{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/138514"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/138514","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Towards a theory for the emergence of grid and place cell codes","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_html":"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_has_math":false,"creators":["Ma, Tzuhsuan."],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences","school":null,"contributors":[],"advisors":["Matthew A. Wilson."],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020","date_published":"2020","updated_at":"2026-07-22T22:21:16Z","subjects":["Brain and Cognitive Sciences."],"languages":["eng"],"rights":["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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/138514","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Matthew A. Wilson."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. 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Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/138514"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, February, 2020","Manuscript.","Includes bibliographical references (pages 227-238)."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph. D."]},{"key":"dc:title","label":"Title","values":["Towards a theory for the emergence of grid and place cell codes"]}]}],"canonical_facts":{"dc:contributor.advisor":["Matthew A. Wilson."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences","Brain"],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences."],"dc:creator":["Ma, Tzuhsuan."],"dc:date.accessioned":["2021-12-17T17:04:27Z"],"dc:date.available":["2021-12-17T17:04:27Z"],"dc:date.issued":["2020"],"dc:description":["Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, February, 2020","Manuscript.","Includes bibliographical references (pages 227-238)."],"dc:description.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."],"dc:description.degree":["Ph. D."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/138514"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Brain and Cognitive Sciences."],"dc:title":["Towards a theory for the emergence of grid and place cell codes"],"dc:type":["Thesis"],"thesis:degree_name":["Doctoral"]},"updated_at":"2026-07-22T22:21:16Z"}