1
|
Carvalho W, Tomov MS, de Cothi W, Barry C, Gershman SJ. Predictive Representations: Building Blocks of Intelligence. Neural Comput 2024; 36:2225-2298. [PMID: 39212963 DOI: 10.1162/neco_a_01705] [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: 02/09/2024] [Accepted: 06/10/2024] [Indexed: 09/04/2024]
Abstract
Adaptive behavior often requires predicting future events. The theory of reinforcement learning prescribes what kinds of predictive representations are useful and how to compute them. This review integrates these theoretical ideas with work on cognition and neuroscience. We pay special attention to the successor representation and its generalizations, which have been widely applied as both engineering tools and models of brain function. This convergence suggests that particular kinds of predictive representations may function as versatile building blocks of intelligence.
Collapse
Affiliation(s)
- Wilka Carvalho
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA 02134, U.S.A.
| | - Momchil S Tomov
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA 02134, U.S.A
- Motional AD LLC, Boston, MA 02210, U.S.A.
| | - William de Cothi
- Department of Cell and Developmental Biology, University College London, London WC1E 7JE, U.K.
| | - Caswell Barry
- Department of Cell and Developmental Biology, University College London, London WC1E 7JE, U.K.
| | - Samuel J Gershman
- Kempner Institute for the Study of Natural and Artificial Intelligence, and Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA 02134, U.S.A
- Center for Brains, Minds, and Machines, MIT, Cambridge, MA 02139, U.S.A.
| |
Collapse
|
2
|
Wen JH, Sorscher B, Aery Jones EA, Ganguli S, Giocomo LM. One-shot entorhinal maps enable flexible navigation in novel environments. Nature 2024:10.1038/s41586-024-08034-3. [PMID: 39385034 DOI: 10.1038/s41586-024-08034-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 09/10/2024] [Indexed: 10/11/2024]
Abstract
Animals must navigate changing environments to find food, shelter or mates. In mammals, grid cells in the medial entorhinal cortex construct a neural spatial map of the external environment1-5. However, how grid cell firing patterns rapidly adapt to novel or changing environmental features on a timescale relevant to behaviour remains unknown. Here, by recording over 15,000 grid cells in mice navigating virtual environments, we tracked the real-time state of the grid cell network. This allowed us to observe and predict how altering environmental features influenced grid cell firing patterns on a nearly instantaneous timescale. We found evidence that visual landmarks provide inputs to fixed points in the grid cell network. This resulted in stable grid cell firing patterns in novel and altered environments after a single exposure. Fixed visual landmark inputs also influenced the grid cell network such that altering landmarks induced distortions in grid cell firing patterns. Such distortions could be predicted by a computational model with a fixed landmark to grid cell network architecture. Finally, a medial entorhinal cortex-dependent task revealed that although grid cell firing patterns are distorted by landmark changes, behaviour can adapt via a downstream region implementing behavioural timescale synaptic plasticity6. Overall, our findings reveal how the navigational system of the brain constructs spatial maps that balance rapidity and accuracy. Fixed connections between landmarks and grid cells enable the brain to quickly generate stable spatial maps, essential for navigation in novel or changing environments. Conversely, plasticity in regions downstream from grid cells allows the spatial maps of the brain to more accurately mirror the external spatial environment. More generally, these findings raise the possibility of a broader neural principle: by allocating fixed and plastic connectivity across different networks, the brain can solve problems requiring both rapidity and representational accuracy.
Collapse
Affiliation(s)
- John H Wen
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Ben Sorscher
- Department of Applied Physics, Stanford University, Stanford, CA, USA
| | - Emily A Aery Jones
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Surya Ganguli
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Applied Physics, Stanford University, Stanford, CA, USA
| | - Lisa M Giocomo
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University School of Medicine, CA, Stanford, USA.
| |
Collapse
|
3
|
Liao Z, Terada S, Raikov IG, Hadjiabadi D, Szoboszlay M, Soltesz I, Losonczy A. Inhibitory plasticity supports replay generalization in the hippocampus. Nat Neurosci 2024; 27:1987-1998. [PMID: 39227715 DOI: 10.1038/s41593-024-01745-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: 10/15/2022] [Accepted: 07/31/2024] [Indexed: 09/05/2024]
Abstract
Memory consolidation assimilates recent experiences into long-term memory. This process requires the replay of learned sequences, although the content of these sequences remains controversial. Recent work has shown that the statistics of replay deviate from those of experience: stimuli that are experientially salient may be either recruited or suppressed from sharp-wave ripples. In this study, we found that this phenomenon can be explained parsimoniously and biologically plausibly by a Hebbian spike-time-dependent plasticity rule at inhibitory synapses. Using models at three levels of abstraction-leaky integrate-and-fire, biophysically detailed and abstract binary-we show that this rule enables efficient generalization, and we make specific predictions about the consequences of intact and perturbed inhibitory dynamics for network dynamics and cognition. Finally, we use optogenetics to artificially implant non-generalizable representations into the network in awake behaving mice, and we find that these representations also accumulate inhibition during sharp-wave ripples, experimentally validating a major prediction of our model. Our work outlines a potential direct link between the synaptic and cognitive levels of memory consolidation, with implications for both normal learning and neurological disease.
Collapse
Affiliation(s)
- Zhenrui Liao
- Department of Neuroscience, Columbia University, New York, NY, USA.
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA.
- Department of Neuroscience, University of Edinburgh, Edinburgh, UK.
| | - Satoshi Terada
- Department of Neuroscience, Columbia University, New York, NY, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Ivan Georgiev Raikov
- Kavli Institute for Brain Science, Columbia University, New York, NY, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Darian Hadjiabadi
- Kavli Institute for Brain Science, Columbia University, New York, NY, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Miklos Szoboszlay
- Department of Neuroscience, Columbia University, New York, NY, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Ivan Soltesz
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Department of Neurosurgery, Stanford University, Stanford, CA, USA.
| | - Attila Losonczy
- Department of Neuroscience, Columbia University, New York, NY, USA.
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
| |
Collapse
|
4
|
Tian Z, Chen J, Zhang C, Min B, Xu B, Wang L. Mental programming of spatial sequences in working memory in the macaque frontal cortex. Science 2024; 385:eadp6091. [PMID: 39325894 DOI: 10.1126/science.adp6091] [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: 04/04/2024] [Accepted: 07/12/2024] [Indexed: 09/28/2024]
Abstract
How the brain mentally sorts a series of items in a specific order within working memory (WM) remains largely unknown. We investigated mental sorting using high-throughput electrophysiological recordings in the frontal cortex of macaque monkeys, who memorized and sorted spatial sequences in forward or backward orders according to visual cues. We discovered that items at each ordinal rank in WM were encoded in separate rank-WM subspaces and then, depending on cues, were maintained or reordered between the subspaces, accompanied by two extra temporary subspaces in two operation steps. Furthermore, the cue activity served as an indexical signal to trigger sorting processes. Thus, we propose a complete conceptual framework, where the neural landscape transitions in frontal neural states underlie the symbolic system for mental programming of sequence WM.
Collapse
Affiliation(s)
- Zhenghe Tian
- Institute of Neuroscience, Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jingwen Chen
- Institute of Neuroscience, Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Cong Zhang
- Institute of Neuroscience, Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Bin Min
- Lingang Laboratory, Shanghai 200031, China
| | - Bo Xu
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liping Wang
- Institute of Neuroscience, Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| |
Collapse
|
5
|
Mahmoodi A, Luo S, Harbison C, Piray P, Rushworth MFS. Human hippocampus and dorsomedial prefrontal cortex infer and update latent causes during social interaction. Neuron 2024:S0896-6273(24)00649-4. [PMID: 39353432 DOI: 10.1016/j.neuron.2024.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 06/04/2024] [Accepted: 09/03/2024] [Indexed: 10/04/2024]
Abstract
Latent-cause inference is the process of identifying features of the environment that have caused an outcome. This problem is especially important in social settings where individuals may not make equal contributions to the outcomes they achieve together. Here, we designed a novel task in which participants inferred which of two characters was more likely to have been responsible for outcomes achieved by working together. Using computational modeling, univariate and multivariate analysis of human fMRI, and continuous theta-burst stimulation, we identified two brain regions that solved the task. Notably, as each outcome occurred, it was possible to decode the inference of its cause (the responsible character) from hippocampal activity. Activity in dorsomedial prefrontal cortex (dmPFC) updated estimates of association between cause-responsible character-and the outcome. Disruption of dmPFC activity impaired participants' ability to update their estimate as a function of inferred responsibility but spared their ability to infer responsibility.
Collapse
Affiliation(s)
- Ali Mahmoodi
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK.
| | - Shuyi Luo
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Caroline Harbison
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Payam Piray
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
| | - Matthew F S Rushworth
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
| |
Collapse
|
6
|
Tacikowski P, Kalender G, Ciliberti D, Fried I. Human hippocampal and entorhinal neurons encode the temporal structure of experience. Nature 2024:10.1038/s41586-024-07973-1. [PMID: 39322671 DOI: 10.1038/s41586-024-07973-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/20/2024] [Indexed: 09/27/2024]
Abstract
Extracting the underlying temporal structure of experience is a fundamental aspect of learning and memory that allows us to predict what is likely to happen next. Current knowledge about the neural underpinnings of this cognitive process in humans stems from functional neuroimaging research1-5. As these methods lack direct access to the neuronal level, it remains unknown how this process is computed by neurons in the human brain. Here we record from single neurons in individuals who have been implanted with intracranial electrodes for clinical reasons, and show that human hippocampal and entorhinal neurons gradually modify their activity to encode the temporal structure of a complex image presentation sequence. This representation was formed rapidly, without providing specific instructions to the participants, and persisted when the prescribed experience was no longer present. Furthermore, the structure recovered from the population activity of hippocampal-entorhinal neurons closely resembled the structural graph defining the sequence, but at the same time, also reflected the probability of upcoming stimuli. Finally, learning of the sequence graph was related to spontaneous, time-compressed replay of individual neurons' activity corresponding to previously experienced graph trajectories. These findings demonstrate that neurons in the hippocampus and entorhinal cortex integrate the 'what' and 'when' information to extract durable and predictive representations of the temporal structure of human experience.
Collapse
Affiliation(s)
- Pawel Tacikowski
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden.
- Coimbra Institute for Biomedical Imaging and Translational Research, University of Coimbra, Coimbra, Portugal.
| | - Güldamla Kalender
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Davide Ciliberti
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, USA
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Itzhak Fried
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA.
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
| |
Collapse
|
7
|
Comrie AE, Monroe EJ, Kahn AE, Denovellis EL, Joshi A, Guidera JA, Krausz TA, Berke JD, Daw ND, Frank LM. Hippocampal representations of alternative possibilities are flexibly generated to meet cognitive demands. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.23.613567. [PMID: 39386651 PMCID: PMC11463554 DOI: 10.1101/2024.09.23.613567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/12/2024]
Abstract
The cognitive ability to go beyond the present to consider alternative possibilities, including potential futures and counterfactual pasts, can support adaptive decision making. Complex and changing real-world environments, however, have many possible alternatives. Whether and how the brain can select among them to represent alternatives that meet current cognitive needs remains unknown. We therefore examined neural representations of alternative spatial locations in the rat hippocampus during navigation in a complex patch foraging environment with changing reward probabilities. We found representations of multiple alternatives along paths ahead and behind the animal, including in distant alternative patches. Critically, these representations were modulated in distinct patterns across successive trials: alternative paths were represented proportionate to their evolving relative value and predicted subsequent decisions, whereas distant alternatives were prevalent during value updating. These results demonstrate that the brain modulates the generation of alternative possibilities in patterns that meet changing cognitive needs for adaptive behavior.
Collapse
|
8
|
Son JY, Vives ML, Bhandari A, FeldmanHall O. Replay shapes abstract cognitive maps for efficient social navigation. Nat Hum Behav 2024:10.1038/s41562-024-01990-w. [PMID: 39300309 DOI: 10.1038/s41562-024-01990-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 08/16/2024] [Indexed: 09/22/2024]
Abstract
To make adaptive social decisions, people must anticipate how information flows through their social network. While this requires knowledge of how people are connected, networks are too large to have first-hand experience with every possible route between individuals. How, then, are people able to accurately track information flow through social networks? Here we find that people immediately cache abstract knowledge about social network structure as they learn who is friends with whom, which enables the identification of efficient routes between remotely connected individuals. These cognitive maps of social networks, which are built while learning, are then reshaped through overnight rest. During these extended periods of rest, a replay-like mechanism helps to make these maps increasingly abstract, which privileges improvements in social navigation accuracy for the longest communication paths that span distinct communities within the network. Together, these findings provide mechanistic insight into the sophisticated mental representations humans use for social navigation.
Collapse
Affiliation(s)
- Jae-Young Son
- Department of Cognitive and Psychological Sciences, Brown University, Providence, RI, USA
| | - Marc-Lluís Vives
- Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Apoorva Bhandari
- Department of Cognitive and Psychological Sciences, Brown University, Providence, RI, USA.
| | - Oriel FeldmanHall
- Department of Cognitive and Psychological Sciences, Brown University, Providence, RI, USA.
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA.
| |
Collapse
|
9
|
Fang C, Lindsey J, Abbott L, Aronov D, Chettih S. Barcode activity in a recurrent network model of the hippocampus enables efficient memory binding. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.09.612073. [PMID: 39345427 PMCID: PMC11429605 DOI: 10.1101/2024.09.09.612073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/01/2024]
Abstract
Forming an episodic memory requires binding together disparate elements that co-occur in a single experience. One model of this process is that neurons representing different components of a memory bind to an "index" - a subset of neurons unique to that memory. Evidence for this model has recently been found in chickadees, which use hippocampal memory to store and recall locations of cached food. Chickadee hippocampus produces sparse, high-dimensional patterns ("barcodes") that uniquely specify each caching event. Unexpectedly, the same neurons that participate in barcodes also exhibit conventional place tuning. It is unknown how barcode activity is generated, and what role it plays in memory formation and retrieval. It is also unclear how a memory index (e.g. barcodes) could function in the same neural population that represents memory content (e.g. place). Here, we design a biologically plausible model that generates barcodes and uses them to bind experiential content. Our model generates barcodes from place inputs through the chaotic dynamics of a recurrent neural network and uses Hebbian plasticity to store barcodes as attractor states. The model matches experimental observations that memory indices (barcodes) and content signals (place tuning) are randomly intermixed in the activity of single neurons. We demonstrate that barcodes reduce memory interference between correlated experiences. We also show that place tuning plays a complementary role to barcodes, enabling flexible, contextually-appropriate memory retrieval. Finally, our model is compatible with previous models of the hippocampus as generating a predictive map. Distinct predictive and indexing functions of the network are achieved via an adjustment of global recurrent gain. Our results suggest how the hippocampus may use barcodes to resolve fundamental tensions between memory specificity (pattern separation) and flexible recall (pattern completion) in general memory systems.
Collapse
Affiliation(s)
- Ching Fang
- Zuckerman Mind Brain Behavior Institute, Columbia University
| | - Jack Lindsey
- Anthropic (work conducted while at Columbia University)
| | - L.F. Abbott
- Zuckerman Mind Brain Behavior Institute, Columbia University
| | - Dmitriy Aronov
- Zuckerman Mind Brain Behavior Institute, Columbia University
- Howard Hughes Medical Institute at Columbia University
| | - Selmaan Chettih
- Zuckerman Mind Brain Behavior Institute, Columbia University
| |
Collapse
|
10
|
Székely A, Török B, Kiss M, Janacsek K, Németh D, Orbán G. Identifying Transfer Learning in the Reshaping of Inductive Biases. Open Mind (Camb) 2024; 8:1107-1128. [PMID: 39296349 PMCID: PMC11410354 DOI: 10.1162/opmi_a_00158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 07/10/2024] [Indexed: 09/21/2024] Open
Abstract
Transfer learning, the reuse of newly acquired knowledge under novel circumstances, is a critical hallmark of human intelligence that has frequently been pitted against the capacities of artificial learning agents. Yet, the computations relevant to transfer learning have been little investigated in humans. The benefit of efficient inductive biases (meta-level constraints that shape learning, often referred as priors in the Bayesian learning approach), has been both theoretically and experimentally established. Efficiency of inductive biases depends on their capacity to generalize earlier experiences. We argue that successful transfer learning upon task acquisition is ensured by updating inductive biases and transfer of knowledge hinges upon capturing the structure of the task in the inductive bias that can be reused in novel tasks. To explore this, we trained participants on a non-trivial visual stimulus sequence task (Alternating Serial Response Times, ASRT); during the Training phase, participants were exposed to one specific sequence for multiple days, then on the Transfer phase, the sequence changed, while the underlying structure of the task remained the same. Our results show that beyond the acquisition of the stimulus sequence, our participants were also able to update their inductive biases. Acquisition of the new sequence was considerably sped up by earlier exposure but this enhancement was specific to individuals showing signatures of abandoning initial inductive biases. Enhancement of learning was reflected in the development of a new internal model. Additionally, our findings highlight the ability of participants to construct an inventory of internal models and alternate between them based on environmental demands. Further, investigation of the behavior during transfer revealed that it is the subjective internal model of individuals that can predict the transfer across tasks. Our results demonstrate that even imperfect learning in a challenging environment helps learning in a new context by reusing the subjective and partial knowledge about environmental regularities.
Collapse
Affiliation(s)
- Anna Székely
- Department of Computational Sciences, HUN-REN Wigner Research Centre for Physics, Konkoly-Thege Miklós út 29-33., H-1121, Budapest, Hungary
- Department of Cognitive Science, Faculty of Natural Sciences, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | | | - Mariann Kiss
- Department of Cognitive Science, Faculty of Natural Sciences, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Karolina Janacsek
- Centre for Thinking and Learning, Institute for Lifecourse Development, School of Human Sciences, Faculty of Education, Health and Human Sciences, University of Greenwich, Greenwich, SE10 9LS United Kingdom
- Institute of Psychology, Faculty of Education and Psychology, Eötvös Loránd University, 1071 Budapest, Damjanich u. 41-43, Hungary
| | - Dezső Németh
- Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, 69500, Bron, France
- NAP Research Group, Institute of Psychology, Eötvös Loránd University & Institute of Cognitive Neuroscience and Psychology, HUN-REN Research Centre for Natural Sciences, Budapest, 1071, Hungary
- Department of Education and Psychology, Faculty of Social Sciences, University of Atlántico Medio, 35017, Las Palmas de Gran Canaria, Spain
| | - Gergő Orbán
- Department of Computational Sciences, HUN-REN Wigner Research Centre for Physics, Konkoly-Thege Miklós út 29-33., H-1121, Budapest, Hungary
| |
Collapse
|
11
|
Chen S, Cheng N, Chen X, Wang C. Integration and competition between space and time in the hippocampus. Neuron 2024:S0896-6273(24)00579-8. [PMID: 39241779 DOI: 10.1016/j.neuron.2024.08.007] [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: 03/12/2024] [Revised: 07/11/2024] [Accepted: 08/09/2024] [Indexed: 09/09/2024]
Abstract
Episodic memory is organized in both spatial and temporal contexts. The hippocampus is crucial for episodic memory and has been demonstrated to encode spatial and temporal information. However, how the representations of space and time interact in the hippocampal memory system is still unclear. Here, we recorded the activity of hippocampal CA1 neurons in mice in a variety of one-dimensional navigation tasks while systematically varying the speed of the animals. For all tasks, we found neurons simultaneously represented space and elapsed time. There was a negative correlation between the preferred space and lap duration, e.g., the preferred spatial position shifted more toward the origin when the lap duration became longer. A similar relationship between the preferred time and traveled distance was also observed. The results strongly suggest a competitive and integrated representation of space-time by single hippocampal neurons, which may provide the neural basis for spatiotemporal contexts.
Collapse
Affiliation(s)
- Shijie Chen
- Brain Research Centre, Department of Neuroscience, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Ning Cheng
- Shenzhen Key Laboratory of Precision Diagnosis and Treatment of Depression, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Xiaojing Chen
- Brain Research Centre, Department of Neuroscience, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.
| | - Cheng Wang
- Shenzhen Key Laboratory of Precision Diagnosis and Treatment of Depression, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; CAS Key Laboratory of Brain Connectome and Manipulation, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China.
| |
Collapse
|
12
|
Webb TW, Frankland SM, Altabaa A, Segert S, Krishnamurthy K, Campbell D, Russin J, Giallanza T, O'Reilly R, Lafferty J, Cohen JD. The relational bottleneck as an inductive bias for efficient abstraction. Trends Cogn Sci 2024; 28:829-843. [PMID: 38729852 DOI: 10.1016/j.tics.2024.04.001] [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: 09/11/2023] [Revised: 03/29/2024] [Accepted: 04/01/2024] [Indexed: 05/12/2024]
Abstract
A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience. This has often been framed in terms of a dichotomy between connectionist and symbolic cognitive models. Here, we highlight a recently emerging line of work that suggests a novel reconciliation of these approaches, by exploiting an inductive bias that we term the relational bottleneck. In that approach, neural networks are constrained via their architecture to focus on relations between perceptual inputs, rather than the attributes of individual inputs. We review a family of models that employ this approach to induce abstractions in a data-efficient manner, emphasizing their potential as candidate models for the acquisition of abstract concepts in the human mind and brain.
Collapse
|
13
|
Chen Y, Zhang H, Cameron M, Sejnowski T. Predictive sequence learning in the hippocampal formation. Neuron 2024; 112:2645-2658.e4. [PMID: 38917804 DOI: 10.1016/j.neuron.2024.05.024] [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: 04/23/2023] [Revised: 01/21/2024] [Accepted: 05/22/2024] [Indexed: 06/27/2024]
Abstract
The hippocampus receives sequences of sensory inputs from the cortex during exploration and encodes the sequences with millisecond precision. We developed a predictive autoencoder model of the hippocampus including the trisynaptic and monosynaptic circuits from the entorhinal cortex (EC). CA3 was trained as a self-supervised recurrent neural network to predict its next input. We confirmed that CA3 is predicting ahead by analyzing the spike coupling between simultaneously recorded neurons in the dentate gyrus, CA3, and CA1 of the mouse hippocampus. In the model, CA1 neurons signal prediction errors by comparing CA3 predictions to the next direct EC input. The model exhibits the rapid appearance and slow fading of CA1 place cells and displays replay and phase precession from CA3. The model could be learned in a biologically plausible way with error-encoding neurons. Similarities between the hippocampal and thalamocortical circuits suggest that such computation motif could also underlie self-supervised sequence learning in the cortex.
Collapse
Affiliation(s)
- Yusi Chen
- Computational Neurobiology Laboratory, Salk Institute for Biological Sciences, La Jolla, CA 92037, USA; Department of Neurobiology, University of California, San Diego, La Jolla, CA 92093, USA; Computational Neuroscience Center, University of Washington, Seattle, WA 98195, USA.
| | - Huanqiu Zhang
- Computational Neurobiology Laboratory, Salk Institute for Biological Sciences, La Jolla, CA 92037, USA; Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Mia Cameron
- Computational Neurobiology Laboratory, Salk Institute for Biological Sciences, La Jolla, CA 92037, USA; Department of Neurobiology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Terrence Sejnowski
- Computational Neurobiology Laboratory, Salk Institute for Biological Sciences, La Jolla, CA 92037, USA; Department of Neurobiology, University of California, San Diego, La Jolla, CA 92093, USA.
| |
Collapse
|
14
|
Liao Z, Losonczy A. Learning, Fast and Slow: Single- and Many-Shot Learning in the Hippocampus. Annu Rev Neurosci 2024; 47:187-209. [PMID: 38663090 DOI: 10.1146/annurev-neuro-102423-100258] [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] [Indexed: 08/09/2024]
Abstract
The hippocampus is critical for memory and spatial navigation. The ability to map novel environments, as well as more abstract conceptual relationships, is fundamental to the cognitive flexibility that humans and other animals require to survive in a dynamic world. In this review, we survey recent advances in our understanding of how this flexibility is implemented anatomically and functionally by hippocampal circuitry, during both active exploration (online) and rest (offline). We discuss the advantages and limitations of spike timing-dependent plasticity and the more recently discovered behavioral timescale synaptic plasticity in supporting distinct learning modes in the hippocampus. Finally, we suggest complementary roles for these plasticity types in explaining many-shot and single-shot learning in the hippocampus and discuss how these rules could work together to support the learning of cognitive maps.
Collapse
Affiliation(s)
- Zhenrui Liao
- Department of Neuroscience and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA;
| | - Attila Losonczy
- Department of Neuroscience and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA;
| |
Collapse
|
15
|
Dong LL, Fiete IR. Grid Cells in Cognition: Mechanisms and Function. Annu Rev Neurosci 2024; 47:345-368. [PMID: 38684081 DOI: 10.1146/annurev-neuro-101323-112047] [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] [Indexed: 05/02/2024]
Abstract
The activity patterns of grid cells form distinctively regular triangular lattices over the explored spatial environment and are largely invariant to visual stimuli, animal movement, and environment geometry. These neurons present numerous fascinating challenges to the curious (neuro)scientist: What are the circuit mechanisms responsible for creating spatially periodic activity patterns from the monotonic input-output responses of single neurons? How and why does the brain encode a local, nonperiodic variable-the allocentric position of the animal-with a periodic, nonlocal code? And, are grid cells truly specialized for spatial computations? Otherwise, what is their role in general cognition more broadly? We review efforts in uncovering the mechanisms and functional properties of grid cells, highlighting recent progress in the experimental validation of mechanistic grid cell models, and discuss the coding properties and functional advantages of the grid code as suggested by continuous attractor network models of grid cells.
Collapse
Affiliation(s)
- Ling L Dong
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA;
| | - Ila R Fiete
- McGovern Institute and K. Lisa Yang Integrative Computational Neuroscience Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA;
| |
Collapse
|
16
|
Mondal SS, Frankland S, Webb TW, Cohen JD. Determinantal point process attention over grid cell code supports out of distribution generalization. eLife 2024; 12:RP89911. [PMID: 39088258 PMCID: PMC11293867 DOI: 10.7554/elife.89911] [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] [Indexed: 08/02/2024] Open
Abstract
Deep neural networks have made tremendous gains in emulating human-like intelligence, and have been used increasingly as ways of understanding how the brain may solve the complex computational problems on which this relies. However, these still fall short of, and therefore fail to provide insight into how the brain supports strong forms of generalization of which humans are capable. One such case is out-of-distribution (OOD) generalization - successful performance on test examples that lie outside the distribution of the training set. Here, we identify properties of processing in the brain that may contribute to this ability. We describe a two-part algorithm that draws on specific features of neural computation to achieve OOD generalization, and provide a proof of concept by evaluating performance on two challenging cognitive tasks. First we draw on the fact that the mammalian brain represents metric spaces using grid cell code (e.g., in the entorhinal cortex): abstract representations of relational structure, organized in recurring motifs that cover the representational space. Second, we propose an attentional mechanism that operates over the grid cell code using determinantal point process (DPP), that we call DPP attention (DPP-A) - a transformation that ensures maximum sparseness in the coverage of that space. We show that a loss function that combines standard task-optimized error with DPP-A can exploit the recurring motifs in the grid cell code, and can be integrated with common architectures to achieve strong OOD generalization performance on analogy and arithmetic tasks. This provides both an interpretation of how the grid cell code in the mammalian brain may contribute to generalization performance, and at the same time a potential means for improving such capabilities in artificial neural networks.
Collapse
Affiliation(s)
- Shanka Subhra Mondal
- Department of Electrical and Computer Engineering, Princeton UniversityPrincetonUnited States
| | - Steven Frankland
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Taylor W Webb
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
| | - Jonathan D Cohen
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| |
Collapse
|
17
|
Huang Q, Luo H. Shared structure facilitates working memory of multiple sequences. eLife 2024; 12:RP93158. [PMID: 39046319 PMCID: PMC11268885 DOI: 10.7554/elife.93158] [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] [Indexed: 07/25/2024] Open
Abstract
Daily experiences often involve the processing of multiple sequences, yet storing them challenges the limited capacity of working memory (WM). To achieve efficient memory storage, relational structures shared by sequences would be leveraged to reorganize and compress information. Here, participants memorized a sequence of items with different colors and spatial locations and later reproduced the full color and location sequences one after another. Crucially, we manipulated the consistency between location and color sequence trajectories. First, sequences with consistent trajectories demonstrate improved memory performance and a trajectory correlation between reproduced color and location sequences. Second, sequences with consistent trajectories show neural reactivation of common trajectories, and display spontaneous replay of color sequences when recalling locations. Finally, neural reactivation correlates with WM behavior. Our findings suggest that a shared common structure is leveraged for the storage of multiple sequences through compressed encoding and neural replay, together facilitating efficient information organization in WM.
Collapse
Affiliation(s)
- Qiaoli Huang
- School of Psychological and Cognitive Sciences, Peking UniversityBeijingChina
- PKU-IDG/McGovern Institute for Brain Research, Peking UniversityBeijingChina
- Beijing Key Laboratory of Behavior and Mental Health, Peking UniversityBeijingChina
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Huan Luo
- School of Psychological and Cognitive Sciences, Peking UniversityBeijingChina
- PKU-IDG/McGovern Institute for Brain Research, Peking UniversityBeijingChina
- Beijing Key Laboratory of Behavior and Mental Health, Peking UniversityBeijingChina
| |
Collapse
|
18
|
Lippl S, Kay K, Jensen G, Ferrera VP, Abbott LF. A mathematical theory of relational generalization in transitive inference. Proc Natl Acad Sci U S A 2024; 121:e2314511121. [PMID: 38968113 PMCID: PMC11252811 DOI: 10.1073/pnas.2314511121] [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: 08/22/2023] [Accepted: 05/30/2024] [Indexed: 07/07/2024] Open
Abstract
Humans and animals routinely infer relations between different items or events and generalize these relations to novel combinations of items. This allows them to respond appropriately to radically novel circumstances and is fundamental to advanced cognition. However, how learning systems (including the brain) can implement the necessary inductive biases has been unclear. We investigated transitive inference (TI), a classic relational task paradigm in which subjects must learn a relation ([Formula: see text] and [Formula: see text]) and generalize it to new combinations of items ([Formula: see text]). Through mathematical analysis, we found that a broad range of biologically relevant learning models (e.g. gradient flow or ridge regression) perform TI successfully and recapitulate signature behavioral patterns long observed in living subjects. First, we found that models with item-wise additive representations automatically encode transitive relations. Second, for more general representations, a single scalar "conjunctivity factor" determines model behavior on TI and, further, the principle of norm minimization (a standard statistical inductive bias) enables models with fixed, partly conjunctive representations to generalize transitively. Finally, neural networks in the "rich regime," which enables representation learning and improves generalization on many tasks, unexpectedly show poor generalization and anomalous behavior on TI. We find that such networks implement a form of norm minimization (over hidden weights) that yields a local encoding mechanism lacking transitivity. Our findings show how minimal statistical learning principles give rise to a classical relational inductive bias (transitivity), explain empirically observed behaviors, and establish a formal approach to understanding the neural basis of relational abstraction.
Collapse
Affiliation(s)
- Samuel Lippl
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY10027
- Center for Theoretical Neuroscience, Department of Neuroscience, Columbia University, New York, NY10027
- Department of Neuroscience, Columbia University Medical Center, New York, NY10032
| | - Kenneth Kay
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY10027
- Center for Theoretical Neuroscience, Department of Neuroscience, Columbia University, New York, NY10027
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY10027
| | - Greg Jensen
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY10027
- Department of Neuroscience, Columbia University Medical Center, New York, NY10032
- Department of Psychology, Reed College, Portland, OR97202
| | - Vincent P. Ferrera
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY10027
- Department of Neuroscience, Columbia University Medical Center, New York, NY10032
- Department of Psychiatry, Columbia University Medical Center, New York, NY10032
| | - L. F. Abbott
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY10027
- Center for Theoretical Neuroscience, Department of Neuroscience, Columbia University, New York, NY10027
- Department of Neuroscience, Columbia University Medical Center, New York, NY10032
| |
Collapse
|
19
|
Lee DG, McLachlan CA, Nogueira R, Kwon O, Carey AE, House G, Lagani GD, LaMay D, Fusi S, Chen JL. Perirhinal cortex learns a predictive map of the task environment. Nat Commun 2024; 15:5544. [PMID: 38956015 PMCID: PMC11219840 DOI: 10.1038/s41467-024-47365-7] [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: 08/09/2023] [Accepted: 03/25/2024] [Indexed: 07/04/2024] Open
Abstract
Goal-directed tasks involve acquiring an internal model, known as a predictive map, of relevant stimuli and associated outcomes to guide behavior. Here, we identified neural signatures of a predictive map of task behavior in perirhinal cortex (Prh). Mice learned to perform a tactile working memory task by classifying sequential whisker stimuli over multiple training stages. Chronic two-photon calcium imaging, population analysis, and computational modeling revealed that Prh encodes stimulus features as sensory prediction errors. Prh forms stable stimulus-outcome associations that can progressively be decoded earlier in the trial as training advances and that generalize as animals learn new contingencies. Stimulus-outcome associations are linked to prospective network activity encoding possible expected outcomes. This link is mediated by cholinergic signaling to guide task performance, demonstrated by acetylcholine imaging and systemic pharmacological perturbation. We propose that Prh combines error-driven and map-like properties to acquire a predictive map of learned task behavior.
Collapse
Affiliation(s)
- David G Lee
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
- Center for Neurophotonics, Boston University, Boston, MA, 02215, USA
| | - Caroline A McLachlan
- Center for Neurophotonics, Boston University, Boston, MA, 02215, USA
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Ramon Nogueira
- Center for Theoretical Neuroscience, Columbia University, New York, NY, 10027, USA
- Department of Neuroscience, Columbia University, New York, NY, 10027, USA
| | - Osung Kwon
- Center for Neurophotonics, Boston University, Boston, MA, 02215, USA
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Alanna E Carey
- Center for Neurophotonics, Boston University, Boston, MA, 02215, USA
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Garrett House
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Gavin D Lagani
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Danielle LaMay
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Stefano Fusi
- Center for Theoretical Neuroscience, Columbia University, New York, NY, 10027, USA
- Department of Neuroscience, Columbia University, New York, NY, 10027, USA
| | - Jerry L Chen
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA.
- Center for Neurophotonics, Boston University, Boston, MA, 02215, USA.
- Department of Biology, Boston University, Boston, MA, 02215, USA.
- Center for Systems Neuroscience, Boston University, Boston, MA, 02215, USA.
| |
Collapse
|
20
|
McNaughton N, Bannerman D. The homogenous hippocampus: How hippocampal cells process available and potential goals. Prog Neurobiol 2024; 240:102653. [PMID: 38960002 DOI: 10.1016/j.pneurobio.2024.102653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 04/25/2024] [Accepted: 06/24/2024] [Indexed: 07/05/2024]
Abstract
We present here a view of the firing patterns of hippocampal cells that is contrary, both functionally and anatomically, to conventional wisdom. We argue that the hippocampus responds to efference copies of goals encoded elsewhere; and that it uses these to detect and resolve conflict or interference between goals in general. While goals can involve space, hippocampal cells do not encode spatial (or other special types of) memory, as such. We also argue that the transverse circuits of the hippocampus operate in an essentially homogeneous way along its length. The apparently different functions of different parts (e.g. memory retrieval versus anxiety) result from the different (situational/motivational) inputs on which those parts perform the same fundamental computational operations. On this view, the key role of the hippocampus is the iterative adjustment, via Papez-like circuits, of synaptic weights in cell assemblies elsewhere.
Collapse
Affiliation(s)
- Neil McNaughton
- Department of Psychology and Brain Health Research Centre, University of Otago, POB56, Dunedin 9054, New Zealand.
| | - David Bannerman
- Department of Experimental Psychology, University of Oxford, South Parks Road, Oxford, England, UK
| |
Collapse
|
21
|
Rao RPN. A sensory-motor theory of the neocortex. Nat Neurosci 2024; 27:1221-1235. [PMID: 38937581 DOI: 10.1038/s41593-024-01673-9] [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: 06/21/2023] [Accepted: 04/26/2024] [Indexed: 06/29/2024]
Abstract
Recent neurophysiological and neuroanatomical studies suggest a close interaction between sensory and motor processes across the neocortex. Here, I propose that the neocortex implements active predictive coding (APC): each cortical area estimates both latent sensory states and actions (including potentially abstract actions internal to the cortex), and the cortex as a whole predicts the consequences of actions at multiple hierarchical levels. Feedback from higher areas modulates the dynamics of state and action networks in lower areas. I show how the same APC architecture can explain (1) how we recognize an object and its parts using eye movements, (2) why perception seems stable despite eye movements, (3) how we learn compositional representations, for example, part-whole hierarchies, (4) how complex actions can be planned using simpler actions, and (5) how we form episodic memories of sensory-motor experiences and learn abstract concepts such as a family tree. I postulate a mapping of the APC model to the laminar architecture of the cortex and suggest possible roles for cortico-cortical and cortico-subcortical pathways.
Collapse
Affiliation(s)
- Rajesh P N Rao
- Center for Neurotechnology, University of Washington, Seattle, WA, USA.
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
| |
Collapse
|
22
|
Pecirno SA, Keinath AT. The neuroscience of turning heads. Nat Hum Behav 2024; 8:1243-1244. [PMID: 38877288 DOI: 10.1038/s41562-024-01920-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2024]
Affiliation(s)
- Sergio A Pecirno
- Department of Psychology, University of Illinois Chicago, Chicago, IL, USA
| | | |
Collapse
|
23
|
Kymn CJ, Mazelet S, Thomas A, Kleyko D, Frady EP, Sommer FT, Olshausen BA. Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps. ARXIV 2024:arXiv:2406.18808v1. [PMID: 38979486 PMCID: PMC11230348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
We propose a normative model for spatial representation in the hippocampal formation that combines optimality principles, such as maximizing coding range and spatial information per neuron, with an algebraic framework for computing in distributed representation. Spatial position is encoded in a residue number system, with individual residues represented by high-dimensional, complex-valued vectors. These are composed into a single vector representing position by a similarity-preserving, conjunctive vector-binding operation. Self-consistency between the representations of the overall position and of the individual residues is enforced by a modular attractor network whose modules correspond to the grid cell modules in entorhinal cortex. The vector binding operation can also associate different contexts to spatial representations, yielding a model for entorhinal cortex and hippocampus. We show that the model achieves normative desiderata including superlinear scaling of patterns with dimension, robust error correction, and hexagonal, carry-free encoding of spatial position. These properties in turn enable robust path integration and association with sensory inputs. More generally, the model formalizes how compositional computations could occur in the hippocampal formation and leads to testable experimental predictions.
Collapse
Affiliation(s)
| | - Sonia Mazelet
- Redwood Center for Theoretical Neuroscience, UC Berkeley, Berkeley, USA
- Université Paris-Saclay, ENS Paris-Saclay, Gif-sur-Yvette, France
| | - Anthony Thomas
- Redwood Center for Theoretical Neuroscience, UC Berkeley, Berkeley, USA
| | - Denis Kleyko
- Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden
| | | | - Friedrich T Sommer
- Redwood Center for Theoretical Neuroscience, UC Berkeley, Berkeley, USA
- Intel Labs, Santa Clara, USA
| | - Bruno A Olshausen
- Redwood Center for Theoretical Neuroscience, UC Berkeley, Berkeley, USA
| |
Collapse
|
24
|
Breffle J, Germaine H, Shin JD, Jadhav SP, Miller P. Intrinsic dynamics of randomly clustered networks generate place fields and preplay of novel environments. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.26.564173. [PMID: 37961479 PMCID: PMC10634993 DOI: 10.1101/2023.10.26.564173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
During both sleep and awake immobility, hippocampal place cells reactivate time-compressed versions of sequences representing recently experienced trajectories in a phenomenon known as replay. Intriguingly, spontaneous sequences can also correspond to forthcoming trajectories in novel environments experienced later, in a phenomenon known as preplay. Here, we present a model showing that sequences of spikes correlated with the place fields underlying spatial trajectories in both previously experienced and future novel environments can arise spontaneously in neural circuits with random, clustered connectivity rather than pre-configured spatial maps. Moreover, the realistic place fields themselves arise in the circuit from minimal, landmark-based inputs. We find that preplay quality depends on the network's balance of cluster isolation and overlap, with optimal preplay occurring in small-world regimes of high clustering yet short path lengths. We validate the results of our model by applying the same place field and preplay analyses to previously published rat hippocampal place cell data. Our results show that clustered recurrent connectivity can generate spontaneous preplay and immediate replay of novel environments. These findings support a framework whereby novel sensory experiences become associated with preexisting "pluripotent" internal neural activity patterns.
Collapse
Affiliation(s)
- Jordan Breffle
- Neuroscience Program, Brandeis University, 415 South St., Waltham, MA 02454
| | - Hannah Germaine
- Neuroscience Program, Brandeis University, 415 South St., Waltham, MA 02454
| | - Justin D Shin
- Neuroscience Program, Brandeis University, 415 South St., Waltham, MA 02454
- Volen National Center for Complex Systems, Brandeis University, 415 South St., Waltham, MA 02454
- Department of Psychology, Brandeis University, 415 South St., Waltham, MA 02454
| | - Shantanu P Jadhav
- Neuroscience Program, Brandeis University, 415 South St., Waltham, MA 02454
- Volen National Center for Complex Systems, Brandeis University, 415 South St., Waltham, MA 02454
- Department of Psychology, Brandeis University, 415 South St., Waltham, MA 02454
| | - Paul Miller
- Neuroscience Program, Brandeis University, 415 South St., Waltham, MA 02454
- Volen National Center for Complex Systems, Brandeis University, 415 South St., Waltham, MA 02454
- Department of Biology, Brandeis University, 415 South St., Waltham, MA 02454
| |
Collapse
|
25
|
Peters-Founshtein G, Dafni-Merom A, Monsa R, Arzy S. Evidence for grid-cell-like activity in the time domain. Neuropsychologia 2024; 198:108878. [PMID: 38574806 DOI: 10.1016/j.neuropsychologia.2024.108878] [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: 07/07/2023] [Revised: 03/22/2024] [Accepted: 03/26/2024] [Indexed: 04/06/2024]
Abstract
The relation between the processing of space and time in the brain has been an enduring cross-disciplinary question. Grid cells have been recognized as a hallmark of the mammalian navigation system, with recent studies attesting to their involvement in the organization of conceptual knowledge in humans. To determine whether grid-cell-like representations support temporal processing, we asked subjects to mentally simulate changes in age and time-of-day, each constituting "trajectory" in an age-day space, while undergoing fMRI. We found that grid-cell-like representations supported trajecting across this age-day space. Furthermore, brain regions concurrently coding past-to-future orientation positively modulated the magnitude of grid-cell-like representation in the left entorhinal cortex. Finally, our findings suggest that temporal processing may be supported by spatially modulated systems, and that innate regularities of abstract domains may interface and alter grid-cell-like representations, similarly to spatial geometry.
Collapse
Affiliation(s)
- Gregory Peters-Founshtein
- The Computational Neuropsychiatry Lab, Department of Medical Neurobiology, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel; Department of Nuclear Medicine, Sheba Medical Center, Ramat-Gan, Israel.
| | - Amnon Dafni-Merom
- The Computational Neuropsychiatry Lab, Department of Medical Neurobiology, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Rotem Monsa
- The Computational Neuropsychiatry Lab, Department of Medical Neurobiology, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Shahar Arzy
- The Computational Neuropsychiatry Lab, Department of Medical Neurobiology, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel; Department of Neurology, Hadassah Hebrew University Medical School, Jerusalem, Israel
| |
Collapse
|
26
|
Neupane S, Fiete I, Jazayeri M. Mental navigation in the primate entorhinal cortex. Nature 2024; 630:704-711. [PMID: 38867051 PMCID: PMC11224022 DOI: 10.1038/s41586-024-07557-z] [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: 12/13/2022] [Accepted: 05/10/2024] [Indexed: 06/14/2024]
Abstract
A cognitive map is a suitably structured representation that enables novel computations using previous experience; for example, planning a new route in a familiar space1. Work in mammals has found direct evidence for such representations in the presence of exogenous sensory inputs in both spatial2,3 and non-spatial domains4-10. Here we tested a foundational postulate of the original cognitive map theory1,11: that cognitive maps support endogenous computations without external input. We recorded from the entorhinal cortex of monkeys in a mental navigation task that required the monkeys to use a joystick to produce one-dimensional vectors between pairs of visual landmarks without seeing the intermediate landmarks. The ability of the monkeys to perform the task and generalize to new pairs indicated that they relied on a structured representation of the landmarks. Task-modulated neurons exhibited periodicity and ramping that matched the temporal structure of the landmarks and showed signatures of continuous attractor networks12,13. A continuous attractor network model of path integration14 augmented with a Hebbian-like learning mechanism provided an explanation of how the system could endogenously recall landmarks. The model also made an unexpected prediction that endogenous landmarks transiently slow path integration, reset the dynamics and thereby reduce variability. This prediction was borne out in a reanalysis of firing rate variability and behaviour. Our findings link the structured patterns of activity in the entorhinal cortex to the endogenous recruitment of a cognitive map during mental navigation.
Collapse
Affiliation(s)
- Sujaya Neupane
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ila Fiete
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mehrdad Jazayeri
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
| |
Collapse
|
27
|
Fenton AA. Remapping revisited: how the hippocampus represents different spaces. Nat Rev Neurosci 2024; 25:428-448. [PMID: 38714834 DOI: 10.1038/s41583-024-00817-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2024] [Indexed: 05/25/2024]
Abstract
The representation of distinct spaces by hippocampal place cells has been linked to changes in their place fields (the locations in the environment where the place cells discharge strongly), a phenomenon that has been termed 'remapping'. Remapping has been assumed to be accompanied by the reorganization of subsecond cofiring relationships among the place cells, potentially maximizing hippocampal information coding capacity. However, several observations challenge this standard view. For example, place cells exhibit mixed selectivity, encode non-positional variables, can have multiple place fields and exhibit unreliable discharge in fixed environments. Furthermore, recent evidence suggests that, when measured at subsecond timescales, the moment-to-moment cofiring of a pair of cells in one environment is remarkably similar in another environment, despite remapping. Here, I propose that remapping is a misnomer for the changes in place fields across environments and suggest instead that internally organized manifold representations of hippocampal activity are actively registered to different environments to enable navigation, promote memory and organize knowledge.
Collapse
Affiliation(s)
- André A Fenton
- Center for Neural Science, New York University, New York, NY, USA.
- Neuroscience Institute at the NYU Langone Medical Center, New York, NY, USA.
| |
Collapse
|
28
|
Qiu Y, Li H, Liao J, Chen K, Wu X, Liu B, Huang R. Forming cognitive maps for abstract spaces: the roles of the human hippocampus and orbitofrontal cortex. Commun Biol 2024; 7:517. [PMID: 38693344 PMCID: PMC11063219 DOI: 10.1038/s42003-024-06214-5] [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: 06/09/2023] [Accepted: 04/18/2024] [Indexed: 05/03/2024] Open
Abstract
How does the human brain construct cognitive maps for decision-making and inference? Here, we conduct an fMRI study on a navigation task in multidimensional abstract spaces. Using a deep neural network model, we assess learning levels and categorized paths into exploration and exploitation stages. Univariate analyses show higher activation in the bilateral hippocampus and lateral prefrontal cortex during exploration, positively associated with learning level and response accuracy. Conversely, the bilateral orbitofrontal cortex (OFC) and retrosplenial cortex show higher activation during exploitation, negatively associated with learning level and response accuracy. Representational similarity analysis show that the hippocampus, entorhinal cortex, and OFC more accurately represent destinations in exploitation than exploration stages. These findings highlight the collaboration between the medial temporal lobe and prefrontal cortex in learning abstract space structures. The hippocampus may be involved in spatial memory formation and representation, while the OFC integrates sensory information for decision-making in multidimensional abstract spaces.
Collapse
Affiliation(s)
- Yidan Qiu
- School of Psychology; Center for the Study of Applied Psychology; Key Laboratory of Mental Health and Cognitive Science of Guangdong Province; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; South China Normal University, Guangzhou, 510631, China
| | - Huakang Li
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Jiajun Liao
- School of Psychology; Center for the Study of Applied Psychology; Key Laboratory of Mental Health and Cognitive Science of Guangdong Province; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; South China Normal University, Guangzhou, 510631, China
| | - Kemeng Chen
- School of Psychology; Center for the Study of Applied Psychology; Key Laboratory of Mental Health and Cognitive Science of Guangdong Province; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; South China Normal University, Guangzhou, 510631, China
| | - Xiaoyan Wu
- School of Psychology; Center for the Study of Applied Psychology; Key Laboratory of Mental Health and Cognitive Science of Guangdong Province; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; South China Normal University, Guangzhou, 510631, China
| | - Bingyi Liu
- School of Psychology; Center for the Study of Applied Psychology; Key Laboratory of Mental Health and Cognitive Science of Guangdong Province; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; South China Normal University, Guangzhou, 510631, China
| | - Ruiwang Huang
- School of Psychology; Center for the Study of Applied Psychology; Key Laboratory of Mental Health and Cognitive Science of Guangdong Province; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; South China Normal University, Guangzhou, 510631, China.
| |
Collapse
|
29
|
Boyle LM, Posani L, Irfan S, Siegelbaum SA, Fusi S. Tuned geometries of hippocampal representations meet the computational demands of social memory. Neuron 2024; 112:1358-1371.e9. [PMID: 38382521 PMCID: PMC11186585 DOI: 10.1016/j.neuron.2024.01.021] [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: 06/16/2023] [Revised: 11/03/2023] [Accepted: 01/19/2024] [Indexed: 02/23/2024]
Abstract
Social memory consists of two processes: the detection of familiar compared with novel conspecifics and the detailed recollection of past social episodes. We investigated the neural bases for these processes using calcium imaging of dorsal CA2 hippocampal pyramidal neurons, known to be important for social memory, during social/spatial encounters with novel conspecifics and familiar littermates. Whereas novel individuals were represented in a low-dimensional geometry that allows for generalization of social identity across different spatial locations and of location across different identities, littermates were represented in a higher-dimensional geometry that supports high-capacity memory storage. Moreover, familiarity was represented in an abstract format, independent of individual identity. The degree to which familiarity increased the dimensionality of CA2 representations for individual mice predicted their performance in a social novelty recognition memory test. Thus, by tuning the geometry of structured neural activity, CA2 is able to meet the demands of distinct social memory processes.
Collapse
Affiliation(s)
- Lara M Boyle
- Department of Neuroscience, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10027, USA
| | - Lorenzo Posani
- Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | | | - Steven A Siegelbaum
- Department of Neuroscience, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Pharmacology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10032, USA; Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA.
| | - Stefano Fusi
- Department of Neuroscience, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA.
| |
Collapse
|
30
|
Zeng YF, Yang KX, Cui Y, Zhu XN, Li R, Zhang H, Wu DC, Stevens RC, Hu J, Zhou N. Conjunctive encoding of exploratory intentions and spatial information in the hippocampus. Nat Commun 2024; 15:3221. [PMID: 38622129 PMCID: PMC11018604 DOI: 10.1038/s41467-024-47570-4] [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: 05/02/2023] [Accepted: 04/02/2024] [Indexed: 04/17/2024] Open
Abstract
The hippocampus creates a cognitive map of the external environment by encoding spatial and self-motion-related information. However, it is unclear whether hippocampal neurons could also incorporate internal cognitive states reflecting an animal's exploratory intention, which is not driven by rewards or unexpected sensory stimuli. In this study, a subgroup of CA1 neurons was found to encode both spatial information and animals' investigatory intentions in male mice. These neurons became active before the initiation of exploration behaviors at specific locations and were nearly silent when the same fields were traversed without exploration. Interestingly, this neuronal activity could not be explained by object features, rewards, or mismatches in environmental cues. Inhibition of the lateral entorhinal cortex decreased the activity of these cells during exploration. Our findings demonstrate that hippocampal neurons may bridge external and internal signals, indicating a potential connection between spatial representation and intentional states in the construction of internal navigation systems.
Collapse
Affiliation(s)
- Yi-Fan Zeng
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Ke-Xin Yang
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Yilong Cui
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Xiao-Na Zhu
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Rui Li
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Hanqing Zhang
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
| | - Dong Chuan Wu
- Neuroscience and Brain Disease Center, Graduate Institute of Biomedical Sciences, China Medical University, Taichung City, 404333, Taiwan
- Translational Medicine Research Center, China Medical University Hospital, Taichung City, 404333, Taiwan
| | - Raymond C Stevens
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Ji Hu
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Ning Zhou
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China.
| |
Collapse
|
31
|
Pesnot Lerousseau J, Summerfield C. Space as a scaffold for rotational generalisation of abstract concepts. eLife 2024; 13:RP93636. [PMID: 38568075 PMCID: PMC10990485 DOI: 10.7554/elife.93636] [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] [Indexed: 04/05/2024] Open
Abstract
Learning invariances allows us to generalise. In the visual modality, invariant representations allow us to recognise objects despite translations or rotations in physical space. However, how we learn the invariances that allow us to generalise abstract patterns of sensory data ('concepts') is a longstanding puzzle. Here, we study how humans generalise relational patterns in stimulation sequences that are defined by either transitions on a nonspatial two-dimensional feature manifold, or by transitions in physical space. We measure rotational generalisation, i.e., the ability to recognise concepts even when their corresponding transition vectors are rotated. We find that humans naturally generalise to rotated exemplars when stimuli are defined in physical space, but not when they are defined as positions on a nonspatial feature manifold. However, if participants are first pre-trained to map auditory or visual features to spatial locations, then rotational generalisation becomes possible even in nonspatial domains. These results imply that space acts as a scaffold for learning more abstract conceptual invariances.
Collapse
|
32
|
Lippl S, Kay K, Jensen G, Ferrera VP, Abbott L. A mathematical theory of relational generalization in transitive inference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.22.554287. [PMID: 37662223 PMCID: PMC10473627 DOI: 10.1101/2023.08.22.554287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Humans and animals routinely infer relations between different items or events and generalize these relations to novel combinations of items. This allows them to respond appropriately to radically novel circumstances and is fundamental to advanced cognition. However, how learning systems (including the brain) can implement the necessary inductive biases has been unclear. Here we investigated transitive inference (TI), a classic relational task paradigm in which subjects must learn a relation (A > B and B > C) and generalize it to new combinations of items (A > C). Through mathematical analysis, we found that a broad range of biologically relevant learning models (e.g. gradient flow or ridge regression) perform TI successfully and recapitulate signature behavioral patterns long observed in living subjects. First, we found that models with item-wise additive representations automatically encode transitive relations. Second, for more general representations, a single scalar "conjunctivity factor" determines model behavior on TI and, further, the principle of norm minimization (a standard statistical inductive bias) enables models with fixed, partly conjunctive representations to generalize transitively. Finally, neural networks in the "rich regime," which enables representation learning and has been found to improve generalization, unexpectedly show poor generalization and anomalous behavior. We find that such networks implement a form of norm minimization (over hidden weights) that yields a local encoding mechanism lacking transitivity. Our findings show how minimal statistical learning principles give rise to a classical relational inductive bias (transitivity), explain empirically observed behaviors, and establish a formal approach to understanding the neural basis of relational abstraction.
Collapse
Affiliation(s)
- Samuel Lippl
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, NY
- Center for Theoretical Neuroscience, Columbia University, NY
- Department of Neuroscience, Columbia University Medical Center, NY
| | - Kenneth Kay
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, NY
- Center for Theoretical Neuroscience, Columbia University, NY
- Grossman Center for the Statistics of Mind, Columbia University, NY
| | - Greg Jensen
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, NY
- Department of Neuroscience, Columbia University Medical Center, NY
- Department of Psychology at Reed College, OR
| | - Vincent P. Ferrera
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, NY
- Department of Neuroscience, Columbia University Medical Center, NY
- Department of Psychiatry, Columbia University Medical Center, NY
| | - L.F. Abbott
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, NY
- Center for Theoretical Neuroscience, Columbia University, NY
- Department of Neuroscience, Columbia University Medical Center, NY
| |
Collapse
|
33
|
Kay K, Biderman N, Khajeh R, Beiran M, Cueva CJ, Shohamy D, Jensen G, Wei XX, Ferrera VP, Abbott LF. Emergent neural dynamics and geometry for generalization in a transitive inference task. PLoS Comput Biol 2024; 20:e1011954. [PMID: 38662797 PMCID: PMC11125559 DOI: 10.1371/journal.pcbi.1011954] [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: 07/26/2023] [Revised: 05/24/2024] [Accepted: 02/28/2024] [Indexed: 05/25/2024] Open
Abstract
Relational cognition-the ability to infer relationships that generalize to novel combinations of objects-is fundamental to human and animal intelligence. Despite this importance, it remains unclear how relational cognition is implemented in the brain due in part to a lack of hypotheses and predictions at the levels of collective neural activity and behavior. Here we discovered, analyzed, and experimentally tested neural networks (NNs) that perform transitive inference (TI), a classic relational task (if A > B and B > C, then A > C). We found NNs that (i) generalized perfectly, despite lacking overt transitive structure prior to training, (ii) generalized when the task required working memory (WM), a capacity thought to be essential to inference in the brain, (iii) emergently expressed behaviors long observed in living subjects, in addition to a novel order-dependent behavior, and (iv) expressed different task solutions yielding alternative behavioral and neural predictions. Further, in a large-scale experiment, we found that human subjects performing WM-based TI showed behavior inconsistent with a class of NNs that characteristically expressed an intuitive task solution. These findings provide neural insights into a classical relational ability, with wider implications for how the brain realizes relational cognition.
Collapse
Affiliation(s)
- Kenneth Kay
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
| | - Natalie Biderman
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Psychology, Columbia University, New York, New York, United States of America
| | - Ramin Khajeh
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
| | - Manuel Beiran
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
| | - Christopher J. Cueva
- Department of Brain and Cognitive Sciences, MIT, Cambridge, Massachusetts, United States of America
| | - Daphna Shohamy
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Psychology, Columbia University, New York, New York, United States of America
- The Kavli Institute for Brain Science, Columbia University, New York, New York, United States of America
| | - Greg Jensen
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University Medical Center, New York, New York, United States of America
- Department of Psychology at Reed College, Portland, Oregon, United States of America
| | - Xue-Xin Wei
- Departments of Neuroscience and Psychology, The University of Texas at Austin, Austin, Texas, United States of America
| | - Vincent P. Ferrera
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University Medical Center, New York, New York, United States of America
- Department of Psychiatry, Columbia University Medical Center, New York, New York, United States of America
| | - LF Abbott
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- The Kavli Institute for Brain Science, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University Medical Center, New York, New York, United States of America
| |
Collapse
|
34
|
McNamee DC. The generative neural microdynamics of cognitive processing. Curr Opin Neurobiol 2024; 85:102855. [PMID: 38428170 DOI: 10.1016/j.conb.2024.102855] [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: 07/07/2023] [Revised: 02/06/2024] [Accepted: 02/07/2024] [Indexed: 03/03/2024]
Abstract
The entorhinal cortex and hippocampus form a recurrent network that informs many cognitive processes, including memory, planning, navigation, and imagination. Neural recordings from these regions reveal spatially organized population codes corresponding to external environments and abstract spaces. Aligning the former cognitive functionalities with the latter neural phenomena is a central challenge in understanding the entorhinal-hippocampal circuit (EHC). Disparate experiments demonstrate a surprising level of complexity and apparent disorder in the intricate spatiotemporal dynamics of sequential non-local hippocampal reactivations, which occur particularly, though not exclusively, during immobile pauses and rest. We review these phenomena with a particular focus on their apparent lack of physical simulative realism. These observations are then integrated within a theoretical framework and proposed neural circuit mechanisms that normatively characterize this neural complexity by conceiving different regimes of hippocampal microdynamics as neuromarkers of diverse cognitive computations.
Collapse
|
35
|
Desbordes T, King JR, Dehaene S. Tracking the neural codes for words and phrases during semantic composition, working-memory storage, and retrieval. Cell Rep 2024; 43:113847. [PMID: 38412098 DOI: 10.1016/j.celrep.2024.113847] [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: 03/20/2023] [Revised: 11/02/2023] [Accepted: 02/07/2024] [Indexed: 02/29/2024] Open
Abstract
The ability to compose successive words into a meaningful phrase is a characteristic feature of human cognition, yet its neural mechanisms remain incompletely understood. Here, we analyze the cortical mechanisms of semantic composition using magnetoencephalography (MEG) while participants read one-word, two-word, and five-word noun phrases and compared them with a subsequent image. Decoding of MEG signals revealed three processing stages. During phrase comprehension, the representation of individual words was sustained for a variable duration depending on phrasal context. During the delay period, the word code was replaced by a working-memory code whose activation increased with semantic complexity. Finally, the speed and accuracy of retrieval depended on semantic complexity and was faster for surface than for deep semantic properties. In conclusion, we propose that the brain initially encodes phrases using factorized dimensions for successive words but later compresses them in working memory and requires a period of decompression to access them.
Collapse
Affiliation(s)
- Théo Desbordes
- Meta AI, Paris, France; Cognitive Neuroimaging Unit, NeuroSpin Center, 91191 Gif-sur-Yvette, France.
| | - Jean-Rémi King
- Meta AI, Paris, France; École Normale Supérieure, PSL University, Paris, France
| | - Stanislas Dehaene
- Université Paris Saclay, INSERM, CEA, Cognitive Neuroimaging Unit, NeuroSpin Center, 91191 Gif-sur-Yvette, France; Collège de France, PSL University, Paris, France
| |
Collapse
|
36
|
Tafazoli S, Bouchacourt FM, Ardalan A, Markov NT, Uchimura M, Mattar MG, Daw ND, Buschman TJ. Building compositional tasks with shared neural subspaces. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.31.578263. [PMID: 38352540 PMCID: PMC10862921 DOI: 10.1101/2024.01.31.578263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Cognition is remarkably flexible; we are able to rapidly learn and perform many different tasks1. Theoretical modeling has shown artificial neural networks trained to perform multiple tasks will re-use representations2 and computational components3 across tasks. By composing tasks from these sub-components, an agent can flexibly switch between tasks and rapidly learn new tasks4. Yet, whether such compositionality is found in the brain is unknown. Here, we show the same subspaces of neural activity represent task-relevant information across multiple tasks, with each task compositionally combining these subspaces in a task-specific manner. We trained monkeys to switch between three compositionally related tasks. Neural recordings found task-relevant information about stimulus features and motor actions were represented in subspaces of neural activity that were shared across tasks. When monkeys performed a task, neural representations in the relevant shared sensory subspace were transformed to the relevant shared motor subspace. Subspaces were flexibly engaged as monkeys discovered the task in effect; their internal belief about the current task predicted the strength of representations in task-relevant subspaces. In sum, our findings suggest that the brain can flexibly perform multiple tasks by compositionally combining task-relevant neural representations across tasks.
Collapse
Affiliation(s)
- Sina Tafazoli
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Adel Ardalan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Nikola T. Markov
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Motoaki Uchimura
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Nathaniel D. Daw
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Timothy J. Buschman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Department of Psychology, Princeton University, Princeton, NJ, USA
| |
Collapse
|
37
|
Stöckl C, Yang Y, Maass W. Local prediction-learning in high-dimensional spaces enables neural networks to plan. Nat Commun 2024; 15:2344. [PMID: 38490999 PMCID: PMC10943103 DOI: 10.1038/s41467-024-46586-0] [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: 07/25/2023] [Accepted: 03/01/2024] [Indexed: 03/18/2024] Open
Abstract
Planning and problem solving are cornerstones of higher brain function. But we do not know how the brain does that. We show that learning of a suitable cognitive map of the problem space suffices. Furthermore, this can be reduced to learning to predict the next observation through local synaptic plasticity. Importantly, the resulting cognitive map encodes relations between actions and observations, and its emergent high-dimensional geometry provides a sense of direction for reaching distant goals. This quasi-Euclidean sense of direction provides a simple heuristic for online planning that works almost as well as the best offline planning algorithms from AI. If the problem space is a physical space, this method automatically extracts structural regularities from the sequence of observations that it receives so that it can generalize to unseen parts. This speeds up learning of navigation in 2D mazes and the locomotion with complex actuator systems, such as legged bodies. The cognitive map learner that we propose does not require a teacher, similar to self-attention networks (Transformers). But in contrast to Transformers, it does not require backpropagation of errors or very large datasets for learning. Hence it provides a blue-print for future energy-efficient neuromorphic hardware that acquires advanced cognitive capabilities through autonomous on-chip learning.
Collapse
Affiliation(s)
- Christoph Stöckl
- Institute of Theoretical Computer Science, Graz University of Technology, 8010, Graz, Austria
| | - Yukun Yang
- Institute of Theoretical Computer Science, Graz University of Technology, 8010, Graz, Austria
| | - Wolfgang Maass
- Institute of Theoretical Computer Science, Graz University of Technology, 8010, Graz, Austria.
| |
Collapse
|
38
|
Qu C, Huang Y, Philippe R, Cai S, Derrington E, Moisan F, Shi M, Dreher JC. Transcranial direct current stimulation suggests a causal role of the medial prefrontal cortex in learning social hierarchy. Commun Biol 2024; 7:304. [PMID: 38461216 PMCID: PMC10924847 DOI: 10.1038/s42003-024-05976-2] [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: 11/02/2022] [Accepted: 02/27/2024] [Indexed: 03/11/2024] Open
Abstract
Social hierarchies can be inferred through observational learning of social relationships between individuals. Yet, little is known about the causal role of specific brain regions in learning hierarchies. Here, using transcranial direct current stimulation, we show a causal role of the medial prefrontal cortex (mPFC) in learning social versus non-social hierarchies. In a Training phase, participants acquired knowledge about social and non-social hierarchies by trial and error. During a Test phase, they were presented with two items from hierarchies that were never encountered together, requiring them to make transitive inferences. Anodal stimulation over mPFC impaired social compared with non-social hierarchy learning, and this modulation was influenced by the relative social rank of the members (higher or lower status). Anodal stimulation also impaired transitive inference making, but only during early blocks before learning was established. Together, these findings demonstrate a causal role of the mPFC in learning social ranks by observation.
Collapse
Affiliation(s)
- Chen Qu
- Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Yulong Huang
- Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
- Department of Experimental Psychology, Ghent University, Ghent, Belgium
| | - Rémi Philippe
- Laboratory of Neuroeconomics, Institut des Sciences Cognitives Marc Jeannerod, CNRS, Lyon, France
- Université Claude Bernard Lyon 1, Lyon, France
| | - Shenggang Cai
- School of Economics and Management, South China Normal University, Guangzhou, China
- Key Lab for Behavioral Economic Science & Technology, South China Normal University, Guangzhou, China
| | - Edmund Derrington
- Laboratory of Neuroeconomics, Institut des Sciences Cognitives Marc Jeannerod, CNRS, Lyon, France
- Université Claude Bernard Lyon 1, Lyon, France
| | | | - Mengke Shi
- Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Jean-Claude Dreher
- Laboratory of Neuroeconomics, Institut des Sciences Cognitives Marc Jeannerod, CNRS, Lyon, France.
- Université Claude Bernard Lyon 1, Lyon, France.
| |
Collapse
|
39
|
Spens E, Burgess N. A generative model of memory construction and consolidation. Nat Hum Behav 2024; 8:526-543. [PMID: 38242925 PMCID: PMC10963272 DOI: 10.1038/s41562-023-01799-z] [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: 05/30/2023] [Accepted: 12/05/2023] [Indexed: 01/21/2024]
Abstract
Episodic memories are (re)constructed, share neural substrates with imagination, combine unique features with schema-based predictions and show schema-based distortions that increase with consolidation. Here we present a computational model in which hippocampal replay (from an autoassociative network) trains generative models (variational autoencoders) to (re)create sensory experiences from latent variable representations in entorhinal, medial prefrontal and anterolateral temporal cortices via the hippocampal formation. Simulations show effects of memory age and hippocampal lesions in agreement with previous models, but also provide mechanisms for semantic memory, imagination, episodic future thinking, relational inference and schema-based distortions including boundary extension. The model explains how unique sensory and predictable conceptual elements of memories are stored and reconstructed by efficiently combining both hippocampal and neocortical systems, optimizing the use of limited hippocampal storage for new and unusual information. Overall, we believe hippocampal replay training generative models provides a comprehensive account of memory construction, imagination and consolidation.
Collapse
Affiliation(s)
- Eleanor Spens
- UCL Institute of Cognitive Neuroscience, University College London, London, UK.
| | - Neil Burgess
- UCL Institute of Cognitive Neuroscience, University College London, London, UK.
- UCL Queen Square Institute of Neurology, University College London, London, UK.
| |
Collapse
|
40
|
Gonzalez A, Giocomo LM. Parahippocampal neurons encode task-relevant information for goal-directed navigation. eLife 2024; 12:RP85646. [PMID: 38363198 PMCID: PMC10942598 DOI: 10.7554/elife.85646] [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] [Indexed: 02/17/2024] Open
Abstract
A behavioral strategy crucial to survival is directed navigation to a goal, such as a food or home location. One potential neural substrate for supporting goal-directed navigation is the parahippocampus, which contains neurons that represent an animal's position, orientation, and movement through the world, and that change their firing activity to encode behaviorally relevant variables such as reward. However, little prior work on the parahippocampus has considered how neurons encode variables during goal-directed navigation in environments that dynamically change. Here, we recorded single units from rat parahippocampal cortex while subjects performed a goal-directed task. The maze dynamically changed goal-locations via a visual cue on a trial-to-trial basis, requiring subjects to use cue-location associations to receive reward. We observed a mismatch-like signal, with elevated neural activity on incorrect trials, leading to rate-remapping. The strength of this remapping correlated with task performance. Recordings during open-field foraging allowed us to functionally define navigational coding for a subset of the neurons recorded in the maze. This approach revealed that head-direction coding units remapped more than other functional-defined units. Taken together, this work thus raises the possibility that during goal-directed navigation, parahippocampal neurons encode error information reflective of an animal's behavioral performance.
Collapse
Affiliation(s)
- Alexander Gonzalez
- Department of Neurobiology, Stanford University School of MedicineStanfordUnited States
| | - Lisa M Giocomo
- Department of Neurobiology, Stanford University School of MedicineStanfordUnited States
| |
Collapse
|
41
|
Sosa M, Plitt MH, Giocomo LM. Hippocampal sequences span experience relative to rewards. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.27.573490. [PMID: 38234842 PMCID: PMC10793396 DOI: 10.1101/2023.12.27.573490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Hippocampal place cells fire in sequences that span spatial environments and non-spatial modalities, suggesting that hippocampal activity can anchor to the most behaviorally salient aspects of experience. As reward is a highly salient event, we hypothesized that sequences of hippocampal activity can anchor to rewards. To test this, we performed two-photon imaging of hippocampal CA1 neurons as mice navigated virtual environments with changing hidden reward locations. When the reward moved, the firing fields of a subpopulation of cells moved to the same relative position with respect to reward, constructing a sequence of reward-relative cells that spanned the entire task structure. The density of these reward-relative sequences increased with task experience as additional neurons were recruited to the reward-relative population. Conversely, a largely separate subpopulation maintained a spatially-based place code. These findings thus reveal separate hippocampal ensembles can flexibly encode multiple behaviorally salient reference frames, reflecting the structure of the experience.
Collapse
Affiliation(s)
- Marielena Sosa
- Department of Neurobiology, Stanford University School of Medicine; Stanford, CA, USA
| | - Mark H. Plitt
- Department of Neurobiology, Stanford University School of Medicine; Stanford, CA, USA
- Present address: Department of Molecular and Cell Biology, University of California Berkeley; Berkeley, CA, USA
| | - Lisa M. Giocomo
- Department of Neurobiology, Stanford University School of Medicine; Stanford, CA, USA
| |
Collapse
|
42
|
Wise T, Emery K, Radulescu A. Naturalistic reinforcement learning. Trends Cogn Sci 2024; 28:144-158. [PMID: 37777463 PMCID: PMC10878983 DOI: 10.1016/j.tics.2023.08.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/23/2023] [Accepted: 08/24/2023] [Indexed: 10/02/2023]
Abstract
Humans possess a remarkable ability to make decisions within real-world environments that are expansive, complex, and multidimensional. Human cognitive computational neuroscience has sought to exploit reinforcement learning (RL) as a framework within which to explain human decision-making, often focusing on constrained, artificial experimental tasks. In this article, we review recent efforts that use naturalistic approaches to determine how humans make decisions in complex environments that better approximate the real world, providing a clearer picture of how humans navigate the challenges posed by real-world decisions. These studies purposely embed elements of naturalistic complexity within experimental paradigms, rather than focusing on simplification, generating insights into the processes that likely underpin humans' ability to navigate complex, multidimensional real-world environments so successfully.
Collapse
Affiliation(s)
- Toby Wise
- Department of Neuroimaging, King's College London, London, UK.
| | - Kara Emery
- Center for Data Science, New York University, New York, NY, USA
| | - Angela Radulescu
- Center for Computational Psychiatry, Icahn School of Medicine at Mt. Sinai, New York, NY, USA
| |
Collapse
|
43
|
Chen D, Axmacher N, Wang L. Grid codes underlie multiple cognitive maps in the human brain. Prog Neurobiol 2024; 233:102569. [PMID: 38232782 DOI: 10.1016/j.pneurobio.2024.102569] [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: 11/06/2023] [Revised: 01/07/2024] [Accepted: 01/10/2024] [Indexed: 01/19/2024]
Abstract
Grid cells fire at multiple positions that organize the vertices of equilateral triangles tiling a 2D space and are well studied in rodents. The last decade witnessed rapid progress in two other research lines on grid codes-empirical studies on distributed human grid-like representations in physical and multiple non-physical spaces, and cognitive computational models addressing the function of grid cells based on principles of efficient and predictive coding. Here, we review the progress in these fields and integrate these lines into a systematic organization. We also discuss the coordinate mechanisms of grid codes in the human entorhinal cortex and medial prefrontal cortex and their role in neurological and psychiatric diseases.
Collapse
Affiliation(s)
- Dong Chen
- CAS Key Laboratory of Mental Health, Institute of Psychology, 100101, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, 100101, Beijing, China
| | - Nikolai Axmacher
- Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, 44801, Bochum, Germany
| | - Liang Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, 100101, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, 100101, Beijing, China.
| |
Collapse
|
44
|
Li S, Li Z, Liu Q, Ren P, Sun L, Cui Z, Liang X. Predictable navigation through spontaneous brain states with cognitive-map-like representations. Prog Neurobiol 2024; 233:102570. [PMID: 38232783 DOI: 10.1016/j.pneurobio.2024.102570] [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: 06/25/2023] [Revised: 11/19/2023] [Accepted: 01/10/2024] [Indexed: 01/19/2024]
Abstract
Just as navigating a physical environment, navigating through the landscapes of spontaneous brain states may also require an internal cognitive map. Contemporary computation theories propose modeling a cognitive map from a reinforcement learning perspective and argue that the map would be predictive in nature, representing each state as its upcoming states. Here, we used resting-state fMRI to test the hypothesis that the spaces of spontaneously reoccurring brain states are cognitive map-like, and may exhibit future-oriented predictivity. We identified two discrete brain states of the navigation-related brain networks during rest. By combining pattern similarity and dimensional reduction analysis, we embedded the occurrences of each brain state in a two-dimensional space. Successor representation modeling analysis recognized that these brain state occurrences exhibit place cell-like representations, akin to those observed in a physical space. Moreover, we observed predictive transitions of reoccurring brain states, which strongly covaried with individual cognitive and emotional assessments. Our findings offer a novel perspective on the cognitive significance of spontaneous brain activity and support the theory of cognitive map as a unifying framework for mental navigation.
Collapse
Affiliation(s)
- Siyang Li
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China; Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin 150001, China; Research Center for Human-Machine Augmented Intelligence, Research Institute of Artificial Intelligence, Zhejiang Lab, Hangzhou, Zhejiang 311100, China
| | - Zhipeng Li
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China; Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin 150001, China
| | - Qiuyi Liu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China; Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin 150001, China
| | - Peng Ren
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Lili Sun
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China; Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin 150001, China
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Xia Liang
- Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin 150001, China; Frontiers Science Center for Matter Behave in Space Environment, Harbin Institute of Technology, Harbin 150001, China.
| |
Collapse
|
45
|
Zheng XY, Hebart MN, Grill F, Dolan RJ, Doeller CF, Cools R, Garvert MM. Parallel cognitive maps for multiple knowledge structures in the hippocampal formation. Cereb Cortex 2024; 34:bhad485. [PMID: 38204296 PMCID: PMC10839836 DOI: 10.1093/cercor/bhad485] [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: 07/28/2023] [Revised: 11/27/2023] [Accepted: 11/30/2023] [Indexed: 01/12/2024] Open
Abstract
The hippocampal-entorhinal system uses cognitive maps to represent spatial knowledge and other types of relational information. However, objects can often be characterized by different types of relations simultaneously. How does the hippocampal formation handle the embedding of stimuli in multiple relational structures that differ vastly in their mode and timescale of acquisition? Does the hippocampal formation integrate different stimulus dimensions into one conjunctive map or is each dimension represented in a parallel map? Here, we reanalyzed human functional magnetic resonance imaging data from Garvert et al. (2017) that had previously revealed a map in the hippocampal formation coding for a newly learnt transition structure. Using functional magnetic resonance imaging adaptation analysis, we found that the degree of representational similarity in the bilateral hippocampus also decreased as a function of the semantic distance between presented objects. Importantly, while both map-like structures localized to the hippocampal formation, the semantic map was located in more posterior regions of the hippocampal formation than the transition structure and thus anatomically distinct. This finding supports the idea that the hippocampal-entorhinal system forms parallel cognitive maps that reflect the embedding of objects in diverse relational structures.
Collapse
Affiliation(s)
- Xiaochen Y Zheng
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 EN, Nijmegen, the Netherlands
| | - Martin N Hebart
- Max-Planck-Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany
- Department of Medicine, Justus Liebig University, 35390, Giessen, Germany
| | - Filip Grill
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 EN, Nijmegen, the Netherlands
- Radboud University Medical Center, Department of Neurology, 6525 GA, Nijmegen, the Netherlands
| | - Raymond J Dolan
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, United Kingdom
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London WC1B 5EH, United Kingdom
| | - Christian F Doeller
- Max-Planck-Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany
- Kavli Institute for Systems Neuroscience, Centre for Neural Computation, The Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, Jebsen Centre for Alzheimer's Disease, NTNU, 7491, Trondheim, Norway
- Wilhelm Wundt Institute of Psychology, Leipzig University, 04109, Leipzig, Germany
| | - Roshan Cools
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 EN, Nijmegen, the Netherlands
- Radboud University Medical Center, Department of Psychiatry, 6525 GA, Nijmegen, the Netherlands
| | - Mona M Garvert
- Max-Planck-Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany
- Max Planck Research Group NeuroCode, Max Planck Institute for Human Development, 14195, Berlin, Germany
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
- Faculty of Human Sciences, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| |
Collapse
|
46
|
Kang L, Toyoizumi T. Distinguishing examples while building concepts in hippocampal and artificial networks. Nat Commun 2024; 15:647. [PMID: 38245502 PMCID: PMC10799871 DOI: 10.1038/s41467-024-44877-0] [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: 03/09/2023] [Accepted: 01/03/2024] [Indexed: 01/22/2024] Open
Abstract
The hippocampal subfield CA3 is thought to function as an auto-associative network that stores experiences as memories. Information from these experiences arrives directly from the entorhinal cortex as well as indirectly through the dentate gyrus, which performs sparsification and decorrelation. The computational purpose for these dual input pathways has not been firmly established. We model CA3 as a Hopfield-like network that stores both dense, correlated encodings and sparse, decorrelated encodings. As more memories are stored, the former merge along shared features while the latter remain distinct. We verify our model's prediction in rat CA3 place cells, which exhibit more distinct tuning during theta phases with sparser activity. Finally, we find that neural networks trained in multitask learning benefit from a loss term that promotes both correlated and decorrelated representations. Thus, the complementary encodings we have found in CA3 can provide broad computational advantages for solving complex tasks.
Collapse
Affiliation(s)
- Louis Kang
- Neural Circuits and Computations Unit, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan.
- Graduate School of Informatics, Kyoto University, 36-1 Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan.
| | - Taro Toyoizumi
- Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan
- Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| |
Collapse
|
47
|
Jarovi J, Pilkiw M, Takehara-Nishiuchi K. Prefrontal neuronal ensembles link prior knowledge with novel actions during flexible action selection. Cell Rep 2023; 42:113492. [PMID: 37999978 DOI: 10.1016/j.celrep.2023.113492] [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: 03/29/2023] [Revised: 10/23/2023] [Accepted: 11/10/2023] [Indexed: 11/26/2023] Open
Abstract
We make decisions based on currently perceivable information or an internal model of the environment. The medial prefrontal cortex (mPFC) and its interaction with the hippocampus have been implicated in the latter, model-based decision-making; however, the underlying computational properties remain incompletely understood. We have examined mPFC spiking and hippocampal oscillatory activity while rats flexibly select new actions using a known associative structure of environmental cues and outcomes. During action selection, the mPFC reinstates representations of the associative structure. These awake reactivation events are accompanied by synchronous firings among neurons coding the associative structure and those coding actions. Moreover, their functional coupling is strengthened upon the reactivation events leading to adaptive actions. In contrast, only cue-coding neurons improve functional coupling during hippocampal sharp wave ripples. Thus, the lack of direct experience disconnects the mPFC from the hippocampus to independently form self-organized neuronal ensemble dynamics linking prior knowledge with novel actions.
Collapse
Affiliation(s)
- Justin Jarovi
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON M5S 3G5, Canada
| | - Maryna Pilkiw
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON M5S 3G5, Canada
| | - Kaori Takehara-Nishiuchi
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON M5S 3G5, Canada; Department of Psychology, University of Toronto, Toronto, ON M5S 3G3, Canada; Collaborative Program in Neuroscience, University of Toronto, Toronto, ON M5S 1A8, Canada.
| |
Collapse
|
48
|
Veselic S, Muller TH, Gutierrez E, Behrens TEJ, Hunt LT, Butler JL, Kennerley SW. A cognitive map for value-guided choice in ventromedial prefrontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.15.571895. [PMID: 38168410 PMCID: PMC10760117 DOI: 10.1101/2023.12.15.571895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
The prefrontal cortex is crucial for economic decision-making and representing the value of options. However, how such representations facilitate flexible decisions remains unknown. We reframe economic decision-making in prefrontal cortex in line with representations of structure within the medial temporal lobe because such cognitive map representations are known to facilitate flexible behaviour. Specifically, we framed choice between different options as a navigation process in value space. Here we show that choices in a 2D value space defined by reward magnitude and probability were represented with a grid-like code, analogous to that found in spatial navigation. The grid-like code was present in ventromedial prefrontal cortex (vmPFC) local field potential theta frequency and the result replicated in an independent dataset. Neurons in vmPFC similarly contained a grid-like code, in addition to encoding the linear value of the chosen option. Importantly, both signals were modulated by theta frequency - occurring at theta troughs but on separate theta cycles. Furthermore, we found sharp-wave ripples - a key neural signature of planning and flexible behaviour - in vmPFC, which were modulated by accuracy and reward. These results demonstrate that multiple cognitive map-like computations are deployed in vmPFC during economic decision-making, suggesting a new framework for the implementation of choice in prefrontal cortex.
Collapse
Affiliation(s)
- Sebastijan Veselic
- Department of Experimental Psychology, University of Oxford, UK
- Clinical and Movement Neurosciences, Department of Motor Neuroscience, University College London, London, UK
| | - Timothy H Muller
- Department of Experimental Psychology, University of Oxford, UK
- Clinical and Movement Neurosciences, Department of Motor Neuroscience, University College London, London, UK
| | - Elena Gutierrez
- Department of Experimental Psychology, University of Oxford, UK
- Clinical and Movement Neurosciences, Department of Motor Neuroscience, University College London, London, UK
| | - Timothy E J Behrens
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford, UK
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour College, University College London, London, UK
| | - Laurence T Hunt
- Department of Experimental Psychology, University of Oxford, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - James L Butler
- Department of Experimental Psychology, University of Oxford, UK
| | - Steven W Kennerley
- Department of Experimental Psychology, University of Oxford, UK
- Clinical and Movement Neurosciences, Department of Motor Neuroscience, University College London, London, UK
| |
Collapse
|
49
|
Son JY, Bhandari A, FeldmanHall O. Abstract cognitive maps of social network structure aid adaptive inference. Proc Natl Acad Sci U S A 2023; 120:e2310801120. [PMID: 37963254 PMCID: PMC10666027 DOI: 10.1073/pnas.2310801120] [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: 06/28/2023] [Accepted: 10/12/2023] [Indexed: 11/16/2023] Open
Abstract
Social navigation-such as anticipating where gossip may spread, or identifying which acquaintances can help land a job-relies on knowing how people are connected within their larger social communities. Problematically, for most social networks, the space of possible relationships is too vast to observe and memorize. Indeed, people's knowledge of these social relations is well known to be biased and error-prone. Here, we reveal that these biased representations reflect a fundamental computation that abstracts over individual relationships to enable principled inferences about unseen relationships. We propose a theory of network representation that explains how people learn inferential cognitive maps of social relations from direct observation, what kinds of knowledge structures emerge as a consequence, and why it can be beneficial to encode systematic biases into social cognitive maps. Leveraging simulations, laboratory experiments, and "field data" from a real-world network, we find that people abstract observations of direct relations (e.g., friends) into inferences of multistep relations (e.g., friends-of-friends). This multistep abstraction mechanism enables people to discover and represent complex social network structure, affording adaptive inferences across a variety of contexts, including friendship, trust, and advice-giving. Moreover, this multistep abstraction mechanism unifies a variety of otherwise puzzling empirical observations about social behavior. Our proposal generalizes the theory of cognitive maps to the fundamental computational problem of social inference, presenting a powerful framework for understanding the workings of a predictive mind operating within a complex social world.
Collapse
Affiliation(s)
- Jae-Young Son
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI02912
| | - Apoorva Bhandari
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI02912
| | - Oriel FeldmanHall
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI02912
- Carney Institute for Brain Sciences, Brown University, Providence, RI02912
| |
Collapse
|
50
|
Schøyen V, Pettersen MB, Holzhausen K, Fyhn M, Malthe-Sørenssen A, Lepperød ME. Coherently remapping toroidal cells but not Grid cells are responsible for path integration in virtual agents. iScience 2023; 26:108102. [PMID: 37867941 PMCID: PMC10589895 DOI: 10.1016/j.isci.2023.108102] [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: 12/15/2022] [Revised: 08/25/2023] [Accepted: 09/27/2023] [Indexed: 10/24/2023] Open
Abstract
It is widely believed that grid cells provide cues for path integration, with place cells encoding an animal's location and environmental identity. When entering a new environment, these cells remap concurrently, sparking debates about their causal relationship. Using a continuous attractor recurrent neural network, we study spatial cell dynamics in multiple environments. We investigate grid cell remapping as a function of global remapping in place-like units through random resampling of place cell centers. Dimensionality reduction techniques reveal that a subset of cells manifest a persistent torus across environments. Unexpectedly, these toroidal cells resemble band-like cells rather than high grid score units. Subsequent pruning studies reveal that toroidal cells are crucial for path integration while grid cells are not. As we extend the model to operate across many environments, we delineate its generalization boundaries, revealing challenges with modeling many environments in current models.
Collapse
Affiliation(s)
- Vemund Schøyen
- Department of Biosciences, University of Oslo, Oslo 0313, Norway
| | | | | | - Marianne Fyhn
- Department of Biosciences, University of Oslo, Oslo 0313, Norway
- Simula Research Laboratory, Norway
| | - Anders Malthe-Sørenssen
- Department of Physics, University of Oslo, Oslo 0313, Norway
- Simula Research Laboratory, Norway
| | - Mikkel Elle Lepperød
- Department of Physics, University of Oslo, Oslo 0313, Norway
- Department of Biosciences, University of Oslo, Oslo 0313, Norway
- Simula Research Laboratory, Norway
| |
Collapse
|