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Matthew A. Wilson. Ma, Tzuhsuan. Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences. Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences 2021-12-17T17:04:27Z 2021-12-17T17:04:27Z 2020 2020 https://hdl.handle.net/1721.1/138514 1280701988 Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, February, 2020 Manuscript. Includes bibliographical references (pages 227-238). This work utilizes theoretical approaches to answer the question: which functions grid and place cells perform that directly lead to their own emergence? To answer such a question, an approach that goes beyond a simple modelling is necessary given the fact that there could be circuit solutions other than grid or place cells that better perform these functions. With this reasoning, I adopted a systematic guideline that aims for an optimization principle attempting to find the optimal solution for performing the hypothesized functions while reproducing the correct phenomenology. Within the optimization principle framework, I applied both recurrent neural network (RNN) training and coding-theoretic approaches to set up appropriate optimization problems for testing a given function hypotheses. The descriptive function hypotheses: 1) Grid cells exist for having a high-capacity and robust path-integrating code and 2) Place cells exist for having a sequentially-learnable and highly-separable path-integrating code were adopted. The non-converging performance in training an RNN to perform a hard navigation task suggests that the attractor dynamics forbids a network to simultaneously possess online learnability and high coding capacity. Because of this dynamical constraint in learning, a grid cell circuit has to be hardwired through some developmental process and cannot be easily modified by an experience-based synaptic rule without compromising its capacity. On the contrary, a place cell circuit being able to continually learn a novel environment inevitably have a mere linear capacity. These results imply that the functional separation of grid and place cell systems observed in the brain could be a result of an unavoidable dynamical constraint from their underlying RNNs. Lastly, a fundamental principle called the tuning-learnability correspondence was uncovered in pursuit of a sequentially learnable neural implementation for place cells. It explains that the seemingly incidental existence of conjunctive tuning property is in fact caused by a necessary metastable attractor dynamics for having sequential learnability rather than by another functional need attached to a particular tuning property. In addition, from the unique property of metastable attractor dynamics, I also predicted that the biased place field propensity recently observed in CA1 sub-region should originate from CA3 due to an inevitable biased activation in the RNN as a side effect of such a dynamical property. In sum, both this principle and the subsequent prediction thus provide a new perspective that contradicts the conventional wisdom which often assumed that a certain nonspatial tuning property exists for performing a relevant task. by Tzuhsuan Ma. Ph.D. Ph. D. Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences 238 pages eng Massachusetts Institute of Technology MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 Brain and Cognitive Sciences. Towards a theory for the emergence of grid and place cell codes Thesis application/pdf 2021-12-17T17:04:27Z Publication Doctoral Brain unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="497c62b2-1c32-48f5-9d52-3d077cf587d4"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Towards a theory for the emergence of grid and place cell codes</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2020</PublicationDate> <Authors> <Author> <DisplayName>Ma, Tzuhsuan.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Brain and Cognitive Sciences.</Keyword> <Abstract>This work utilizes theoretical approaches to answer the question: which functions grid and place cells perform that directly lead to their own emergence? To answer such a question, an approach that goes beyond a simple modelling is necessary given the fact that there could be circuit solutions other than grid or place cells that better perform these functions. With this reasoning, I adopted a systematic guideline that aims for an optimization principle attempting to find the optimal solution for performing the hypothesized functions while reproducing the correct phenomenology. Within the optimization principle framework, I applied both recurrent neural network (RNN) training and coding-theoretic approaches to set up appropriate optimization problems for testing a given function hypotheses. The descriptive function hypotheses: 1) Grid cells exist for having a high-capacity and robust path-integrating code and 2) Place cells exist for having a sequentially-learnable and highly-separable path-integrating code were adopted. The non-converging performance in training an RNN to perform a hard navigation task suggests that the attractor dynamics forbids a network to simultaneously possess online learnability and high coding capacity. Because of this dynamical constraint in learning, a grid cell circuit has to be hardwired through some developmental process and cannot be easily modified by an experience-based synaptic rule without compromising its capacity. On the contrary, a place cell circuit being able to continually learn a novel environment inevitably have a mere linear capacity. These results imply that the functional separation of grid and place cell systems observed in the brain could be a result of an unavoidable dynamical constraint from their underlying RNNs. Lastly, a fundamental principle called the tuning-learnability correspondence was uncovered in pursuit of a sequentially learnable neural implementation for place cells. It explains that the seemingly incidental existence of conjunctive tuning property is in fact caused by a necessary metastable attractor dynamics for having sequential learnability rather than by another functional need attached to a particular tuning property. In addition, from the unique property of metastable attractor dynamics, I also predicted that the biased place field propensity recently observed in CA1 sub-region should originate from CA3 due to an inevitable biased activation in the RNN as a side effect of such a dynamical property. In sum, both this principle and the subsequent prediction thus provide a new perspective that contradicts the conventional wisdom which often assumed that a certain nonspatial tuning property exists for performing a relevant task.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>