Area-Specificity and Plasticity of History-Dependent Value Coding During Learning

Decision making is often driven by the subjective value of available options, a value which is formed through experience. To support this fundamental behavior, the brain must encode and maintain the subjective value. To investigate the area specificity and plasticity of value coding, we trained mice in a value-based decision task and imaged neural activity in 6 cortical areas with cellular resolution. History- and value-related signals were widespread across areas, but their strength and temporal patterns differed. In expert mice, the retrosplenial cortex (RSC) uniquely encoded history- and value-related signals with persistent population activity patterns across trials. This unique encoding of RSC emerged during task learning with a strong increase in more distant history signals. Acute inactivation of RSC selectively impaired the reward-history-based behavioral strategy. Our results indicate that RSC flexibly changes its history coding and persistently encodes value-related signals to support adaptive behaviors.

[1]  Hongkui Zeng,et al.  Long-Term Optical Access to an Estimated One Million Neurons in the Live Mouse Cortex. , 2016, Cell reports.

[2]  K. Doya,et al.  Representation of Action-Specific Reward Values in the Striatum , 2005, Science.

[3]  Upinder S Bhalla,et al.  Non-redundant odor coding by sister mitral cells revealed by light addressable glomeruli in the mouse , 2010, Nature Neuroscience.

[4]  Fabian Grabenhorst,et al.  A dynamic code for economic object valuation in prefrontal cortex neurons , 2016, Nature Communications.

[5]  W. Newsome,et al.  Matching Behavior and the Representation of Value in the Parietal Cortex , 2004, Science.

[6]  Matthew T. Kaufman,et al.  A category-free neural population supports evolving demands during decision-making , 2014, Nature Neuroscience.

[7]  Zengcai V. Guo,et al.  Flow of Cortical Activity Underlying a Tactile Decision in Mice , 2014, Neuron.

[8]  E. Shen,et al.  Long-term in vivo time-lapse imaging of synapse development and plasticity in the cerebellum. , 2014, Journal of neurophysiology.

[9]  Mario Dipoppa,et al.  Suite2p: beyond 10,000 neurons with standard two-photon microscopy , 2016, bioRxiv.

[10]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[11]  Stephen J. Wright,et al.  Sparse reconstruction by separable approximation , 2009, IEEE Trans. Signal Process..

[12]  L. Wilbrecht,et al.  Rule learning enhances structural plasticity of long-range axons in frontal cortex , 2016, Nature Communications.

[13]  C. Padoa-Schioppa,et al.  Neurons in the orbitofrontal cortex encode economic value , 2006, Nature.

[14]  Kenneth D Harris,et al.  Robustness of Spike Deconvolution for Neuronal Calcium Imaging , 2018, The Journal of Neuroscience.

[15]  Bruce L. McNaughton,et al.  Sparse orthogonal population representation of spatial context in the retrosplenial cortex , 2017, Nature Communications.

[16]  Daeyeol Lee,et al.  Role of rodent secondary motor cortex in value-based action selection , 2011, Nature Neuroscience.

[17]  Jung Hoon Sul,et al.  Distinct Roles of Rodent Orbitofrontal and Medial Prefrontal Cortex in Decision Making , 2010, Neuron.

[18]  Naoki Yamawaki,et al.  A Corticocortical Circuit Directly Links Retrosplenial Cortex to M2 in the Mouse , 2016, The Journal of Neuroscience.

[19]  C. Ranganath,et al.  Two cortical systems for memory-guided behaviour , 2012, Nature Reviews Neuroscience.

[20]  P. Glimcher,et al.  JOURNAL OF THE EXPERIMENTAL ANALYSIS OF BEHAVIOR 2005, 84, 555–579 NUMBER 3(NOVEMBER) DYNAMIC RESPONSE-BY-RESPONSE MODELS OF MATCHING BEHAVIOR IN RHESUS MONKEYS , 2022 .

[21]  Douglas A Nitz,et al.  Retrosplenial cortex maps the conjunction of internal and external spaces , 2015, Nature Neuroscience.

[22]  K. Doya,et al.  Validation of Decision-Making Models and Analysis of Decision Variables in the Rat Basal Ganglia , 2009, The Journal of Neuroscience.

[23]  Takaki Komiyama,et al.  Learning enhances the relative impact of top-down processing in the visual cortex , 2015, Nature Neuroscience.

[24]  T. Komiyama,et al.  Transformation of Cortex-wide Emergent Properties during Motor Learning , 2017, Neuron.

[25]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[26]  Ari S. Morcos,et al.  History-dependent variability in population dynamics during evidence accumulation in cortex , 2016, Nature Neuroscience.

[27]  George Paxinos,et al.  The Mouse Brain in Stereotaxic Coordinates , 2001 .

[28]  W M Baum,et al.  On two types of deviation from the matching law: bias and undermatching. , 1974, Journal of the experimental analysis of behavior.

[29]  Georgios D. Evangelidis,et al.  Parametric Image Alignment Using Enhanced Correlation Coefficient Maximization , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Mark S. Cembrowski,et al.  Dissociable Structural and Functional Hippocampal Outputs via Distinct Subiculum Cell Classes , 2018, Cell.

[31]  Michael L. Platt,et al.  Neural correlates of decision variables in parietal cortex , 1999, Nature.

[32]  Alcino J. Silva,et al.  Encoding and storage of spatial information in the retrosplenial cortex , 2014, Proceedings of the National Academy of Sciences.

[33]  Hye-Won Jeong,et al.  A Neural Circuit for Auditory Dominance over Visual Perception , 2017, Neuron.

[34]  Takaki Komiyama,et al.  Real-Time Processing of Two-Photon Calcium Imaging Data Including Lateral Motion Artifact Correction , 2018, Front. Neuroinform..

[35]  Hatim A. Zariwala,et al.  Neural correlates, computation and behavioural impact of decision confidence , 2008, Nature.

[36]  Karel Svoboda,et al.  Learning-related fine-scale specificity imaged in motor cortex circuits of behaving mice , 2010, Nature.

[37]  G. Schoenbaum,et al.  Orbitofrontal neurons infer the value and identity of predicted outcomes , 2014, Nature Communications.

[38]  N. Heintz,et al.  Cell-Type-Specific Contributions of Medial Prefrontal Neurons to Flexible Behaviors , 2018, The Journal of Neuroscience.

[39]  R. Herrnstein.  On the law of effect. , 1970, Journal of the experimental analysis of behavior.

[40]  Denise M. Piscopo,et al.  Large-scale imaging of cortical dynamics during sensory perception and behavior. , 2016, Journal of neurophysiology.

[41]  Hiroshi Yamada,et al.  Roles of the Lateral Habenula and Anterior Cingulate Cortex in Negative Outcome Monitoring and Behavioral Adjustment in Nonhuman Primates , 2015, Neuron.

[42]  Vaughn L. Hetrick,et al.  Mesolimbic Dopamine Signals the Value of Work , 2015, Nature Neuroscience.

[43]  Y. Loewenstein,et al.  Striatal action-value neurons reconsidered , 2017, bioRxiv.

[44]  E. Maguire,et al.  What does the retrosplenial cortex do? , 2009, Nature Reviews Neuroscience.

[45]  T. Komiyama,et al.  History-based action selection bias in posterior parietal cortex , 2017, Nature Communications.

[46]  D. Barraclough,et al.  Prefrontal cortex and decision making in a mixed-strategy game , 2004, Nature Neuroscience.

[47]  Hongkui Zeng,et al.  Olfactory cortical neurons read out a relative time code in the olfactory bulb , 2013, Nature Neuroscience.

[48]  Laurie Lambot,et al.  Long-range inhibitory intersection of a retrosplenial thalamocortical circuit by apical tuft-targeting CA1 neurons , 2018, Nature Neuroscience.

[49]  H. Seung,et al.  JOURNAL OF THE EXPERIMENTAL ANALYSIS OF BEHAVIOR 2005, 84, 581–617 NUMBER 3(NOVEMBER) LINEAR-NONLINEAR-POISSON MODELS OF PRIMATE CHOICE DYNAMICS , 2022 .

[50]  Carlos D. Brody,et al.  Fronto-parietal Cortical Circuits Encode Accumulated Evidence with a Diversity of Timescales , 2017, Neuron.

[51]  Christopher D. Harvey,et al.  Choice-specific sequences in parietal cortex during a virtual-navigation decision task , 2012, Nature.

[52]  Tianyi Mao,et al.  A comprehensive excitatory input map of the striatum reveals novel functional organization , 2016, eLife.