Evolution of neural activity in circuits bridging sensory and abstract knowledge

The ability to associate sensory stimuli with abstract classes is critical for survival. How are these associations implemented in brain circuits? And what governs how neural activity evolves during abstract knowledge acquisition? To investigate these questions, we consider a circuit model that learns to map sensory input to abstract classes via gradient descent synaptic plasticity. We focus on typical neuroscience tasks (simple, and context-dependent, categorization), and study how both synaptic connectivity and neural activity evolve during learning. To make contact with the current generation of experiments, we analyze activity via standard measures such as selectivity, correlations, and tuning symmetry. We find that the model is able to recapitulate experimental observations, including seemingly disparate ones. We determine how, in the model, the behaviour of these measures depends on details of the circuit and the task. These dependencies make experimentally-testable predictions about the circuitry supporting abstract knowledge acquisition in the brain.

[1]  C. Pehlevan,et al.  The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks , 2022, ArXiv.

[2]  Nuo Li,et al.  Thalamus-driven functional populations in frontal cortex support decision-making , 2022, Nature Neuroscience.

[3]  I. Fiete,et al.  How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective , 2021, ICML.

[4]  Jean-Pierre Nadal,et al.  Categorical Perception: A Groundwork for Deep Learning , 2020, Neural Computation.

[5]  S. Ostojic,et al.  The role of population structure in computations through neural dynamics , 2022, Nature Neuroscience.

[6]  Bo Li,et al.  Prefrontal top-down projections control context-dependent strategy selection , 2021, bioRxiv.

[7]  Scott W. Linderman,et al.  Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems , 2021, NeurIPS.

[8]  Kenneth W. Latimer,et al.  Low-dimensional encoding of decisions in parietal cortex reflects long-term training history , 2021, bioRxiv.

[9]  H. Sompolinsky,et al.  A Minimum Perturbation Theory of Deep Perceptual Learning , 2021, bioRxiv.

[10]  T. Bonhoeffer,et al.  Mouse visual cortex areas represent perceptual and semantic features of learned visual categories , 2021, Nature Neuroscience.

[11]  John Lafferty,et al.  Convergence and Alignment of Gradient Descentwith Random Back propagation Weights , 2021, NeurIPS.

[12]  Andrew M. Saxe,et al.  Rich and lazy learning of task representations in brains and neural networks , 2021, bioRxiv.

[13]  T. Bonhoeffer,et al.  Mouse prefrontal cortex represents learned rules for categorization , 2021, Nature.

[14]  Haim Sompolinsky,et al.  Statistical Mechanics of Deep Linear Neural Networks: The Backpropagating Kernel Renormalization , 2020, Physical Review X.

[15]  Kenneth D. Harris,et al.  Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings , 2020, Science.

[16]  C. Pehlevan,et al.  Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks , 2020, Nature Communications.

[17]  Mikhail Belkin,et al.  Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification Tasks , 2020, ICLR.

[18]  Greg Yang,et al.  Feature Learning in Infinite-Width Neural Networks , 2020, ArXiv.

[19]  O. Barak,et al.  Quality of internal representation shapes learning performance in feedback neural networks , 2020, 2011.06066.

[20]  Silvia Bernardi,et al.  The Geometry of Abstraction in the Hippocampus and Prefrontal Cortex , 2020, Cell.

[21]  Mikhail Belkin,et al.  On the linearity of large non-linear models: when and why the tangent kernel is constant , 2020, NeurIPS.

[22]  David L. Donoho,et al.  Prevalence of neural collapse during the terminal phase of deep learning training , 2020, Proceedings of the National Academy of Sciences.

[23]  Nathan Srebro,et al.  Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy , 2020, NeurIPS.

[24]  Jeffrey Pennington,et al.  The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks , 2020, NeurIPS.

[25]  Francesca Mastrogiuseppe,et al.  The interplay between randomness and structure during learning in RNNs , 2020, NeurIPS.

[26]  David J. Freedman,et al.  A neural circuit mechanism of categorical perception: top-down signaling in the primate cortex , 2020, bioRxiv.

[27]  P. Latham,et al.  Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks , 2020, NeurIPS.

[28]  Richard Naud,et al.  Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits , 2020, Nature Neuroscience.

[29]  Christopher Summerfield,et al.  Structure learning and the posterior parietal cortex , 2020, Progress in Neurobiology.

[30]  Jiaoyang Huang,et al.  Dynamics of Deep Neural Networks and Neural Tangent Hierarchy , 2019, ICML.

[31]  Matthieu Wyart,et al.  Disentangling feature and lazy training in deep neural networks , 2019, Journal of Statistical Mechanics: Theory and Experiment.

[32]  Junya Hirokawa,et al.  Frontal cortex neuron types categorically encode single decision variables , 2019, Nature.

[33]  Surya Ganguli,et al.  A deep learning framework for neuroscience , 2019, Nature Neuroscience.

[34]  Peter C. Humphreys,et al.  Deep Learning without Weight Transport , 2019, NeurIPS.

[35]  James C. R. Whittington,et al.  Theories of Error Back-Propagation in the Brain , 2019, Trends in Cognitive Sciences.

[36]  Jaehoon Lee,et al.  Wide neural networks of any depth evolve as linear models under gradient descent , 2019, NeurIPS.

[37]  Francis Bach,et al.  On Lazy Training in Differentiable Programming , 2018, NeurIPS.

[38]  Surya Ganguli,et al.  A mathematical theory of semantic development in deep neural networks , 2018, Proceedings of the National Academy of Sciences.

[39]  Francesca Mastrogiuseppe,et al.  A Geometrical Analysis of Global Stability in Trained Feedback Networks , 2019, Neural Computation.

[40]  Yoshua Bengio,et al.  Dendritic cortical microcircuits approximate the backpropagation algorithm , 2018, NeurIPS.

[41]  Fred H Hamker,et al.  On the Role of Cortex-Basal Ganglia Interactions for Category Learning: A Neurocomputational Approach , 2018, The Journal of Neuroscience.

[42]  Arthur Jacot,et al.  Neural tangent kernel: convergence and generalization in neural networks (invited paper) , 2018, NeurIPS.

[43]  Daniel L. K. Yamins,et al.  A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy , 2018, Neuron.

[44]  Andrea Montanari,et al.  A mean field view of the landscape of two-layer neural networks , 2018, Proceedings of the National Academy of Sciences.

[45]  E. Miller,et al.  Gradual progression from sensory to task-related processing in cerebral cortex , 2017, Proceedings of the National Academy of Sciences.

[46]  Rafal Bogacz,et al.  An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity , 2017, Neural Computation.

[47]  David J. Freedman,et al.  Computing by Robust Transience: How the Fronto-Parietal Network Performs Sequential, Category-Based Decisions , 2017, Neuron.

[48]  Haim Sompolinsky,et al.  Optimal Degrees of Synaptic Connectivity , 2017, Neuron.

[49]  Colin J. Akerman,et al.  Random synaptic feedback weights support error backpropagation for deep learning , 2016, Nature Communications.

[50]  Avishek Adhikari,et al.  Wiring and Molecular Features of Prefrontal Ensembles Representing Distinct Experiences , 2016, Cell.

[51]  J. DiCarlo,et al.  Using goal-driven deep learning models to understand sensory cortex , 2016, Nature Neuroscience.

[52]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[53]  David J. Freedman,et al.  Choice-correlated activity fluctuations underlie learning of neuronal category representation , 2015, Nature Communications.

[54]  H. Sompolinsky,et al.  Sparseness and Expansion in Sensory Representations , 2014, Neuron.

[55]  Blake S. Porter,et al.  Hippocampal Representation of Related and Opposing Memories Develop within Distinct, Hierarchically Organized Neural Schemas , 2014, Neuron.

[56]  W. Newsome,et al.  Context-dependent computation by recurrent dynamics in prefrontal cortex , 2013, Nature.

[57]  Xiao-Jing Wang,et al.  The importance of mixed selectivity in complex cognitive tasks , 2013, Nature.

[58]  Stefano Fusi,et al.  The Sparseness of Mixed Selectivity Neurons Controls the Generalization–Discrimination Trade-Off , 2013, The Journal of Neuroscience.

[59]  Lindsey L. Glickfeld,et al.  Cortico-cortical projections in mouse visual cortex are functionally target specific , 2013, Nature Neuroscience.

[60]  David J. Freedman,et al.  Biased Associative Representations in Parietal Cortex , 2013, Neuron.

[61]  Stefano Fusi,et al.  Learning selective top-down control enhances performance in a visual categorization task. , 2012, Journal of neurophysiology.

[62]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[63]  David J. Freedman,et al.  Generalized associative representations in parietal cortex , 2011, Nature Neuroscience.

[64]  Jefferson E. Roy,et al.  Prefrontal Cortex Activity during Flexible Categorization , 2010, The Journal of Neuroscience.

[65]  Jefferson E. Roy,et al.  Representation of Multiple, Independent Categories in the Primate Prefrontal Cortex , 2010, Neuron.

[66]  J. Assad,et al.  Direction selectivity of neurons in the macaque lateral intraparietal area. , 2009, Journal of neurophysiology.

[67]  David J. Freedman,et al.  Neural mechanisms of visual categorization: Insights from neurophysiology , 2008, Neuroscience & Biobehavioral Reviews.

[68]  K. Miller,et al.  One-Dimensional Dynamics of Attention and Decision Making in LIP , 2008, Neuron.

[69]  David J. Freedman,et al.  Experience-dependent representation of visual categories in parietal cortex , 2006, Nature.

[70]  Bruno A Olshausen,et al.  Sparse coding of sensory inputs , 2004, Current Opinion in Neurobiology.

[71]  Lawrence H Snyder,et al.  Single Neurons in Posterior Parietal Cortex of Monkeys Encode Cognitive Set , 2004, Neuron.

[72]  P. Lennie.  Receptive fields , 2003, Current Biology.

[73]  Gregory C DeAngelis,et al.  Coding of horizontal disparity and velocity by MT neurons in the alert macaque. , 2003, Journal of neurophysiology.

[74]  L. Squire,et al.  Neuronal representations of stimulus associations develop in the temporal lobe during learning , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[75]  K. C. Anderson,et al.  Single neurons in prefrontal cortex encode abstract rules , 2001, Nature.

[76]  David J. Freedman,et al.  Categorical representation of visual stimuli in the primate prefrontal cortex. , 2001, Science.

[77]  S. Wise,et al.  Rule-dependent neuronal activity in the prefrontal cortex , 1999, Experimental Brain Research.

[78]  E. Miller,et al.  Neural Activity in the Primate Prefrontal Cortex during Associative Learning , 1998, Neuron.

[79]  T. Albright.  Direction and orientation selectivity of neurons in visual area MT of the macaque. , 1984, Journal of neurophysiology.

[80]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[81]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[82]  C. H. Graham,et al.  Sensation and perception in an objective psychology. , 1958, Psychological review.