC); with somatosensory regions (frontal opercular FOP, insula and parietal PF); with other TPOJ regions; and with the inferior frontal gyrus regions (IFJa and IFSp). It is proposed that this system builds semantic representations specialising in auditory and related facial motion information useful in theory of mind and somatosensory / body image information, with outputs directed not only to regions 44 and 45, but also to premotor 55b and midcingulate premotor cortex. Both semantic networks (Groups 1 and 3) have access to the hippocampal episodic memory system via parahippocampal TF. A third largely frontal network (Group 2) (44, 45, 47l; 55b; the Superior Frontal Language region SFL; and including temporal pole TGv) receives effective connectivity from the two semantic systems, and is implicated in syntax and speech output.
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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; Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China.
| | - Gustavo Deco
- Department of Information and Communication Technologies, Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Brain and Cognition, Pompeu Fabra University, Barcelona 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200602, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK; Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
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He L, Caudill MS, Jing J, Wang W, Sun Y, Tang J, Jiang X, Zoghbi HY. A weakened recurrent circuit in the hippocampus of Rett syndrome mice disrupts long-term memory representations. Neuron 2022; 110:1689-1699.e6. [PMID: 35290792 PMCID: PMC9930308 DOI: 10.1016/j.neuron.2022.02.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 12/30/2021] [Accepted: 02/16/2022] [Indexed: 02/02/2023]
Abstract
Successful recall of a contextual memory requires reactivating ensembles of hippocampal cells that were allocated during memory formation. Altering the ratio of excitation-to-inhibition (E/I) during memory retrieval can bias cell participation in an ensemble and hinder memory recall. In the case of Rett syndrome (RTT), a neurological disorder with severe learning and memory deficits, the E/I balance is altered, but the source of this imbalance is unknown. Using in vivo imaging during an associative memory task, we show that during long-term memory retrieval, RTT CA1 cells poorly distinguish mnemonic context and form larger ensembles than wild-type mouse cells. Simultaneous multiple whole-cell recordings revealed that mutant somatostatin expressing interneurons (SOM) are poorly recruited by CA1 pyramidal cells and are less active during long-term memory retrieval in vivo. Chemogenetic manipulation revealed that reduced SOM activity underlies poor long-term memory recall. Our findings reveal a disrupted recurrent CA1 circuit contributing to RTT memory impairment.
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Affiliation(s)
- Lingjie He
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; Howard Hughes Medical Institute, Baylor College of Medicine, Houston, TX, USA
| | - Matthew S Caudill
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Junzhan Jing
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Wei Wang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA
| | - Yaling Sun
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; Howard Hughes Medical Institute, Baylor College of Medicine, Houston, TX, USA
| | - Jianrong Tang
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Xiaolong Jiang
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Huda Y Zoghbi
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA; Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA; Department of Neurology, Baylor College of Medicine, Houston, TX, USA; Howard Hughes Medical Institute, Baylor College of Medicine, Houston, TX, USA.
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Gutierrez-Galan D, Schoepe T, Dominguez-Morales JP, Jimenez-Fernandez A, Chicca E, Linares-Barranco A. An Event-Based Digital Time Difference Encoder Model Implementation for Neuromorphic Systems. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:1959-1973. [PMID: 34495850 DOI: 10.1109/tnnls.2021.3108047] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Neuromorphic systems are a viable alternative to conventional systems for real-time tasks with constrained resources. Their low power consumption, compact hardware realization, and low-latency response characteristics are the key ingredients of such systems. Furthermore, the event-based signal processing approach can be exploited for reducing the computational load and avoiding data loss due to its inherently sparse representation of sensed data and adaptive sampling time. In event-based systems, the information is commonly coded by the number of spikes within a specific temporal window. However, the temporal information of event-based signals can be difficult to extract when using rate coding. In this work, we present a novel digital implementation of the model, called time difference encoder (TDE), for temporal encoding on event-based signals, which translates the time difference between two consecutive input events into a burst of output events. The number of output events along with the time between them encodes the temporal information. The proposed model has been implemented as a digital circuit with a configurable time constant, allowing it to be used in a wide range of sensing tasks that require the encoding of the time difference between events, such as optical flow-based obstacle avoidance, sound source localization, and gas source localization. This proposed bioinspired model offers an alternative to the Jeffress model for the interaural time difference estimation, which is validated in this work with a sound source lateralization proof-of-concept system. The model was simulated and implemented on a field-programmable gate array (FPGA), requiring 122 slice registers of hardware resources and less than 1 mW of power consumption.
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Benucci A. Motor-related signals support localization invariance for stable visual perception. PLoS Comput Biol 2022; 18:e1009928. [PMID: 35286305 PMCID: PMC8947590 DOI: 10.1371/journal.pcbi.1009928] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 03/24/2022] [Accepted: 02/16/2022] [Indexed: 11/19/2022] Open
Abstract
Our ability to perceive a stable visual world in the presence of continuous movements of the body, head, and eyes has puzzled researchers in the neuroscience field for a long time. We reformulated this problem in the context of hierarchical convolutional neural networks (CNNs)-whose architectures have been inspired by the hierarchical signal processing of the mammalian visual system-and examined perceptual stability as an optimization process that identifies image-defining features for accurate image classification in the presence of movements. Movement signals, multiplexed with visual inputs along overlapping convolutional layers, aided classification invariance of shifted images by making the classification faster to learn and more robust relative to input noise. Classification invariance was reflected in activity manifolds associated with image categories emerging in late CNN layers and with network units acquiring movement-associated activity modulations as observed experimentally during saccadic eye movements. Our findings provide a computational framework that unifies a multitude of biological observations on perceptual stability under optimality principles for image classification in artificial neural networks.
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Affiliation(s)
- Andrea Benucci
- RIKEN Center for Brain Science, Wako-shi, Japan
- University of Tokyo, Graduate School of Information Science and Technology, Department of Mathematical Informatics, Tokyo, Japan
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Bahramian A, Ramadoss J, Nazarimehr F, Rajagopal K, Jafari S, Hussain I. A simple one-dimensional map-based model of spiking neurons with wide ranges of firing rates and complexities. J Theor Biol 2022; 539:111062. [DOI: 10.1016/j.jtbi.2022.111062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 02/01/2022] [Accepted: 02/08/2022] [Indexed: 11/25/2022]
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37
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Aprile F, Onesto V, Gentile F. The small world coefficient 4.8 ± 1 optimizes information processing in 2D neuronal networks. NPJ Syst Biol Appl 2022; 8:4. [PMID: 35087062 PMCID: PMC8795235 DOI: 10.1038/s41540-022-00215-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 01/05/2022] [Indexed: 11/14/2022] Open
Abstract
Small world networks have recently attracted much attention because of their unique properties. Mounting evidence suggests that communication is optimized in networks with a small world topology. However, despite the relevance of the argument, little is known about the effective enhancement of information in similar graphs. Here, we provide a quantitative estimate of the efficiency of small world networks. We used a model of the brain in which neurons are described as agents that integrate the signals from other neurons and generate an output that spreads in the system. We then used the Shannon Information Entropy to decode those signals and compute the information transported in the grid as a function of its small-world-ness (\documentclass[12pt]{minimal}
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\begin{document}$${\rm{SW}}$$\end{document}SW and there is no convenience in increasing indefinitely the number of active links in the system. Supported by the findings of the work and in analogy with the exergy in thermodynamics, we introduce the concept of exordic systems: a system is exordic if it is topologically biased to transmit information efficiently.
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Affiliation(s)
- F Aprile
- Department of Electric Engineering and Information Technology, University Federico II, 80125, Naples, Italy
| | - V Onesto
- Institute of Nanotechnology, National Research Council (CNR-NANOTEC), Campus Ecotekne, via Monteroni, Lecce, 73100, Italy
| | - F Gentile
- Nanotechnology Research Center, Department of Experimental and Clinical Medicine, University of Magna Graecia, 88100, Catanzaro, Italy.
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Ma Q, Rolls ET, Huang CC, Cheng W, Feng J. Extensive cortical functional connectivity of the human hippocampal memory system. Cortex 2021; 147:83-101. [PMID: 35026557 DOI: 10.1016/j.cortex.2021.11.014] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 10/12/2021] [Accepted: 11/22/2021] [Indexed: 01/09/2023]
Abstract
The cortical connections of the human hippocampal memory system are fundamental to understanding its operation in health and disease, especially in the context of the great development of the human cortex. The functional connectivity of the human hippocampal system was analyzed in 172 participants imaged at 7T in the Human Connectome Project. The human hippocampus has high functional connectivity not only with the entorhinal cortex, but also with areas that are more distant in the ventral 'what' stream including the perirhinal cortex and temporal cortical visual areas. Parahippocampal gyrus TF in humans has connectivity with this ventral 'what' subsystem. Correspondingly for the dorsal stream, the hippocampus has high functional connectivity not only with the presubiculum, but also with areas more distant, the medial parahippocampal cortex TH which includes the parahippocampal place or scene area, the posterior cingulate including retrosplenial cortex, and the parietal cortex. Further, there is considerable cross connectivity between the ventral and dorsal streams with the hippocampus. The findings are supported by anatomical connections, which together provide an unprecedented and quantitative overview of the extensive cortical connectivity of the human hippocampal system that goes beyond hierarchically organised and segregated pathways connecting the hippocampus and neocortex, and leads to new concepts on the operation of the hippocampal memory system in humans.
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Affiliation(s)
- Qing Ma
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - Edmund T Rolls
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, UK; Oxford Centre for Computational Neuroscience, Oxford, UK.
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China.
| | - Jianfeng Feng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, UK; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China.
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Rolls ET. On pattern separation in the primate, including human, hippocampus. Trends Cogn Sci 2021; 25:920-922. [PMID: 34598879 DOI: 10.1016/j.tics.2021.07.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 07/04/2021] [Accepted: 07/08/2021] [Indexed: 11/26/2022]
Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK; Department of Computer Science, University of Warwick, Coventry, UK.
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Huang CC, Rolls ET, Hsu CCH, Feng J, Lin CP. Extensive Cortical Connectivity of the Human Hippocampal Memory System: Beyond the "What" and "Where" Dual Stream Model. Cereb Cortex 2021; 31:4652-4669. [PMID: 34013342 PMCID: PMC8866812 DOI: 10.1093/cercor/bhab113] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 04/09/2021] [Accepted: 04/10/2021] [Indexed: 10/06/2023] Open
Abstract
The human hippocampus is involved in forming new memories: damage impairs memory. The dual stream model suggests that object "what" representations from ventral stream temporal cortex project to the hippocampus via the perirhinal and then lateral entorhinal cortex, and spatial "where" representations from the dorsal parietal stream via the parahippocampal gyrus and then medial entorhinal cortex. The hippocampus can then associate these inputs to form episodic memories of what happened where. Diffusion tractography was used to reveal the direct connections of hippocampal system areas in humans. This provides evidence that the human hippocampus has extensive direct cortical connections, with connections that bypass the entorhinal cortex to connect with the perirhinal and parahippocampal cortex, with the temporal pole, with the posterior and retrosplenial cingulate cortex, and even with early sensory cortical areas. The connections are less hierarchical and segregated than in the dual stream model. This provides a foundation for a conceptualization for how the hippocampal memory system connects with the cerebral cortex and operates in humans. One implication is that prehippocampal cortical areas such as the parahippocampal TF and TH subregions and perirhinal cortices may implement specialized computations that can benefit from inputs from the dorsal and ventral streams.
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Affiliation(s)
- Chu-Chung Huang
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China
- Shanghai Changning Mental Health Center, Shanghai 200335, China
| | - Edmund T Rolls
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, China
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
- Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Chih-Chin Heather Hsu
- Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei 11217, Taiwan
- Institute of Neuroscience, National Yang-Ming Chiao Tung University, Taipei 11217, Taiwan
| | - Jianfeng Feng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, China
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Ching-Po Lin
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, China
- Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei 11217, Taiwan
- Brain Research Center, National Yang-Ming Chiao Tung University, Taipei 11217, Taiwan
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Rolls ET. The connections of neocortical pyramidal cells can implement the learning of new categories, attractor memory, and top-down recall and attention. Brain Struct Funct 2021; 226:2523-2536. [PMID: 34347165 PMCID: PMC8448704 DOI: 10.1007/s00429-021-02347-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 07/19/2021] [Indexed: 11/17/2022]
Abstract
Neocortical pyramidal cells have three key classes of excitatory input: forward inputs from the previous cortical area (or thalamus); recurrent collateral synapses from nearby pyramidal cells; and backprojection inputs from the following cortical area. The neocortex performs three major types of computation: (1) unsupervised learning of new categories, by allocating neurons to respond to combinations of inputs from the preceding cortical stage, which can be performed using competitive learning; (2) short-term memory, which can be performed by an attractor network using the recurrent collaterals; and (3) recall of what has been learned by top–down backprojections from the following cortical area. There is only one type of excitatory neuron involved, pyramidal cells, with these three types of input. It is proposed, and tested by simulations of a neuronal network model, that pyramidal cells can implement all three types of learning simultaneously, and can subsequently usefully categorise the forward inputs; keep them active in short-term memory; and later recall the representations using the backprojection input. This provides a new approach to understanding how one type of excitatory neuron in the neocortex can implement these three major types of computation, and provides a conceptual advance in understanding how the cerebral neocortex may work.
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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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Rolls ET. Learning Invariant Object and Spatial View Representations in the Brain Using Slow Unsupervised Learning. Front Comput Neurosci 2021; 15:686239. [PMID: 34366818 PMCID: PMC8335547 DOI: 10.3389/fncom.2021.686239] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 06/29/2021] [Indexed: 11/13/2022] Open
Abstract
First, neurophysiological evidence for the learning of invariant representations in the inferior temporal visual cortex is described. This includes object and face representations with invariance for position, size, lighting, view and morphological transforms in the temporal lobe visual cortex; global object motion in the cortex in the superior temporal sulcus; and spatial view representations in the hippocampus that are invariant with respect to eye position, head direction, and place. Second, computational mechanisms that enable the brain to learn these invariant representations are proposed. For the ventral visual system, one key adaptation is the use of information available in the statistics of the environment in slow unsupervised learning to learn transform-invariant representations of objects. This contrasts with deep supervised learning in artificial neural networks, which uses training with thousands of exemplars forced into different categories by neuronal teachers. Similar slow learning principles apply to the learning of global object motion in the dorsal visual system leading to the cortex in the superior temporal sulcus. The learning rule that has been explored in VisNet is an associative rule with a short-term memory trace. The feed-forward architecture has four stages, with convergence from stage to stage. This type of slow learning is implemented in the brain in hierarchically organized competitive neuronal networks with convergence from stage to stage, with only 4-5 stages in the hierarchy. Slow learning is also shown to help the learning of coordinate transforms using gain modulation in the dorsal visual system extending into the parietal cortex and retrosplenial cortex. Representations are learned that are in allocentric spatial view coordinates of locations in the world and that are independent of eye position, head direction, and the place where the individual is located. This enables hippocampal spatial view cells to use idiothetic, self-motion, signals for navigation when the view details are obscured for short periods.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, United Kingdom.,Department of Computer Science, University of Warwick, Coventry, United Kingdom
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Rolls ET. Mind Causality: A Computational Neuroscience Approach. Front Comput Neurosci 2021; 15:706505. [PMID: 34305562 PMCID: PMC8295486 DOI: 10.3389/fncom.2021.706505] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 06/10/2021] [Indexed: 11/13/2022] Open
Abstract
A neuroscience-based approach has recently been proposed for the relation between the mind and the brain. The proposal is that events at the sub-neuronal, neuronal, and neuronal network levels take place simultaneously to perform a computation that can be described at a high level as a mental state, with content about the world. It is argued that as the processes at the different levels of explanation take place at the same time, they are linked by a non-causal supervenient relationship: causality can best be described in brains as operating within but not between levels. This mind-brain theory allows mental events to be different in kind from the mechanistic events that underlie them; but does not lead one to argue that mental events cause brain events, or vice versa: they are different levels of explanation of the operation of the computational system. Here, some implications are developed. It is proposed that causality, at least as it applies to the brain, should satisfy three conditions. First, interventionist tests for causality must be satisfied. Second, the causally related events should be at the same level of explanation. Third, a temporal order condition must be satisfied, with a suitable time scale in the order of 10 ms (to exclude application to quantum physics; and a cause cannot follow an effect). Next, although it may be useful for different purposes to describe causality involving the mind and brain at the mental level, or at the brain level, it is argued that the brain level may sometimes be more accurate, for sometimes causal accounts at the mental level may arise from confabulation by the mentalee, whereas understanding exactly what computations have occurred in the brain that result in a choice or action will provide the correct causal account for why a choice or action was made. Next, it is argued that possible cases of "downward causation" can be accounted for by a within-levels-of-explanation account of causality. This computational neuroscience approach provides an opportunity to proceed beyond Cartesian dualism and physical reductionism in considering the relations between the mind and the brain.
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Affiliation(s)
- Edmund T. Rolls
- Oxford Centre for Computational Neuroscience, Oxford, United Kingdom
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
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45
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Sakon JJ, Suzuki WA. Neural evidence for recognition of naturalistic videos in monkey hippocampus. Hippocampus 2021; 31:916-932. [PMID: 34021646 DOI: 10.1002/hipo.23335] [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: 11/09/2020] [Revised: 02/26/2021] [Accepted: 04/17/2021] [Indexed: 11/11/2022]
Abstract
The role of the hippocampus in recognition memory has long been a source of debate. Tasks used to study recognition that typically require an explicit probe, where the participant must make a response to prove they remember, yield mixed results on hippocampal involvement. Here, we tasked monkeys to freely view naturalistic videos, and only tested their memory via looking times for two separate novel versus repeat video conditions on each trial. Notably, a large proportion (>30%) of hippocampal neurons differentiated these videos via changes in firing rates time-locked to the duration of their presentation on screen, and not during the delay period between them as would be expected for working memory. Many of these single neurons (>15%) contributed to both retrieval conditions, and differentiated novel from repeat videos across trials with trial-unique content, suggesting they detect familiarity. The majority of neurons contributing to the classifier showed an enhancement in firing rate on repeat compared with novel videos, a pattern which has not previously been shown in hippocampus. These results suggest the hippocampus contributes to recognition memory via familiarity during free-viewing.
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Affiliation(s)
- John J Sakon
- Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Wendy A Suzuki
- Center for Neural Science, New York University, New York, New York, USA
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Galus W. Whether Mirror and Conceptual Neurons are Myths? Sparse vs. Distributed Neuronal Representations. NETWORK (BRISTOL, ENGLAND) 2021; 32:110-134. [PMID: 35072588 DOI: 10.1080/0954898x.2022.2029967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 12/02/2021] [Accepted: 01/10/2022] [Indexed: 06/14/2023]
Abstract
Multi-layer neural networks, mirror neurons, and gnostic neurons are concepts that assign neural representations to mental representations of percepts and inner sensations. However, none of these approaches alone can explain the higher mental functions, which we observe in natural minds from the third and first-person perspectives through introspection. Recent concepts of preservation of chemical traces of sensory stimuli and hierarchical structures of postsynaptic associations represented by specifically organized groups of neurons combine these concepts and effectively explain much more complex mental functions. To find an operative model and understand how knowledge in the mind creates conscious sensations, we explain how perceptions, sensory impressions, and environment models gain their neural representations. It was pointed out ways to detect the similarity of structures representing previously remembered patterns to the mental and neuronal representations of new perceptions, ways of their associations, and principles of information processing. Supplemented, presented in earlier works, concepts of competition of representations stimulation and factors stimulating their action explain the mind's complex functions, including speech production and recognition. We postulate that using new methods of modelling the neural network's functions through the parallel physical process allows creating a physical model of natural and artificial, conscious, intelligent minds.
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Novikov N, Gutkin B. Role of synaptic nonlinearity in persistent firing rate shifts caused by external periodic forcing. Phys Rev E 2021; 101:052408. [PMID: 32575174 DOI: 10.1103/physreve.101.052408] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 03/03/2020] [Indexed: 11/07/2022]
Abstract
Information storage and processing in the brain largely relies on the neural population coding principle. In this framework, information is reflected in the population firing rate that reflects asynchronous irregular spiking of its constituent neurons. Periodic modulations of neural activity can lead to neural activity oscillations. Data indicate that such oscillations are ubiquitous in brain activity and are modulated, in frequency and amplitude, in a functionally meaningful manner. The relationship between oscillations and the population rate code remains an open issue. While ample works show how changes in the mean firing rate may alter neural oscillations, the reverse connection is unclear. One notable possibility is that oscillatory activity impinging on a neural population modulates its mean firing rate, thereby impacting information processing. We suggest that such modulation requires nonlinearities and propose nonlinear excitatory coupling via slow N-methyl-D-aspartate (NMDA) receptors as the prevalent mechanism. The aim of our paper is to theoretically explore to what extent the NMDA-related mechanism could account for oscillation-induced mean firing rate changes. We consider a mean-field model of a neural circuit containing an excitatory and an inhibitory population with linear transfer functions. Along with NMDA excitation, the model included fast recurrent excitatory and inhibitory connectivity. To explicitly study the effects of impinging oscillation on the rate dynamics, we subjected the circuit to a sinusoidal input signal imitating an input from distant brain regions or from a larger network into which the circuit is embedded. Using time-scale separation and time-averaging techniques, we developed a geometric method to determine the oscillation-induced mean firing rate shifts and validated it by numeric simulations of the model. Our results indicate that a large-amplitude stable firing rate shift requires nonlinear NMDA synapses on both the excitatory and the inhibitory populations. Our results delineate specific neural synaptic properties that enable neural oscillations to act as flexible modulators of the population rate code.
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Affiliation(s)
- Nikita Novikov
- Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow 101000, Russia
| | - Boris Gutkin
- Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow 101000, Russia.,Group for Neural Theory, LNC INSERM U960, Department of Cognitive Studies, Ecole Normale Superieure PSL Research University, Paris 75005, France
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Epigenomically Bistable Regions across Neuron-Specific Genes Govern Neuron Eligibility to a Coding Ensemble in the Hippocampus. Cell Rep 2021; 31:107789. [PMID: 32579919 PMCID: PMC7440841 DOI: 10.1016/j.celrep.2020.107789] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 04/12/2020] [Accepted: 05/29/2020] [Indexed: 12/20/2022] Open
Abstract
Sensory inputs activate sparse neuronal ensembles in the dentate gyrus of the hippocampus, but how eligibility of individual neurons to recruitment is determined remains elusive. We identify thousands of largely bistable (CpG methylated or unmethylated) regions within neuronal gene bodies, established during mouse dentate gyrus development. Reducing DNA methylation and the proportion of the methylated epialleles at bistable regions compromises novel context-induced neuronal activation. Conversely, increasing methylation and the frequency of the methylated epialleles at bistable regions enhances intrinsic excitability. Single-nucleus profiling reveals enrichment of specific epialleles related to a subset of primarily exonic, bistable regions in activated neurons. Genes displaying both differential methylation and expression in activated neurons define a network of proteins regulating neuronal excitability and structural plasticity. We propose a model in which bistable regions create neuron heterogeneity and constellations of exonic methylation, which may contribute to cell-specific gene expression, excitability, and eligibility to a coding ensemble. Odell et al. show regions within neuronal genes with bistable DNA methylation states that are associated with gene expression, excitability, and activation in the dentate gyrus of the hippocampus. These data suggest that the methylation state of bistable regions dictates, via modulating gene expression, neuron eligibility to a coding ensemble.
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An Alternative Explanation for Difficulties with Speech in Background Talkers: Abnormal Fusion of Vowels Across Fundamental Frequency and Ears. J Assoc Res Otolaryngol 2021; 22:443-461. [PMID: 33877470 DOI: 10.1007/s10162-021-00790-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 02/07/2021] [Indexed: 10/21/2022] Open
Abstract
Normal-hearing (NH) listeners use frequency cues, such as fundamental frequency (voice pitch), to segregate sounds into discrete auditory streams. However, many hearing-impaired (HI) individuals have abnormally broad binaural pitch fusion which leads to fusion and averaging of the original monaural pitches into the same stream instead of segregating the two streams (Oh and Reiss, 2017) and may similarly lead to fusion and averaging of speech streams across ears. In this study, using dichotic speech stimuli, we examined the relationship between speech fusion and vowel identification. Dichotic vowel perception was measured in NH and HI listeners, with across-ear fundamental frequency differences varied. Synthetic vowels /i/, /u/, /a/, and /ae/ were generated with three fundamental frequencies (F0) of 106.9, 151.2, and 201.8 Hz and presented dichotically through headphones. For HI listeners, stimuli were shaped according to NAL-NL2 prescriptive targets. Although the dichotic vowels presented were always different across ears, listeners were not informed that there were no single vowel trials and could identify one vowel or two different vowels on each trial. When there was no F0 difference between the ears, both NH and HI listeners were more likely to fuse the vowels and identify only one vowel. As ΔF0 increased, NH listeners increased the percentage of two-vowel responses, but HI listeners were more likely to continue to fuse vowels even with large ΔF0. Binaural tone fusion range was significantly correlated with vowel fusion rates in both NH and HI listeners. Confusion patterns with dichotic vowels differed from those seen with concurrent monaural vowels, suggesting different mechanisms behind the errors. Together, the findings suggest that broad fusion leads to spectral blending across ears, even for different ΔF0, and may hinder the stream segregation and understanding of speech in the presence of competing talkers.
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Rolls ET. Attractor cortical neurodynamics, schizophrenia, and depression. Transl Psychiatry 2021; 11:215. [PMID: 33846293 PMCID: PMC8041760 DOI: 10.1038/s41398-021-01333-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Revised: 03/09/2021] [Accepted: 03/24/2021] [Indexed: 12/17/2022] Open
Abstract
The local recurrent collateral connections between cortical neurons provide a basis for attractor neural networks for memory, attention, decision-making, and thereby for many aspects of human behavior. In schizophrenia, a reduction of the firing rates of cortical neurons, caused for example by reduced NMDA receptor function or reduced spines on neurons, can lead to instability of the high firing rate attractor states that normally implement short-term memory and attention in the prefrontal cortex, contributing to the cognitive symptoms. Reduced NMDA receptor function in the orbitofrontal cortex by reducing firing rates may produce negative symptoms, by reducing reward, motivation, and emotion. Reduced functional connectivity between some brain regions increases the temporal variability of the functional connectivity, contributing to the reduced stability and more loosely associative thoughts. Further, the forward projections have decreased functional connectivity relative to the back projections in schizophrenia, and this may reduce the effects of external bottom-up inputs from the world relative to internal top-down thought processes. Reduced cortical inhibition, caused by a reduction of GABA neurotransmission, can lead to instability of the spontaneous firing states of cortical networks, leading to a noise-induced jump to a high firing rate attractor state even in the absence of external inputs, contributing to the positive symptoms of schizophrenia. In depression, the lateral orbitofrontal cortex non-reward attractor network system is over-connected and has increased sensitivity to non-reward, providing a new approach to understanding depression. This is complemented by under-sensitivity and under-connectedness of the medial orbitofrontal cortex reward system in depression.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK.
- Department of Computer Science, University of Warwick, Coventry, UK.
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