51
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Courellis HS, Mixha J, Cardenas AR, Kimmel D, Reed CM, Valiante TA, Salzman CD, Mamelak AN, Fusi S, Rutishauser U. Abstract representations emerge in human hippocampal neurons during inference behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.10.566490. [PMID: 37986878 PMCID: PMC10659400 DOI: 10.1101/2023.11.10.566490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Humans have the remarkable cognitive capacity to rapidly adapt to changing environments. Central to this capacity is the ability to form high-level, abstract representations that take advantage of regularities in the world to support generalization 1 . However, little is known about how these representations are encoded in populations of neurons, how they emerge through learning, and how they relate to behavior 2,3 . Here we characterized the representational geometry of populations of neurons (single-units) recorded in the hippocampus, amygdala, medial frontal cortex, and ventral temporal cortex of neurosurgical patients who are performing an inferential reasoning task. We find that only the neural representations formed in the hippocampus simultaneously encode multiple task variables in an abstract, or disentangled, format. This representational geometry is uniquely observed after patients learn to perform inference, and consisted of disentangled directly observable and discovered latent task variables. Interestingly, learning to perform inference by trial and error or through verbal instructions led to the formation of hippocampal representations with similar geometric properties. The observed relation between representational format and inference behavior suggests that abstract/disentangled representational geometries are important for complex cognition.
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52
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Stroud JP, Watanabe K, Suzuki T, Stokes MG, Lengyel M. Optimal information loading into working memory explains dynamic coding in the prefrontal cortex. Proc Natl Acad Sci U S A 2023; 120:e2307991120. [PMID: 37983510 PMCID: PMC10691340 DOI: 10.1073/pnas.2307991120] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/29/2023] [Indexed: 11/22/2023] Open
Abstract
Working memory involves the short-term maintenance of information and is critical in many tasks. The neural circuit dynamics underlying working memory remain poorly understood, with different aspects of prefrontal cortical (PFC) responses explained by different putative mechanisms. By mathematical analysis, numerical simulations, and using recordings from monkey PFC, we investigate a critical but hitherto ignored aspect of working memory dynamics: information loading. We find that, contrary to common assumptions, optimal loading of information into working memory involves inputs that are largely orthogonal, rather than similar, to the late delay activities observed during memory maintenance, naturally leading to the widely observed phenomenon of dynamic coding in PFC. Using a theoretically principled metric, we show that PFC exhibits the hallmarks of optimal information loading. We also find that optimal information loading emerges as a general dynamical strategy in task-optimized recurrent neural networks. Our theory unifies previous, seemingly conflicting theories of memory maintenance based on attractor or purely sequential dynamics and reveals a normative principle underlying dynamic coding.
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Affiliation(s)
- Jake P. Stroud
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, CambridgeCB2 1PZ, United Kingdom
| | - Kei Watanabe
- Graduate School of Frontier Biosciences, Osaka University, Osaka565-0871, Japan
| | - Takafumi Suzuki
- Center for Information and Neural Networks, National Institute of Communication and Information Technology, Osaka565-0871, Japan
| | - Mark G. Stokes
- Department of Experimental Psychology, University of Oxford, OxfordOX2 6GG, United Kingdom
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, OxfordOX3 9DU, United Kingdom
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, CambridgeCB2 1PZ, United Kingdom
- Center for Cognitive Computation, Department of Cognitive Science, Central European University, BudapestH-1051, Hungary
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53
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Avcu E, Gow D. Exploring Abstract Pattern Representation in The Brain and Non-symbolic Neural Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.27.568877. [PMID: 38076846 PMCID: PMC10705297 DOI: 10.1101/2023.11.27.568877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2023]
Abstract
Human cognitive and linguistic generativity depends on the ability to identify abstract relationships between perceptually dissimilar items. Marcus et al. (1999) found that human infants can rapidly discover and generalize patterns of syllable repetition (reduplication) that depend on the abstract property of identity, but simple recurrent neural networks (SRNs) could not. They interpreted these results as evidence that purely associative neural network models provide an inadequate framework for characterizing the fundamental generativity of human cognition. Here, we present a series of deep long short-term memory (LSTM) models that identify abstract syllable repetition patterns and words based on training with cochleagrams that represent auditory stimuli. We demonstrate that models trained to identify individual syllable trigram words and models trained to identify reduplication patterns discover representations that support classification of abstract repetition patterns. Simulations examined the effects of training categories (words vs. patterns) and pretraining to identify syllables, on the development of hidden node representations that support repetition pattern discrimination. Representational similarity analyses (RSA) comparing patterns of regional brain activity based on MRI-constrained MEG/EEG data to patterns of hidden node activation elicited by the same stimuli showed a significant correlation between brain activity localized in primarily posterior temporal regions and representations discovered by the models. These results suggest that associative mechanisms operating over discoverable representations that capture abstract stimulus properties account for a critical example of human cognitive generativity.
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Affiliation(s)
- Enes Avcu
- Department of Neurology, Massachusetts General Hospital, Cambridge, MA 02170
| | - David Gow
- Department of Neurology, Massachusetts General Hospital, Cambridge, MA 02170
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54
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Kim TD, Luo TZ, Can T, Krishnamurthy K, Pillow JW, Brody CD. Flow-field inference from neural data using deep recurrent networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.14.567136. [PMID: 38014290 PMCID: PMC10680687 DOI: 10.1101/2023.11.14.567136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Computations involved in processes such as decision-making, working memory, and motor control are thought to emerge from the dynamics governing the collective activity of neurons in large populations. But the estimation of these dynamics remains a significant challenge. Here we introduce Flow-field Inference from Neural Data using deep Recurrent networks (FINDR), an unsupervised deep learning method that can infer low-dimensional nonlinear stochastic dynamics underlying neural population activity. Using population spike train data from frontal brain regions of rats performing an auditory decision-making task, we demonstrate that FINDR outperforms existing methods in capturing the heterogeneous responses of individual neurons. We further show that FINDR can discover interpretable low-dimensional dynamics when it is trained to disentangle task-relevant and irrelevant components of the neural population activity. Importantly, the low-dimensional nature of the learned dynamics allows for explicit visualization of flow fields and attractor structures. We suggest FINDR as a powerful method for revealing the low-dimensional task-relevant dynamics of neural populations and their associated computations.
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Affiliation(s)
| | - Thomas Zhihao Luo
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | - Tankut Can
- School of Natural Sciences, Institute for Advanced Study, Princeton, NJ
| | - Kamesh Krishnamurthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
- Joseph Henry Laboratories of Physics, Princeton University, Princeton, NJ
| | - Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | - Carlos D Brody
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
- Howard Hughes Medical Institute, Princeton University, Princeton, NJ
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55
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Jarne C, Laje R. Exploring weight initialization, diversity of solutions, and degradation in recurrent neural networks trained for temporal and decision-making tasks. J Comput Neurosci 2023; 51:407-431. [PMID: 37561278 DOI: 10.1007/s10827-023-00857-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 05/26/2023] [Accepted: 06/27/2023] [Indexed: 08/11/2023]
Abstract
Recurrent Neural Networks (RNNs) are frequently used to model aspects of brain function and structure. In this work, we trained small fully-connected RNNs to perform temporal and flow control tasks with time-varying stimuli. Our results show that different RNNs can solve the same task by converging to different underlying dynamics and also how the performance gracefully degrades as either network size is decreased, interval duration is increased, or connectivity damage is induced. For the considered tasks, we explored how robust the network obtained after training can be according to task parameterization. In the process, we developed a framework that can be useful to parameterize other tasks of interest in computational neuroscience. Our results are useful to quantify different aspects of the models, which are normally used as black boxes and need to be understood in order to model the biological response of cerebral cortex areas.
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Affiliation(s)
- Cecilia Jarne
- Universidad Nacional de Quilmes, Departamento de Ciencia y Tecnología, Bernal, Buenos Aires, Argentina.
- CONICET, Buenos Aires, Argentina.
- Center for Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
| | - Rodrigo Laje
- Universidad Nacional de Quilmes, Departamento de Ciencia y Tecnología, Bernal, Buenos Aires, Argentina
- CONICET, Buenos Aires, Argentina
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56
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Finn ES, Poldrack RA, Shine JM. Functional neuroimaging as a catalyst for integrated neuroscience. Nature 2023; 623:263-273. [PMID: 37938706 DOI: 10.1038/s41586-023-06670-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 09/22/2023] [Indexed: 11/09/2023]
Abstract
Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake, behaving human brain. By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our understanding of brain function and its links to both normal and atypical behaviour. Despite this headway, progress in human cognitive neuroscience that uses fMRI has been relatively isolated from rapid advances in other subdomains of neuroscience, which themselves are also somewhat siloed from one another. In this Perspective, we argue that fMRI is well-placed to integrate the diverse subfields of systems, cognitive, computational and clinical neuroscience. We first summarize the strengths and weaknesses of fMRI as an imaging tool, then highlight examples of studies that have successfully used fMRI in each subdomain of neuroscience. We then provide a roadmap for the future advances that will be needed to realize this integrative vision. In this way, we hope to demonstrate how fMRI can help usher in a new era of interdisciplinary coherence in neuroscience.
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Affiliation(s)
- Emily S Finn
- Department of Psychological and Brain Sciences, Dartmouth College, Dartmouth, NH, USA.
| | | | - James M Shine
- School of Medical Sciences, University of Sydney, Sydney, New South Wales, Australia.
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57
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Durstewitz D, Koppe G, Thurm MI. Reconstructing computational system dynamics from neural data with recurrent neural networks. Nat Rev Neurosci 2023; 24:693-710. [PMID: 37794121 DOI: 10.1038/s41583-023-00740-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/18/2023] [Indexed: 10/06/2023]
Abstract
Computational models in neuroscience usually take the form of systems of differential equations. The behaviour of such systems is the subject of dynamical systems theory. Dynamical systems theory provides a powerful mathematical toolbox for analysing neurobiological processes and has been a mainstay of computational neuroscience for decades. Recently, recurrent neural networks (RNNs) have become a popular machine learning tool for studying the non-linear dynamics of neural and behavioural processes by emulating an underlying system of differential equations. RNNs have been routinely trained on similar behavioural tasks to those used for animal subjects to generate hypotheses about the underlying computational mechanisms. By contrast, RNNs can also be trained on the measured physiological and behavioural data, thereby directly inheriting their temporal and geometrical properties. In this way they become a formal surrogate for the experimentally probed system that can be further analysed, perturbed and simulated. This powerful approach is called dynamical system reconstruction. In this Perspective, we focus on recent trends in artificial intelligence and machine learning in this exciting and rapidly expanding field, which may be less well known in neuroscience. We discuss formal prerequisites, different model architectures and training approaches for RNN-based dynamical system reconstructions, ways to evaluate and validate model performance, how to interpret trained models in a neuroscience context, and current challenges.
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Affiliation(s)
- Daniel Durstewitz
- Dept. of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany.
- Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany.
| | - Georgia Koppe
- Dept. of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Dept. of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Max Ingo Thurm
- Dept. of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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58
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Fisher A, Rao RPN. Recursive neural programs: A differentiable framework for learning compositional part-whole hierarchies and image grammars. PNAS NEXUS 2023; 2:pgad337. [PMID: 37954157 PMCID: PMC10637337 DOI: 10.1093/pnasnexus/pgad337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 10/05/2023] [Indexed: 11/14/2023]
Abstract
Human vision, thought, and planning involve parsing and representing objects and scenes using structured representations based on part-whole hierarchies. Computer vision and machine learning researchers have recently sought to emulate this capability using neural networks, but a generative model formulation has been lacking. Generative models that leverage compositionality, recursion, and part-whole hierarchies are thought to underlie human concept learning and the ability to construct and represent flexible mental concepts. We introduce Recursive Neural Programs (RNPs), a neural generative model that addresses the part-whole hierarchy learning problem by modeling images as hierarchical trees of probabilistic sensory-motor programs. These programs recursively reuse learned sensory-motor primitives to model an image within different spatial reference frames, enabling hierarchical composition of objects from parts and implementing a grammar for images. We show that RNPs can learn part-whole hierarchies for a variety of image datasets, allowing rich compositionality and intuitive parts-based explanations of objects. Our model also suggests a cognitive framework for understanding how human brains can potentially learn and represent concepts in terms of recursively defined primitives and their relations with each other.
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Affiliation(s)
- Ares Fisher
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
| | - Rajesh P N Rao
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
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59
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Tsuda B, Richmond BJ, Sejnowski TJ. Exploring strategy differences between humans and monkeys with recurrent neural networks. PLoS Comput Biol 2023; 19:e1011618. [PMID: 37983250 PMCID: PMC10695363 DOI: 10.1371/journal.pcbi.1011618] [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: 02/19/2023] [Revised: 12/04/2023] [Accepted: 10/19/2023] [Indexed: 11/22/2023] Open
Abstract
Animal models are used to understand principles of human biology. Within cognitive neuroscience, non-human primates are considered the premier model for studying decision-making behaviors in which direct manipulation experiments are still possible. Some prominent studies have brought to light major discrepancies between monkey and human cognition, highlighting problems with unverified extrapolation from monkey to human. Here, we use a parallel model system-artificial neural networks (ANNs)-to investigate a well-established discrepancy identified between monkeys and humans with a working memory task, in which monkeys appear to use a recency-based strategy while humans use a target-selective strategy. We find that ANNs trained on the same task exhibit a progression of behavior from random behavior (untrained) to recency-like behavior (partially trained) and finally to selective behavior (further trained), suggesting monkeys and humans may occupy different points in the same overall learning progression. Surprisingly, what appears to be recency-like behavior in the ANN, is in fact an emergent non-recency-based property of the organization of the neural network's state space during its development through training. We find that explicit encouragement of recency behavior during training has a dual effect, not only causing an accentuated recency-like behavior, but also speeding up the learning process altogether, resulting in an efficient shaping mechanism to achieve the optimal strategy. Our results suggest a new explanation for the discrepency observed between monkeys and humans and reveal that what can appear to be a recency-based strategy in some cases may not be recency at all.
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Affiliation(s)
- Ben Tsuda
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California, United States of America
- Neurosciences Graduate Program, University of California San Diego, La Jolla, California, United States of America
- Medical Scientist Training Program, University of California San Diego, La Jolla, California, United States of America
| | - Barry J. Richmond
- Section on Neural Coding and Computation, National Institute of Mental Health, Bethesda, Maryland, United States of America
| | - Terrence J. Sejnowski
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California, United States of America
- Institute for Neural Computation, University of California San Diego, La Jolla, California, United States of America
- Division of Biological Sciences, University of California San Diego, La Jolla, California, United States of America
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60
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Shah NP, Avansino D, Kamdar F, Nicolas C, Kapitonava A, Vargas-Irwin C, Hochberg L, Pandarinath C, Shenoy K, Willett FR, Henderson J. Pseudo-linear Summation explains Neural Geometry of Multi-finger Movements in Human Premotor Cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.11.561982. [PMID: 37873182 PMCID: PMC10592742 DOI: 10.1101/2023.10.11.561982] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
How does the motor cortex combine simple movements (such as single finger flexion/extension) into complex movements (such hand gestures or playing piano)? Motor cortical activity was recorded using intracortical multi-electrode arrays in two people with tetraplegia as they attempted single, pairwise and higher order finger movements. Neural activity for simultaneous movements was largely aligned with linear summation of corresponding single finger movement activities, with two violations. First, the neural activity was normalized, preventing a large magnitude with an increasing number of moving fingers. Second, the neural tuning direction of weakly represented fingers (e.g. middle) changed significantly as a result of the movement of other fingers. These deviations from linearity resulted in non-linear methods outperforming linear methods for neural decoding. Overall, simultaneous finger movements are thus represented by the combination of individual finger movements by pseudo-linear summation.
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Affiliation(s)
| | - Donald Avansino
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
| | | | - Claire Nicolas
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Anastasia Kapitonava
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Carlos Vargas-Irwin
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA
- Department of Neuroscience, Brown University, Providence, RI, USA
| | - Leigh Hochberg
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA
- School of Engineering, Brown University, Providence, RI, USA
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Chethan Pandarinath
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
- Department of Neurosurgery, Emory University, Atlanta, GA, USA
| | - Krishna Shenoy
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Bio-X Institute, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
| | - Francis R Willett
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
| | - Jaimie Henderson
- Department of Neurosurgery, Stanford University
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Bio-X Institute, Stanford University, Stanford, CA, USA
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61
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Soo WWM, Goudar V, Wang XJ. Training biologically plausible recurrent neural networks on cognitive tasks with long-term dependencies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.10.561588. [PMID: 37873445 PMCID: PMC10592728 DOI: 10.1101/2023.10.10.561588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Training recurrent neural networks (RNNs) has become a go-to approach for generating and evaluating mechanistic neural hypotheses for cognition. The ease and efficiency of training RNNs with backpropagation through time and the availability of robustly supported deep learning libraries has made RNN modeling more approachable and accessible to neuroscience. Yet, a major technical hindrance remains. Cognitive processes such as working memory and decision making involve neural population dynamics over a long period of time within a behavioral trial and across trials. It is difficult to train RNNs to accomplish tasks where neural representations and dynamics have long temporal dependencies without gating mechanisms such as LSTMs or GRUs which currently lack experimental support and prohibit direct comparison between RNNs and biological neural circuits. We tackled this problem based on the idea of specialized skip-connections through time to support the emergence of task-relevant dynamics, and subsequently reinstitute biological plausibility by reverting to the original architecture. We show that this approach enables RNNs to successfully learn cognitive tasks that prove impractical if not impossible to learn using conventional methods. Over numerous tasks considered here, we achieve less training steps and shorter wall-clock times, particularly in tasks that require learning long-term dependencies via temporal integration over long timescales or maintaining a memory of past events in hidden-states. Our methods expand the range of experimental tasks that biologically plausible RNN models can learn, thereby supporting the development of theory for the emergent neural mechanisms of computations involving long-term dependencies.
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62
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Khona M, Chandra S, Ma JJ, Fiete IR. Winning the Lottery With Neural Connectivity Constraints: Faster Learning Across Cognitive Tasks With Spatially Constrained Sparse RNNs. Neural Comput 2023; 35:1850-1869. [PMID: 37725708 DOI: 10.1162/neco_a_01613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 06/20/2023] [Indexed: 09/21/2023]
Abstract
Recurrent neural networks (RNNs) are often used to model circuits in the brain and can solve a variety of difficult computational problems requiring memory, error correction, or selection (Hopfield, 1982; Maass et al., 2002; Maass, 2011). However, fully connected RNNs contrast structurally with their biological counterparts, which are extremely sparse (about 0.1%). Motivated by the neocortex, where neural connectivity is constrained by physical distance along cortical sheets and other synaptic wiring costs, we introduce locality masked RNNs (LM-RNNs) that use task-agnostic predetermined graphs with sparsity as low as 4%. We study LM-RNNs in a multitask learning setting relevant to cognitive systems neuroscience with a commonly used set of tasks, 20-Cog-tasks (Yang et al., 2019). We show through reductio ad absurdum that 20-Cog-tasks can be solved by a small pool of separated autapses that we can mechanistically analyze and understand. Thus, these tasks fall short of the goal of inducing complex recurrent dynamics and modular structure in RNNs. We next contribute a new cognitive multitask battery, Mod-Cog, consisting of up to 132 tasks that expands by about seven-fold the number of tasks and task complexity of 20-Cog-tasks. Importantly, while autapses can solve the simple 20-Cog-tasks, the expanded task set requires richer neural architectures and continuous attractor dynamics. On these tasks, we show that LM-RNNs with an optimal sparsity result in faster training and better data efficiency than fully connected networks.
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Affiliation(s)
- Mikail Khona
- Department of Physics, MIT, Cambridge, MA 02139, U.S.A.
| | - Sarthak Chandra
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, U.S.A.
| | - Joy J Ma
- Department of Physics, MIT, Cambridge, MA 02139, U.S.A.
| | - Ila R Fiete
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, U.S.A.
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63
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Qin S. Emotion representations in context: maturation and convergence pathways. Trends Cogn Sci 2023; 27:883-885. [PMID: 37598002 DOI: 10.1016/j.tics.2023.07.009] [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: 07/10/2023] [Accepted: 07/23/2023] [Indexed: 08/21/2023]
Abstract
How does the human brain develop stable emotion representations? According to recent work by Camacho et al., neural representations of contextualized emotional cues are distinct and fairly stable by mid-to-late childhood and activation patterns become increasingly similar between individuals during adolescence. Here, I propose a framework for investigating contextualized emotion processing.
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Affiliation(s)
- Shaozheng Qin
- IDG/McGovern Institute for Brain Research & State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China.
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64
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Wu S, Huang C, Snyder A, Smith M, Doiron B, Yu B. Automated customization of large-scale spiking network models to neuronal population activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.21.558920. [PMID: 37790533 PMCID: PMC10542160 DOI: 10.1101/2023.09.21.558920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Understanding brain function is facilitated by constructing computational models that accurately reproduce aspects of brain activity. Networks of spiking neurons capture the underlying biophysics of neuronal circuits, yet the dependence of their activity on model parameters is notoriously complex. As a result, heuristic methods have been used to configure spiking network models, which can lead to an inability to discover activity regimes complex enough to match large-scale neuronal recordings. Here we propose an automatic procedure, Spiking Network Optimization using Population Statistics (SNOPS), to customize spiking network models that reproduce the population-wide covariability of large-scale neuronal recordings. We first confirmed that SNOPS accurately recovers simulated neural activity statistics. Then, we applied SNOPS to recordings in macaque visual and prefrontal cortices and discovered previously unknown limitations of spiking network models. Taken together, SNOPS can guide the development of network models and thereby enable deeper insight into how networks of neurons give rise to brain function.
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Affiliation(s)
- Shenghao Wu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Chengcheng Huang
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Adam Snyder
- Department of Neuroscience, University of Rochester, Rochester, NY, USA
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, USA
- Center for Visual Science, University of Rochester, Rochester, NY, USA
| | - Matthew Smith
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Brent Doiron
- Department of Neurobiology, University of Chicago, Chicago, IL, USA
- Department of Statistics, University of Chicago, Chicago, IL, USA
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, USA
| | - Byron Yu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
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Tan PK, Tang C, Herikstad R, Pillay A, Libedinsky C. Distinct Lateral Prefrontal Regions Are Organized in an Anterior-Posterior Functional Gradient. J Neurosci 2023; 43:6564-6572. [PMID: 37607819 PMCID: PMC10513068 DOI: 10.1523/jneurosci.0007-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 08/13/2023] [Accepted: 08/15/2023] [Indexed: 08/24/2023] Open
Abstract
The dorsolateral prefrontal cortex (dlPFC) is composed of multiple anatomically defined regions involved in higher-order cognitive processes, including working memory and selective attention. It is organized in an anterior-posterior global gradient where posterior regions track changes in the environment, whereas anterior regions support abstract neural representations. However, it remains unknown if such a global gradient results from a smooth gradient that spans regions or an emergent property arising from functionally distinct regions, that is, an areal gradient. Here, we recorded single neurons in the dlPFC of nonhuman primates trained to perform a memory-guided saccade task with an interfering distractor and analyzed their physiological properties along the anterior-posterior axis. We found that these physiological properties were best described by an areal gradient. Further, population analyses revealed that there is a distributed representation of spatial information across the dlPFC. Our results validate the functional boundaries between anatomically defined dlPFC regions and highlight the distributed nature of computations underlying working memory across the dlPFC.SIGNIFICANCE STATEMENT Activity of frontal lobe regions is known to possess an anterior-posterior functional gradient. However, it is not known whether this gradient is the result of individual brain regions organized in a gradient (like a staircase), or a smooth gradient that spans regions (like a slide). Analysis of physiological properties of individual neurons in the primate frontal regions suggest that individual regions are organized as a gradient, rather than a smooth gradient. At the population level, working memory was more prominent in posterior regions, although it was also present in anterior regions. This is consistent with the functional segregation of brain regions that is also observed in other systems (i.e., the visual system).
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Affiliation(s)
- Pin Kwang Tan
- Institute of Neuroscience, University of Texas at Austin, Austin, Texas 78712
| | - Cheng Tang
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Roger Herikstad
- The N1 Institute for Health, National University of Singapore, Singapore 117456
| | - Arunika Pillay
- Department of Psychology, Royal Holloway University of London, Egham TW20 OEX, United Kingdom
| | - Camilo Libedinsky
- The N1 Institute for Health, National University of Singapore, Singapore 117456
- Department of Psychology, National University of Singapore, Singapore 117570
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, National University of Singapore, Singapore 138632
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Miconi T, Kay K. An active neural mechanism for relational learning and fast knowledge reassembly. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.27.550739. [PMID: 37546842 PMCID: PMC10402151 DOI: 10.1101/2023.07.27.550739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
How do we gain general insights from limited novel experiences? Humans and animals have a striking ability to learn relationships between experienced items, enabling efficient generalization and rapid assimilation of new information. One fundamental instance of such relational learning is transitive inference (learn A>B and B>C, infer A>C), which can be quickly and globally reorganized upon learning a new item (learn A>B>C and D>E>F, then C>D, and infer B>E). Despite considerable study, neural mechanisms of transitive inference and fast reassembly of existing knowledge remain elusive. Here we adopt a meta-learning ("learning-to-learn") approach. We train artificial neural networks, endowed with synaptic plasticity and neuromodulation, to be able to learn novel orderings of arbitrary stimuli from repeated presentation of stimulus pairs. We then obtain a complete mechanistic understanding of this discovered neural learning algorithm. Remarkably, this learning involves active cognition: items from previous trials are selectively reinstated in working memory, enabling delayed, self-generated learning and knowledge reassembly. These findings identify a new mechanism for relational learning and insight, suggest new interpretations of neural activity in cognitive tasks, and highlight a novel approach to discovering neural mechanisms capable of supporting cognitive behaviors.
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67
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Stavropoulos A, Lakshminarasimhan KJ, Angelaki DE. Belief embodiment through eye movements facilitates memory-guided navigation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.21.554107. [PMID: 37662309 PMCID: PMC10473632 DOI: 10.1101/2023.08.21.554107] [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
Neural network models optimized for task performance often excel at predicting neural activity but do not explain other properties such as the distributed representation across functionally distinct areas. Distributed representations may arise from animals' strategies for resource utilization, however, fixation-based paradigms deprive animals of a vital resource: eye movements. During a naturalistic task in which humans use a joystick to steer and catch flashing fireflies in a virtual environment lacking position cues, subjects physically track the latent task variable with their gaze. We show this strategy to be true also during an inertial version of the task in the absence of optic flow and demonstrate that these task-relevant eye movements reflect an embodiment of the subjects' dynamically evolving internal beliefs about the goal. A neural network model with tuned recurrent connectivity between oculomotor and evidence-integrating frontoparietal circuits accounted for this behavioral strategy. Critically, this model better explained neural data from monkeys' posterior parietal cortex compared to task-optimized models unconstrained by such an oculomotor-based cognitive strategy. These results highlight the importance of unconstrained movement in working memory computations and establish a functional significance of oculomotor signals for evidence-integration and navigation computations via embodied cognition.
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Affiliation(s)
| | | | - Dora E. Angelaki
- Center for Neural Science, New York University, New York, NY, USA
- Tandon School of Engineering, New York University, New York, NY, USA
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68
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Thrower L, Dang W, Jaffe RG, Sun JD, Constantinidis C. Decoding working memory information from persistent and activity-silent neurons in the primate prefrontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.25.550371. [PMID: 37546782 PMCID: PMC10402050 DOI: 10.1101/2023.07.25.550371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Persistent activity of neurons in the prefrontal cortex has been thought to represent the information maintained in working memory, though alternative models have recently challenged this idea. Activity-silent theories posit that stimulus information may be maintained by the activity pattern of neurons that do not produce firing rate significantly elevated about their baseline during the delay period of working memory tasks. We thus tested the ability of neurons that do and do not generate persistent activity in the prefrontal cortex of monkeys to represent spatial and object information in working memory. Neurons that generated persistent activity represented more information about the stimuli in both spatial and object working memory tasks. The amount of information that could be decoded from neural activity depended on the choice of decoder and parameters used but neurons with persistent activity outperformed non-persistent neurons consistently. Although averaged across all neurons and stimuli, firing rate did not appear clearly elevated above baseline during the maintenance of neural activity particularly for object working memory, this grant average masked neurons that generated persistent activity selective for their preferred stimuli, which carried the majority of information about the stimulus identity. These results reveal that prefrontal neurons with generate persistent activity constitute the primary mechanism of working memory maintenance in the cortex. NEW AND NOTEWORTHY Competing theories suggest that neurons that generate persistent activity or do not are primarily responsible for the maintenance of information, particularly regarding object working memory. While the two models have been debated on theoretical terms, direct comparison of empirical results have been lacking. Analysis of neural activity in a large database of prefrontal recordings revealed that neurons that generate persistent activity were primarily responsible for the maintenance of both spatial and object working memory.
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69
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Liu J, Bayle DJ, Spagna A, Sitt JD, Bourgeois A, Lehongre K, Fernandez-Vidal S, Adam C, Lambrecq V, Navarro V, Seidel Malkinson T, Bartolomeo P. Fronto-parietal networks shape human conscious report through attention gain and reorienting. Commun Biol 2023; 6:730. [PMID: 37454150 PMCID: PMC10349830 DOI: 10.1038/s42003-023-05108-2] [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: 04/28/2023] [Accepted: 07/06/2023] [Indexed: 07/18/2023] Open
Abstract
How do attention and consciousness interact in the human brain? Rival theories of consciousness disagree on the role of fronto-parietal attentional networks in conscious perception. We recorded neural activity from 727 intracerebral contacts in 13 epileptic patients, while they detected near-threshold targets preceded by attentional cues. Clustering revealed three neural patterns: first, attention-enhanced conscious report accompanied sustained right-hemisphere fronto-temporal activity in networks connected by the superior longitudinal fasciculus (SLF) II-III, and late accumulation of activity (>300 ms post-target) in bilateral dorso-prefrontal and right-hemisphere orbitofrontal cortex (SLF I-III). Second, attentional reorienting affected conscious report through early, sustained activity in a right-hemisphere network (SLF III). Third, conscious report accompanied left-hemisphere dorsolateral-prefrontal activity. Task modeling with recurrent neural networks revealed multiple clusters matching the identified brain clusters, elucidating the causal relationship between clusters in conscious perception of near-threshold targets. Thus, distinct, hemisphere-asymmetric fronto-parietal networks support attentional gain and reorienting in shaping human conscious experience.
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Affiliation(s)
- Jianghao Liu
- Sorbonne Université, Inserm, CNRS, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France.
- Dassault Systèmes, Vélizy-Villacoublay, France.
| | | | - Alfredo Spagna
- Sorbonne Université, Inserm, CNRS, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
- Department of Psychology, Columbia University in the City of New York, New York, NY, 10027, USA
| | - Jacobo D Sitt
- Sorbonne Université, Inserm, CNRS, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
| | - Alexia Bourgeois
- Laboratory of Cognitive Neurorehabilitation, Faculty of Medicine, University of Geneva, 1206, Geneva, Switzerland
| | - Katia Lehongre
- CENIR - Centre de Neuro-Imagerie de Recherche, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
| | - Sara Fernandez-Vidal
- CENIR - Centre de Neuro-Imagerie de Recherche, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
| | - Claude Adam
- Epilepsy Unit, AP-HP, Pitié-Salpêtrière Hospital, 75013, Paris, France
| | - Virginie Lambrecq
- Sorbonne Université, Inserm, CNRS, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
- Epilepsy Unit, AP-HP, Pitié-Salpêtrière Hospital, 75013, Paris, France
- Clinical Neurophysiology Department, AP-HP, Pitié-Salpêtrière Hospital, 75013, Paris, France
| | - Vincent Navarro
- Sorbonne Université, Inserm, CNRS, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
- Epilepsy Unit, AP-HP, Pitié-Salpêtrière Hospital, 75013, Paris, France
- Clinical Neurophysiology Department, AP-HP, Pitié-Salpêtrière Hospital, 75013, Paris, France
| | - Tal Seidel Malkinson
- Sorbonne Université, Inserm, CNRS, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France.
- CNRS, CRAN, Université de Lorraine, F-54000, Nancy, France.
| | - Paolo Bartolomeo
- Sorbonne Université, Inserm, CNRS, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France.
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70
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Hutt A, Rich S, Valiante TA, Lefebvre J. Intrinsic neural diversity quenches the dynamic volatility of neural networks. Proc Natl Acad Sci U S A 2023; 120:e2218841120. [PMID: 37399421 PMCID: PMC10334753 DOI: 10.1073/pnas.2218841120] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 05/19/2023] [Indexed: 07/05/2023] Open
Abstract
Heterogeneity is the norm in biology. The brain is no different: Neuronal cell types are myriad, reflected through their cellular morphology, type, excitability, connectivity motifs, and ion channel distributions. While this biophysical diversity enriches neural systems' dynamical repertoire, it remains challenging to reconcile with the robustness and persistence of brain function over time (resilience). To better understand the relationship between excitability heterogeneity (variability in excitability within a population of neurons) and resilience, we analyzed both analytically and numerically a nonlinear sparse neural network with balanced excitatory and inhibitory connections evolving over long time scales. Homogeneous networks demonstrated increases in excitability, and strong firing rate correlations-signs of instability-in response to a slowly varying modulatory fluctuation. Excitability heterogeneity tuned network stability in a context-dependent way by restraining responses to modulatory challenges and limiting firing rate correlations, while enriching dynamics during states of low modulatory drive. Excitability heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in population size, connection probability, strength and variability of synaptic weights, by quenching the volatility (i.e., its susceptibility to critical transitions) of its dynamics. Together, these results highlight the fundamental role played by cell-to-cell heterogeneity in the robustness of brain function in the face of change.
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Affiliation(s)
- Axel Hutt
- Université de Strasbourg, CNRS, Inria, ICube, MLMS, MIMESIS, StrasbourgF-67000, France
| | - Scott Rich
- Krembil Brain Institute, Division of Clinical and Computational Neuroscience, University Health Network, Toronto, ONM5T 0S8, Canada
| | - Taufik A. Valiante
- Krembil Brain Institute, Division of Clinical and Computational Neuroscience, University Health Network, Toronto, ONM5T 0S8, Canada
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ONM5S 3G8, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ONM5S 3G9, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ONM5S 1A8, Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ONM5G 2C4, Canada
- Center for Advancing Neurotechnological Innovation to Application, University of Toronto, Toronto, ONM5G 2A2, Canada
- Max Planck-University of Toronto Center for Neural Science and Technology, University of Toronto, Toronto, ONM5S 3G8, Canada
| | - Jérémie Lefebvre
- Krembil Brain Institute, Division of Clinical and Computational Neuroscience, University Health Network, Toronto, ONM5T 0S8, Canada
- Department of Biology, University of Ottawa, Ottawa, ONK1N 6N5, Canada
- Department of Mathematics, University of Toronto, Toronto, ONM5S 2E4, Canada
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71
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Low IIC, Giocomo LM, Williams AH. Remapping in a recurrent neural network model of navigation and context inference. eLife 2023; 12:RP86943. [PMID: 37410093 PMCID: PMC10328512 DOI: 10.7554/elife.86943] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/07/2023] Open
Abstract
Neurons in navigational brain regions provide information about position, orientation, and speed relative to environmental landmarks. These cells also change their firing patterns ('remap') in response to changing contextual factors such as environmental cues, task conditions, and behavioral states, which influence neural activity throughout the brain. How can navigational circuits preserve their local computations while responding to global context changes? To investigate this question, we trained recurrent neural network models to track position in simple environments while at the same time reporting transiently-cued context changes. We show that these combined task constraints (navigation and context inference) produce activity patterns that are qualitatively similar to population-wide remapping in the entorhinal cortex, a navigational brain region. Furthermore, the models identify a solution that generalizes to more complex navigation and inference tasks. We thus provide a simple, general, and experimentally-grounded model of remapping as one neural circuit performing both navigation and context inference.
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Affiliation(s)
- Isabel IC Low
- Zuckerman Mind Brain Behavior Institute, Columbia UniversityNew YorkUnited States
| | - Lisa M Giocomo
- Department of Neurobiology, Stanford UniversityStanfordUnited States
| | - Alex H Williams
- Center for Computational Neuroscience, Flatiron InstituteNew YorkUnited States
- Center for Neural Science, New York UniversityNew YorkUnited States
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72
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Xue X, Wimmer RD, Halassa MM, Chen ZS. Spiking Recurrent Neural Networks Represent Task-Relevant Neural Sequences in Rule-Dependent Computation. Cognit Comput 2023; 15:1167-1189. [PMID: 37771569 PMCID: PMC10530699 DOI: 10.1007/s12559-022-09994-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 01/13/2022] [Indexed: 11/28/2022]
Abstract
Background Prefrontal cortical neurons play essential roles in performing rule-dependent tasks and working memory-based decision making. Methods Motivated by PFG recordings of task-performing mice, we developed an excitatory-inhibitory spiking recurrent neural network (SRNN) to perform a rule-dependent two-alternative forced choice (2AFC) task. We imposed several important biological constraints onto the SRNN, and adapted spike frequency adaptation (SFA) and SuperSpike gradient methods to train the SRNN efficiently. Results The trained SRNN produced emergent rule-specific tunings in single-unit representations, showing rule-dependent population dynamics that resembled experimentally observed data. Under varying test conditions, we manipulated the SRNN parameters or configuration in computer simulations, and we investigated the impacts of rule-coding error, delay duration, recurrent weight connectivity and sparsity, and excitation/inhibition (E/I) balance on both task performance and neural representations. Conclusions Overall, our modeling study provides a computational framework to understand neuronal representations at a fine timescale during working memory and cognitive control, and provides new experimentally testable hypotheses in future experiments.
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Affiliation(s)
- Xiaohe Xue
- Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
| | - Ralf D. Wimmer
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Michael M. Halassa
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Zhe Sage Chen
- Department of Psychiatry, New York University School of Medicine, New York, NY, USA
- Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, USA
- Neuroscience Institute, New York University School of Medicine, New York, NY, USA
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73
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Zhou S, Seay M, Taxidis J, Golshani P, Buonomano DV. Multiplexing working memory and time in the trajectories of neural networks. Nat Hum Behav 2023; 7:1170-1184. [PMID: 37081099 PMCID: PMC10913811 DOI: 10.1038/s41562-023-01592-y] [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: 11/08/2022] [Accepted: 03/22/2023] [Indexed: 04/22/2023]
Abstract
Working memory (WM) and timing are generally considered distinct cognitive functions, but similar neural signatures have been implicated in both. To explore the hypothesis that WM and timing may rely on shared neural mechanisms, we used psychophysical tasks that contained either task-irrelevant timing or WM components. In both cases, the task-irrelevant component influenced performance. We then developed recurrent neural network (RNN) simulations that revealed that cue-specific neural sequences, which multiplexed WM and time, emerged as the dominant regime that captured the behavioural findings. During training, RNN dynamics transitioned from low-dimensional ramps to high-dimensional neural sequences, and depending on task requirements, steady-state or ramping activity was also observed. Analysis of RNN structure revealed that neural sequences relied primarily on inhibitory connections, and could survive the deletion of all excitatory-to-excitatory connections. Our results indicate that in some instances WM is encoded in time-varying neural activity because of the importance of predicting when WM will be used.
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Affiliation(s)
- Shanglin Zhou
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Michael Seay
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - Jiannis Taxidis
- Program in Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Physiology, University of Toronto, Toronto, Ontario, Canada
| | - Peyman Golshani
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Integrative Center for Learning and Memory, Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Semel Institute for Neuroscience and Behavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
- West Los Angeles VA Medical Center, Los Angeles, CA, USA
| | - Dean V Buonomano
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Department of Psychology, University of California, Los Angeles, CA, USA.
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
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74
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Mill RD, Cole MW. Neural representation dynamics reveal computational principles of cognitive task learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.27.546751. [PMID: 37425922 PMCID: PMC10327096 DOI: 10.1101/2023.06.27.546751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
During cognitive task learning, neural representations must be rapidly constructed for novel task performance, then optimized for robust practiced task performance. How the geometry of neural representations changes to enable this transition from novel to practiced performance remains unknown. We hypothesized that practice involves a shift from compositional representations (task-general activity patterns that can be flexibly reused across tasks) to conjunctive representations (task-specific activity patterns specialized for the current task). Functional MRI during learning of multiple complex tasks substantiated this dynamic shift from compositional to conjunctive representations, which was associated with reduced cross-task interference (via pattern separation) and behavioral improvement. Further, we found that conjunctions originated in subcortex (hippocampus and cerebellum) and slowly spread to cortex, extending multiple memory systems theories to encompass task representation learning. The formation of conjunctive representations hence serves as a computational signature of learning, reflecting cortical-subcortical dynamics that optimize task representations in the human brain.
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75
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Feather J, Chung S. Unveiling the benefits of multitasking in disentangled representation formation. Trends Cogn Sci 2023:S1364-6613(23)00127-4. [PMID: 37357063 DOI: 10.1016/j.tics.2023.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 05/22/2023] [Indexed: 06/27/2023]
Abstract
Johnston and Fusi recently investigated the emergence of disentangled representations when a neural network was trained to perform multiple simultaneous tasks. Such experiments explore the benefits of flexible representations and add to a growing field of research investigating the representational geometry of artificial and biological neural networks.
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Affiliation(s)
- Jenelle Feather
- Center for Computational Neuroscience, Flatiron Institute, NY, USA; Center for Neural Science, New York University, NY, USA
| | - SueYeon Chung
- Center for Computational Neuroscience, Flatiron Institute, NY, USA; Center for Neural Science, New York University, NY, USA.
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76
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Kay K, Bonnen K, Denison RN, Arcaro MJ, Barack DL. Tasks and their role in visual neuroscience. Neuron 2023; 111:1697-1713. [PMID: 37040765 DOI: 10.1016/j.neuron.2023.03.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/13/2023] [Accepted: 03/15/2023] [Indexed: 04/13/2023]
Abstract
Vision is widely used as a model system to gain insights into how sensory inputs are processed and interpreted by the brain. Historically, careful quantification and control of visual stimuli have served as the backbone of visual neuroscience. There has been less emphasis, however, on how an observer's task influences the processing of sensory inputs. Motivated by diverse observations of task-dependent activity in the visual system, we propose a framework for thinking about tasks, their role in sensory processing, and how we might formally incorporate tasks into our models of vision.
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Affiliation(s)
- Kendrick Kay
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Kathryn Bonnen
- School of Optometry, Indiana University, Bloomington, IN 47405, USA
| | - Rachel N Denison
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA
| | - Mike J Arcaro
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19146, USA
| | - David L Barack
- Departments of Neuroscience and Philosophy, University of Pennsylvania, Philadelphia, PA 19146, USA
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77
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Langdon C, Genkin M, Engel TA. A unifying perspective on neural manifolds and circuits for cognition. Nat Rev Neurosci 2023; 24:363-377. [PMID: 37055616 PMCID: PMC11058347 DOI: 10.1038/s41583-023-00693-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/06/2023] [Indexed: 04/15/2023]
Abstract
Two different perspectives have informed efforts to explain the link between the brain and behaviour. One approach seeks to identify neural circuit elements that carry out specific functions, emphasizing connectivity between neurons as a substrate for neural computations. Another approach centres on neural manifolds - low-dimensional representations of behavioural signals in neural population activity - and suggests that neural computations are realized by emergent dynamics. Although manifolds reveal an interpretable structure in heterogeneous neuronal activity, finding the corresponding structure in connectivity remains a challenge. We highlight examples in which establishing the correspondence between low-dimensional activity and connectivity has been possible, unifying the neural manifold and circuit perspectives. This relationship is conspicuous in systems in which the geometry of neural responses mirrors their spatial layout in the brain, such as the fly navigational system. Furthermore, we describe evidence that, in systems in which neural responses are heterogeneous, the circuit comprises interactions between activity patterns on the manifold via low-rank connectivity. We suggest that unifying the manifold and circuit approaches is important if we are to be able to causally test theories about the neural computations that underlie behaviour.
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Affiliation(s)
- Christopher Langdon
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Mikhail Genkin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Tatiana A Engel
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
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78
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Costello JT, Temmar H, Cubillos LH, Mender MJ, Wallace DM, Willsey MS, Patil PG, Chestek CA. Balancing Memorization and Generalization in RNNs for High Performance Brain-Machine Interfaces. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.28.542435. [PMID: 37292755 PMCID: PMC10245969 DOI: 10.1101/2023.05.28.542435] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Brain-machine interfaces (BMIs) can restore motor function to people with paralysis but are currently limited by the accuracy of real-time decoding algorithms. Recurrent neural networks (RNNs) using modern training techniques have shown promise in accurately predicting movements from neural signals but have yet to be rigorously evaluated against other decoding algorithms in a closed-loop setting. Here we compared RNNs to other neural network architectures in real-time, continuous decoding of finger movements using intracortical signals from nonhuman primates. Across one and two finger online tasks, LSTMs (a type of RNN) outperformed convolutional and transformer-based neural networks, averaging 18% higher throughput than the convolution network. On simplified tasks with a reduced movement set, RNN decoders were allowed to memorize movement patterns and matched able-bodied control. Performance gradually dropped as the number of distinct movements increased but did not go below fully continuous decoder performance. Finally, in a two-finger task where one degree-of-freedom had poor input signals, we recovered functional control using RNNs trained to act both like a movement classifier and continuous decoder. Our results suggest that RNNs can enable functional real-time BMI control by learning and generating accurate movement patterns.
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79
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Han CZ, Donoghue T, Cao R, Kunz L, Wang S, Jacobs J. Using multi-task experiments to test principles of hippocampal function. Hippocampus 2023; 33:646-657. [PMID: 37042212 PMCID: PMC10249632 DOI: 10.1002/hipo.23540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 03/30/2023] [Accepted: 03/31/2023] [Indexed: 04/13/2023]
Abstract
Investigations of hippocampal functions have revealed a dizzying array of findings, from lesion-based behavioral deficits, to a diverse range of characterized neural activations, to computational models of putative functionality. Across these findings, there remains an ongoing debate about the core function of the hippocampus and the generality of its representation. Researchers have debated whether the hippocampus's primary role relates to the representation of space, the neural basis of (episodic) memory, or some more general computation that generalizes across various cognitive domains. Within these different perspectives, there is much debate about the nature of feature encodings. Here, we suggest that in order to evaluate hippocampal responses-investigating, for example, whether neuronal representations are narrowly targeted to particular tasks or if they subserve domain-general purposes-a promising research strategy may be the use of multi-task experiments, or more generally switching between multiple task contexts while recording from the same neurons in a given session. We argue that this strategy-when combined with explicitly defined theoretical motivations that guide experiment design-could be a fruitful approach to better understand how hippocampal representations support different behaviors. In doing so, we briefly review key open questions in the field, as exemplified by articles in this special issue, as well as previous work using multi-task experiments, and extrapolate to consider how this strategy could be further applied to probe fundamental questions about hippocampal function.
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Affiliation(s)
- Claire Z. Han
- Department of Biomedical Engineering, Columbia University
| | | | - Runnan Cao
- Department of Radiology, Washington University in St. Louis
| | - Lukas Kunz
- Department of Epileptology, University of Bonn Medical Center, Bonn, Germany
| | - Shuo Wang
- Department of Radiology, Washington University in St. Louis
| | - Joshua Jacobs
- Department of Biomedical Engineering, Columbia University
- Department of Neurological Surgery, Columbia University
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80
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Hanganu-Opatz IL, Klausberger T, Sigurdsson T, Nieder A, Jacob SN, Bartos M, Sauer JF, Durstewitz D, Leibold C, Diester I. Resolving the prefrontal mechanisms of adaptive cognitive behaviors: A cross-species perspective. Neuron 2023; 111:1020-1036. [PMID: 37023708 DOI: 10.1016/j.neuron.2023.03.017] [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: 11/04/2022] [Revised: 02/15/2023] [Accepted: 03/10/2023] [Indexed: 04/08/2023]
Abstract
The prefrontal cortex (PFC) enables a staggering variety of complex behaviors, such as planning actions, solving problems, and adapting to new situations according to external information and internal states. These higher-order abilities, collectively defined as adaptive cognitive behavior, require cellular ensembles that coordinate the tradeoff between the stability and flexibility of neural representations. While the mechanisms underlying the function of cellular ensembles are still unclear, recent experimental and theoretical studies suggest that temporal coordination dynamically binds prefrontal neurons into functional ensembles. A so far largely separate stream of research has investigated the prefrontal efferent and afferent connectivity. These two research streams have recently converged on the hypothesis that prefrontal connectivity patterns influence ensemble formation and the function of neurons within ensembles. Here, we propose a unitary concept that, leveraging a cross-species definition of prefrontal regions, explains how prefrontal ensembles adaptively regulate and efficiently coordinate multiple processes in distinct cognitive behaviors.
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Affiliation(s)
- Ileana L Hanganu-Opatz
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Thomas Klausberger
- Center for Brain Research, Division of Cognitive Neurobiology, Medical University of Vienna, Vienna, Austria
| | - Torfi Sigurdsson
- Institute of Neurophysiology, Goethe University, Frankfurt, Germany
| | - Andreas Nieder
- Animal Physiology Unit, Institute of Neurobiology, University of Tübingen, 72076 Tübingen, Germany
| | - Simon N Jacob
- Translational Neurotechnology Laboratory, Department of Neurosurgery, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Marlene Bartos
- Institute for Physiology I, Medical Faculty, University of Freiburg, Freiburg im Breisgau, Germany
| | - Jonas-Frederic Sauer
- Institute for Physiology I, Medical Faculty, University of Freiburg, Freiburg im Breisgau, Germany
| | - Daniel Durstewitz
- Department of Theoretical Neuroscience, Central Institute of Mental Health & Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Christian Leibold
- Faculty of Biology, Bernstein Center Freiburg, BrainLinks-BrainTools, University of Freiburg, Freiburg im Breisgau, Germany
| | - Ilka Diester
- Optophysiology - Optogenetics and Neurophysiology, IMBIT // BrainLinks-BrainTools, University of Freiburg, Freiburg im Breisgau, Germany.
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81
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Avcu E, Hwang M, Brown KS, Gow DW. A tale of two lexica: Investigating computational pressures on word representation with neural networks. Front Artif Intell 2023; 6:1062230. [PMID: 37051161 PMCID: PMC10083378 DOI: 10.3389/frai.2023.1062230] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 03/10/2023] [Indexed: 03/28/2023] Open
Abstract
Introduction The notion of a single localized store of word representations has become increasingly less plausible as evidence has accumulated for the widely distributed neural representation of wordform grounded in motor, perceptual, and conceptual processes. Here, we attempt to combine machine learning methods and neurobiological frameworks to propose a computational model of brain systems potentially responsible for wordform representation. We tested the hypothesis that the functional specialization of word representation in the brain is driven partly by computational optimization. This hypothesis directly addresses the unique problem of mapping sound and articulation vs. mapping sound and meaning. Results We found that artificial neural networks trained on the mapping between sound and articulation performed poorly in recognizing the mapping between sound and meaning and vice versa. Moreover, a network trained on both tasks simultaneously could not discover the features required for efficient mapping between sound and higher-level cognitive states compared to the other two models. Furthermore, these networks developed internal representations reflecting specialized task-optimized functions without explicit training. Discussion Together, these findings demonstrate that different task-directed representations lead to more focused responses and better performance of a machine or algorithm and, hypothetically, the brain. Thus, we imply that the functional specialization of word representation mirrors a computational optimization strategy given the nature of the tasks that the human brain faces.
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Affiliation(s)
- Enes Avcu
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | | | - Kevin Scott Brown
- Department of Pharmaceutical Sciences and School of Chemical, Biological, and Environmental Engineering, Oregon State University, Corvallis, OR, United States
| | - David W. Gow
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital, Charlestown, MA, United States
- Department of Psychology, Salem State University, Salem, MA, United States
- Harvard-MIT Division of Health Sciences and Technology, Boston, MA, United States
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82
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Soda T, Ahmadi A, Tani J, Honda M, Hanakawa T, Yamashita Y. Simulating developmental diversity: Impact of neural stochasticity on atypical flexibility and hierarchy. Front Psychiatry 2023; 14:1080668. [PMID: 37009124 PMCID: PMC10050443 DOI: 10.3389/fpsyt.2023.1080668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 02/21/2023] [Indexed: 03/17/2023] Open
Abstract
Introduction Investigating the pathological mechanisms of developmental disorders is a challenge because the symptoms are a result of complex and dynamic factors such as neural networks, cognitive behavior, environment, and developmental learning. Recently, computational methods have started to provide a unified framework for understanding developmental disorders, enabling us to describe the interactions among those multiple factors underlying symptoms. However, this approach is still limited because most studies to date have focused on cross-sectional task performance and lacked the perspectives of developmental learning. Here, we proposed a new research method for understanding the mechanisms of the acquisition and its failures in hierarchical Bayesian representations using a state-of-the-art computational model, referred to as in silico neurodevelopment framework for atypical representation learning. Methods Simple simulation experiments were conducted using the proposed framework to examine whether manipulating the neural stochasticity and noise levels in external environments during the learning process can lead to the altered acquisition of hierarchical Bayesian representation and reduced flexibility. Results Networks with normal neural stochasticity acquired hierarchical representations that reflected the underlying probabilistic structures in the environment, including higher-order representation, and exhibited good behavioral and cognitive flexibility. When the neural stochasticity was high during learning, top-down generation using higher-order representation became atypical, although the flexibility did not differ from that of the normal stochasticity settings. However, when the neural stochasticity was low in the learning process, the networks demonstrated reduced flexibility and altered hierarchical representation. Notably, this altered acquisition of higher-order representation and flexibility was ameliorated by increasing the level of noises in external stimuli. Discussion These results demonstrated that the proposed method assists in modeling developmental disorders by bridging between multiple factors, such as the inherent characteristics of neural dynamics, acquisitions of hierarchical representation, flexible behavior, and external environment.
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Affiliation(s)
- Takafumi Soda
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
- Department of NCNP Brain Physiology and Pathology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | | | - Jun Tani
- Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Manabu Honda
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Takashi Hanakawa
- Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yuichi Yamashita
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
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83
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Pugavko MM, Maslennikov OV, Nekorkin VI. Multitask computation through dynamics in recurrent spiking neural networks. Sci Rep 2023; 13:3997. [PMID: 36899052 PMCID: PMC10006454 DOI: 10.1038/s41598-023-31110-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 03/06/2023] [Indexed: 03/12/2023] Open
Abstract
In this work, inspired by cognitive neuroscience experiments, we propose recurrent spiking neural networks trained to perform multiple target tasks. These models are designed by considering neurocognitive activity as computational processes through dynamics. Trained by input-output examples, these spiking neural networks are reverse engineered to find the dynamic mechanisms that are fundamental to their performance. We show that considering multitasking and spiking within one system provides insightful ideas on the principles of neural computation.
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Affiliation(s)
- Mechislav M Pugavko
- Institute of Applied Physics of the Russian Academy of Sciences, Nizhny Novgorod, 603950, Russia
| | - Oleg V Maslennikov
- Institute of Applied Physics of the Russian Academy of Sciences, Nizhny Novgorod, 603950, Russia.
| | - Vladimir I Nekorkin
- Institute of Applied Physics of the Russian Academy of Sciences, Nizhny Novgorod, 603950, Russia
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84
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DePasquale B, Sussillo D, Abbott LF, Churchland MM. The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks. Neuron 2023; 111:631-649.e10. [PMID: 36630961 PMCID: PMC10118067 DOI: 10.1016/j.neuron.2022.12.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 06/17/2022] [Accepted: 12/05/2022] [Indexed: 01/12/2023]
Abstract
Neural activity is often described in terms of population-level factors extracted from the responses of many neurons. Factors provide a lower-dimensional description with the aim of shedding light on network computations. Yet, mechanistically, computations are performed not by continuously valued factors but by interactions among neurons that spike discretely and variably. Models provide a means of bridging these levels of description. We developed a general method for training model networks of spiking neurons by leveraging factors extracted from either data or firing-rate-based networks. In addition to providing a useful model-building framework, this formalism illustrates how reliable and continuously valued factors can arise from seemingly stochastic spiking. Our framework establishes procedures for embedding this property in network models with different levels of realism. The relationship between spikes and factors in such networks provides a foundation for interpreting (and subtly redefining) commonly used quantities such as firing rates.
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Affiliation(s)
- Brian DePasquale
- Princeton Neuroscience Institute, Princeton University, Princeton NJ, USA; Department of Neuroscience, Columbia University, New York, NY, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY, USA.
| | - David Sussillo
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - L F Abbott
- Department of Neuroscience, Columbia University, New York, NY, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA; Department of Physiology and Cellular Biophysics, Columbia University, New York, NY, USA; Kavli Institute for Brain Science, Columbia University, New York, NY, USA
| | - Mark M Churchland
- Department of Neuroscience, Columbia University, New York, NY, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA; Kavli Institute for Brain Science, Columbia University, New York, NY, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
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85
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Frisby SL, Halai AD, Cox CR, Lambon Ralph MA, Rogers TT. Decoding semantic representations in mind and brain. Trends Cogn Sci 2023; 27:258-281. [PMID: 36631371 DOI: 10.1016/j.tics.2022.12.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 12/12/2022] [Accepted: 12/13/2022] [Indexed: 01/11/2023]
Abstract
A key goal for cognitive neuroscience is to understand the neurocognitive systems that support semantic memory. Recent multivariate analyses of neuroimaging data have contributed greatly to this effort, but the rapid development of these novel approaches has made it difficult to track the diversity of findings and to understand how and why they sometimes lead to contradictory conclusions. We address this challenge by reviewing cognitive theories of semantic representation and their neural instantiation. We then consider contemporary approaches to neural decoding and assess which types of representation each can possibly detect. The analysis suggests why the results are heterogeneous and identifies crucial links between cognitive theory, data collection, and analysis that can help to better connect neuroimaging to mechanistic theories of semantic cognition.
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Affiliation(s)
- Saskia L Frisby
- Medical Research Council (MRC) Cognition and Brain Sciences Unit, Chaucer Road, Cambridge CB2 7EF, UK.
| | - Ajay D Halai
- Medical Research Council (MRC) Cognition and Brain Sciences Unit, Chaucer Road, Cambridge CB2 7EF, UK
| | - Christopher R Cox
- Department of Psychology, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Matthew A Lambon Ralph
- Medical Research Council (MRC) Cognition and Brain Sciences Unit, Chaucer Road, Cambridge CB2 7EF, UK
| | - Timothy T Rogers
- Department of Psychology, University of Wisconsin-Madison, 1202 West Johnson Street, Madison, WI 53706, USA.
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86
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Kanwisher N, Khosla M, Dobs K. Using artificial neural networks to ask 'why' questions of minds and brains. Trends Neurosci 2023; 46:240-254. [PMID: 36658072 DOI: 10.1016/j.tins.2022.12.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/29/2022] [Accepted: 12/22/2022] [Indexed: 01/19/2023]
Abstract
Neuroscientists have long characterized the properties and functions of the nervous system, and are increasingly succeeding in answering how brains perform the tasks they do. But the question 'why' brains work the way they do is asked less often. The new ability to optimize artificial neural networks (ANNs) for performance on human-like tasks now enables us to approach these 'why' questions by asking when the properties of networks optimized for a given task mirror the behavioral and neural characteristics of humans performing the same task. Here we highlight the recent success of this strategy in explaining why the visual and auditory systems work the way they do, at both behavioral and neural levels.
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Affiliation(s)
- Nancy Kanwisher
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Meenakshi Khosla
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Katharina Dobs
- Department of Psychology, Justus Liebig University Giessen, Giessen, Germany; Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University, Giessen, Germany.
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87
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Recurrent networks endowed with structural priors explain suboptimal animal behavior. Curr Biol 2023; 33:622-638.e7. [PMID: 36657448 DOI: 10.1016/j.cub.2022.12.044] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 10/03/2022] [Accepted: 12/16/2022] [Indexed: 01/19/2023]
Abstract
The strategies found by animals facing a new task are determined both by individual experience and by structural priors evolved to leverage the statistics of natural environments. Rats quickly learn to capitalize on the trial sequence correlations of two-alternative forced choice (2AFC) tasks after correct trials but consistently deviate from optimal behavior after error trials. To understand this outcome-dependent gating, we first show that recurrent neural networks (RNNs) trained in the same 2AFC task outperform rats as they can readily learn to use across-trial information both after correct and error trials. We hypothesize that, although RNNs can optimize their behavior in the 2AFC task without any a priori restrictions, rats' strategy is constrained by a structural prior adapted to a natural environment in which rewarded and non-rewarded actions provide largely asymmetric information. When pre-training RNNs in a more ecological task with more than two possible choices, networks develop a strategy by which they gate off the across-trial evidence after errors, mimicking rats' behavior. Population analyses show that the pre-trained networks form an accurate representation of the sequence statistics independently of the outcome in the previous trial. After error trials, gating is implemented by a change in the network dynamics that temporarily decouple the categorization of the stimulus from the across-trial accumulated evidence. Our results suggest that the rats' suboptimal behavior reflects the influence of a structural prior that reacts to errors by isolating the network decision dynamics from the context, ultimately constraining the performance in a 2AFC laboratory task.
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88
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Aitken K, Mihalas S. Neural population dynamics of computing with synaptic modulations. eLife 2023; 12:e83035. [PMID: 36820526 PMCID: PMC10072874 DOI: 10.7554/elife.83035] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 02/22/2023] [Indexed: 02/24/2023] Open
Abstract
In addition to long-timescale rewiring, synapses in the brain are subject to significant modulation that occurs at faster timescales that endow the brain with additional means of processing information. Despite this, models of the brain like recurrent neural networks (RNNs) often have their weights frozen after training, relying on an internal state stored in neuronal activity to hold task-relevant information. In this work, we study the computational potential and resulting dynamics of a network that relies solely on synapse modulation during inference to process task-relevant information, the multi-plasticity network (MPN). Since the MPN has no recurrent connections, this allows us to study the computational capabilities and dynamical behavior contributed by synapses modulations alone. The generality of the MPN allows for our results to apply to synaptic modulation mechanisms ranging from short-term synaptic plasticity (STSP) to slower modulations such as spike-time dependent plasticity (STDP). We thoroughly examine the neural population dynamics of the MPN trained on integration-based tasks and compare it to known RNN dynamics, finding the two to have fundamentally different attractor structure. We find said differences in dynamics allow the MPN to outperform its RNN counterparts on several neuroscience-relevant tests. Training the MPN across a battery of neuroscience tasks, we find its computational capabilities in such settings is comparable to networks that compute with recurrent connections. Altogether, we believe this work demonstrates the computational possibilities of computing with synaptic modulations and highlights important motifs of these computations so that they can be identified in brain-like systems.
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Affiliation(s)
- Kyle Aitken
- Allen Institute, MindScope ProgramSeattleUnited States
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89
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Johnston WJ, Fusi S. Abstract representations emerge naturally in neural networks trained to perform multiple tasks. Nat Commun 2023; 14:1040. [PMID: 36823136 PMCID: PMC9950464 DOI: 10.1038/s41467-023-36583-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 02/07/2023] [Indexed: 02/25/2023] Open
Abstract
Humans and other animals demonstrate a remarkable ability to generalize knowledge across distinct contexts and objects during natural behavior. We posit that this ability to generalize arises from a specific representational geometry, that we call abstract and that is referred to as disentangled in machine learning. These abstract representations have been observed in recent neurophysiological studies. However, it is unknown how they emerge. Here, using feedforward neural networks, we demonstrate that the learning of multiple tasks causes abstract representations to emerge, using both supervised and reinforcement learning. We show that these abstract representations enable few-sample learning and reliable generalization on novel tasks. We conclude that abstract representations of sensory and cognitive variables may emerge from the multiple behaviors that animals exhibit in the natural world, and, as a consequence, could be pervasive in high-level brain regions. We also make several specific predictions about which variables will be represented abstractly.
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Affiliation(s)
- W Jeffrey Johnston
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA. .,Mortimer B. Zuckerman Mind, Brain and Behavior Institute, Columbia University, New York, NY, USA.
| | - Stefano Fusi
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA. .,Mortimer B. Zuckerman Mind, Brain and Behavior Institute, Columbia University, New York, NY, USA.
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90
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Abstract
Neural mechanisms of perceptual decision making have been extensively studied in experimental settings that mimic stable environments with repeating stimuli, fixed rules, and payoffs. In contrast, we live in an ever-changing environment and have varying goals and behavioral demands. To accommodate variability, our brain flexibly adjusts decision-making processes depending on context. Here, we review a growing body of research that explores the neural mechanisms underlying this flexibility. We highlight diverse forms of context dependency in decision making implemented through a variety of neural computations. Context-dependent neural activity is observed in a distributed network of brain structures, including posterior parietal, sensory, motor, and subcortical regions, as well as the prefrontal areas classically implicated in cognitive control. We propose that investigating the distributed network underlying flexible decisions is key to advancing our understanding and discuss a path forward for experimental and theoretical investigations.
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Affiliation(s)
- Gouki Okazawa
- Center for Neural Science, New York University, New York, NY, USA;
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Roozbeh Kiani
- Center for Neural Science, New York University, New York, NY, USA;
- Department of Psychology, New York University, New York, NY, USA
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91
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Brain-inspired multisensory integration neural network for cross-modal recognition through spatiotemporal dynamics and deep learning. Cogn Neurodyn 2023. [DOI: 10.1007/s11571-023-09932-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
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92
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Ito T, Murray JD. Multitask representations in the human cortex transform along a sensory-to-motor hierarchy. Nat Neurosci 2023; 26:306-315. [PMID: 36536240 DOI: 10.1038/s41593-022-01224-0] [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: 12/02/2021] [Accepted: 10/28/2022] [Indexed: 12/24/2022]
Abstract
Human cognition recruits distributed neural processes, yet the organizing computational and functional architectures remain unclear. Here, we characterized the geometry and topography of multitask representations across the human cortex using functional magnetic resonance imaging during 26 cognitive tasks in the same individuals. We measured the representational similarity across tasks within a region and the alignment of representations between regions. Representational alignment varied in a graded manner along the sensory-association-motor axis. Multitask dimensionality exhibited compression then expansion along this gradient. To investigate computational principles of multitask representations, we trained multilayer neural network models to transform empirical visual-to-motor representations. Compression-then-expansion organization in models emerged exclusively in a rich training regime, which is associated with learning optimized representations that are robust to noise. This regime produces hierarchically structured representations similar to empirical cortical patterns. Together, these results reveal computational principles that organize multitask representations across the human cortex to support multitask cognition.
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Affiliation(s)
- Takuya Ito
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - John D Murray
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA.
- Department of Physics, Yale University, New Haven, CT, USA.
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93
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Thivierge JP, Giraud É, Lynn M. Toward a Brain-Inspired Theory of Artificial Learning. Cognit Comput 2023. [DOI: 10.1007/s12559-023-10121-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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94
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Momennejad I. A rubric for human-like agents and NeuroAI. Philos Trans R Soc Lond B Biol Sci 2023; 378:20210446. [PMID: 36511409 PMCID: PMC9745874 DOI: 10.1098/rstb.2021.0446] [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: 07/17/2022] [Accepted: 10/27/2022] [Indexed: 12/15/2022] Open
Abstract
Researchers across cognitive, neuro- and computer sciences increasingly reference 'human-like' artificial intelligence and 'neuroAI'. However, the scope and use of the terms are often inconsistent. Contributed research ranges widely from mimicking behaviour, to testing machine learning methods as neurally plausible hypotheses at the cellular or functional levels, or solving engineering problems. However, it cannot be assumed nor expected that progress on one of these three goals will automatically translate to progress in others. Here, a simple rubric is proposed to clarify the scope of individual contributions, grounded in their commitments to human-like behaviour, neural plausibility or benchmark/engineering/computer science goals. This is clarified using examples of weak and strong neuroAI and human-like agents, and discussing the generative, corroborate and corrective ways in which the three dimensions interact with one another. The author maintains that future progress in artificial intelligence will need strong interactions across the disciplines, with iterative feedback loops and meticulous validity tests-leading to both known and yet-unknown advances that may span decades to come. This article is part of a discussion meeting issue 'New approaches to 3D vision'.
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Affiliation(s)
- Ida Momennejad
- Microsoft Research NYC, Reinforcement Learning Station, 300 Lafayette, New York, NY 10012, USA
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95
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Low II, Giocomo LM, Williams AH. Remapping in a recurrent neural network model of navigation and context inference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.25.525596. [PMID: 36747825 PMCID: PMC9900889 DOI: 10.1101/2023.01.25.525596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Neurons in navigational brain regions provide information about position, orientation, and speed relative to environmental landmarks. These cells also change their firing patterns ("remap") in response to changing contextual factors such as environmental cues, task conditions, and behavioral state, which influence neural activity throughout the brain. How can navigational circuits preserve their local computations while responding to global context changes? To investigate this question, we trained recurrent neural network models to track position in simple environments while at the same time reporting transiently-cued context changes. We show that these combined task constraints (navigation and context inference) produce activity patterns that are qualitatively similar to population-wide remapping in the entorhinal cortex, a navigational brain region. Furthermore, the models identify a solution that generalizes to more complex navigation and inference tasks. We thus provide a simple, general, and experimentally-grounded model of remapping as one neural circuit performing both navigation and context inference.
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Affiliation(s)
- Isabel I.C. Low
- Zuckerman Mind Brain Behavior Institute, Columbia University,Center for Computational Neuroscience, Flatiron Institute,Correspondence to: ,
| | | | - Alex H. Williams
- Center for Computational Neuroscience, Flatiron Institute,Center for Neural Science, New York University,Correspondence to: ,
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96
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Foundations of human spatial problem solving. Sci Rep 2023; 13:1485. [PMID: 36707649 PMCID: PMC9883268 DOI: 10.1038/s41598-023-28834-3] [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: 09/09/2022] [Accepted: 01/25/2023] [Indexed: 01/28/2023] Open
Abstract
Despite great strides in both machine learning and neuroscience, we do not know how the human brain solves problems in the general sense. We approach this question by drawing on the framework of engineering control theory. We demonstrate a computational neural model with only localist learning laws that is able to find solutions to arbitrary problems. The model and humans perform a multi-step task with arbitrary and changing starting and desired ending states. Using a combination of computational neural modeling, human fMRI, and representational similarity analysis, we show here that the roles of a number of brain regions can be reinterpreted as interacting mechanisms of a control theoretic system. The results suggest a new set of functional perspectives on the orbitofrontal cortex, hippocampus, basal ganglia, anterior temporal lobe, lateral prefrontal cortex, and visual cortex, as well as a new path toward artificial general intelligence.
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97
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Bray SR, Wyss LS, Chai C, Lozada ME, Wang B. Adaptive robustness through incoherent signaling mechanisms in a regenerative brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.20.523817. [PMID: 36711454 PMCID: PMC9882340 DOI: 10.1101/2023.01.20.523817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Animal behavior emerges from collective dynamics of interconnected neurons, making it vulnerable to connectome damage. Paradoxically, many organisms maintain significant behavioral output after large-scale neural injury. Molecular underpinnings of this extreme robustness remain largely unknown. Here, we develop a quantitative behavioral analysis pipeline to measure previously uncharacterized long-lasting latent memory states in planarian flatworms during whole-brain regeneration. By combining >20,000 animal trials with neural population dynamic modeling, we show that long-range volumetric peptidergic signals allow the planarian to rapidly reestablish latent states and restore coarse behavior after large structural perturbations to the nervous system, while small-molecule neuromodulators gradually refine the precision. The different time and length scales of neuropeptide and small-molecule transmission generate incoherent patterns of neural activity which competitively regulate behavior and memory. Controlling behavior through opposing communication mechanisms creates a more robust system than either alone and may serve as a generic approach to construct robust neural networks.
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Affiliation(s)
- Samuel R. Bray
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Livia S. Wyss
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Chew Chai
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Maria E. Lozada
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Biomedical Engineering, University of Miami, Coral Gables, FL, USA
| | - Bo Wang
- Department of Bioengineering, Stanford University, Stanford, CA, USA
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98
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Takahashi Y, Murata S, Ueki M, Tomita H, Yamashita Y. Interaction between Functional Connectivity and Neural Excitability in Autism: A Novel Framework for Computational Modeling and Application to Biological Data. COMPUTATIONAL PSYCHIATRY (CAMBRIDGE, MASS.) 2023; 7:14-29. [PMID: 38774640 PMCID: PMC11104370 DOI: 10.5334/cpsy.93] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 01/09/2023] [Indexed: 01/22/2023]
Abstract
Functional connectivity (FC) and neural excitability may interact to affect symptoms of autism spectrum disorder (ASD). We tested this hypothesis with neural network simulations, and applied it with functional magnetic resonance imaging (fMRI). A hierarchical recurrent neural network embodying predictive processing theory was subjected to a facial emotion recognition task. Neural network simulations examined the effects of FC and neural excitability on changes in neural representations by developmental learning, and eventually on ASD-like performance. Next, by mapping each neural network condition to subject subgroups on the basis of fMRI parameters, the association between ASD-like performance in the simulation and ASD diagnosis in the corresponding subject subgroup was examined. In the neural network simulation, the more homogeneous the neural excitability of the lower-level network, the more ASD-like the performance (reduced generalization and emotion recognition capability). In addition, in homogeneous networks, the higher the FC, the more ASD-like performance, while in heterogeneous networks, the higher the FC, the less ASD-like performance, demonstrating that FC and neural excitability interact. As an underlying mechanism, neural excitability determines the generalization capability of top-down prediction, and FC determines whether the model's information processing will be top-down prediction-dependent or bottom-up sensory-input dependent. In fMRI datasets, ASD was actually more prevalent in subject subgroups corresponding to the network condition showing ASD-like performance. The current study suggests an interaction between FC and neural excitability, and presents a novel framework for computational modeling and biological application of a developmental learning process underlying cognitive alterations in ASD.
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Affiliation(s)
- Yuta Takahashi
- Department of Psychiatry, Tohoku University Hospital, Japan
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, Japan
- Department of Information Medicine, National Center of Neurology and Psychiatry, Japan
| | - Shingo Murata
- Department of Electronics and Electrical Engineering, Faculty of Science and Technology, Keio University, Japan
| | - Masao Ueki
- School of Information and Data Sciences, Nagasaki University, Japan
| | - Hiroaki Tomita
- Department of Psychiatry, Tohoku University Hospital, Japan
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, Japan
| | - Yuichi Yamashita
- Department of Information Medicine, National Center of Neurology and Psychiatry, Japan
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99
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Jaffe PI, Poldrack RA, Schafer RJ, Bissett PG. Modelling human behaviour in cognitive tasks with latent dynamical systems. Nat Hum Behav 2023:10.1038/s41562-022-01510-8. [PMID: 36658212 DOI: 10.1038/s41562-022-01510-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 12/06/2022] [Indexed: 01/21/2023]
Abstract
Response time data collected from cognitive tasks are a cornerstone of psychology and neuroscience research, yet existing models of these data either make strong assumptions about the data-generating process or are limited to modelling single trials. We introduce task-DyVA, a deep learning framework in which expressive dynamical systems are trained to reproduce sequences of response times observed in data from individual human subjects. Models fitted to a large task-switching dataset captured subject-specific behavioural differences with high temporal precision, including task-switching costs. Through perturbation experiments and analyses of the models' latent dynamics, we find support for a rational account of switch costs in terms of a stability-flexibility trade-off. Thus, our framework can be used to discover interpretable cognitive theories that explain how the brain dynamically gives rise to behaviour.
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Affiliation(s)
- Paul I Jaffe
- Department of Psychology, Stanford University, Stanford, CA, USA. .,Lumos Labs, San Francisco, CA, USA.
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100
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Flesch T, Nagy DG, Saxe A, Summerfield C. Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals. PLoS Comput Biol 2023; 19:e1010808. [PMID: 36656823 PMCID: PMC9851563 DOI: 10.1371/journal.pcbi.1010808] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 12/11/2022] [Indexed: 01/20/2023] Open
Abstract
Humans can learn several tasks in succession with minimal mutual interference but perform more poorly when trained on multiple tasks at once. The opposite is true for standard deep neural networks. Here, we propose novel computational constraints for artificial neural networks, inspired by earlier work on gating in the primate prefrontal cortex, that capture the cost of interleaved training and allow the network to learn two tasks in sequence without forgetting. We augment standard stochastic gradient descent with two algorithmic motifs, so-called "sluggish" task units and a Hebbian training step that strengthens connections between task units and hidden units that encode task-relevant information. We found that the "sluggish" units introduce a switch-cost during training, which biases representations under interleaved training towards a joint representation that ignores the contextual cue, while the Hebbian step promotes the formation of a gating scheme from task units to the hidden layer that produces orthogonal representations which are perfectly guarded against interference. Validating the model on previously published human behavioural data revealed that it matches performance of participants who had been trained on blocked or interleaved curricula, and that these performance differences were driven by misestimation of the true category boundary.
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Affiliation(s)
- Timo Flesch
- Department of Experimental Psychology, University of Oxford; Oxford, United Kingdom
- * E-mail:
| | - David G. Nagy
- Department of Computational Sciences, Wigner Research Centre for Physics; Budapest, Hungary
| | - Andrew Saxe
- Gatsby Computational Neuroscience Unit & Sainsbury Wellcome Centre, University College London; London, United Kingdom
- CIFAR Azrieli Global Scholars program, CIFAR; Toronto, Canada
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