000007504 001__ 7504
000007504 005__ 20250923113753.0
000007504 0247_ $$2doi$$a10.6082/uchicago.7504
000007504 037__ $$aTHESIS$$bDissertation
000007504 041__ $$aeng
000007504 245__ $$aUnderstanding Neocortical Dynamics and Computation through Spiking Neural Network Modeling
000007504 260__ $$bUniversity of Chicago
000007504 269__ $$a2023-08
000007504 300__ $$a162
000007504 336__ $$aDissertation
000007504 502__ $$bPh.D.
000007504 520__ $$aThrough the use of biofidelic spiking neural network models (SNNs), this work offers mechanistic insights into the relationship between neocortical structure, dynamics, and computation. To set the stage, the topics of cortical computation, ANNs as applied to neuroscience, SNNs as applied to studying structure-function relationships in neocortex, and the challenges of model interpretability are introduced. In the first chapter, in the tradition of using SNNs to address structure-function questions, we ask why stable dynamics are possible in neocortex. Biofidelic SNNs were created through a grid search for architectures that yielded realistic spiking activity that was low-rate, asynchronous, and near-critical. The maintenance of this activity is linked to patterns of higher order coordination of synaptic activity, and this coordination takes the form of transitions in time between specific three-unit motifs. These motifs summarize the way spikes traverse the underlying synaptic topology. The second chapter turns its focus to computation, which occurs in neocortex on a substrate of stable activity that we studied in the first. Specifically, this chapter covers why computation becomes possible through specific synaptic changes and resulting dynamic changes that occur through learning. After training SNN models to perform an ethologically relevant task, models come to selectively adjust firing rates in response to the stimulus input. Excitatory and inhibitory connectivity between input and recurrent layers changed in accordance with this rate modulation. In particular, recurrent inhibitory units which were tuned to one input over the other strengthened their connections to recurrent units of the opposite tuning. We conclude by discussing the potential of task-trained SNNs for hypothesis generation and testing in future research on neocortical computation. Additional neocortical features that may be important for computation are surveyed, and questions of model interpretation are revisited in the context of these results.
000007504 540__ $$a© 2023 Yuqing Zhu
000007504 542__ $$fCC BY-NC-SA
000007504 650__ $$aNeurosciences
000007504 650__ $$aComputer science
000007504 653__ $$amachine learning
000007504 653__ $$aneocortex
000007504 653__ $$anetwork science
000007504 653__ $$aneural network models
000007504 653__ $$aspiking neural networks
000007504 653__ $$asystems neuroscience
000007504 690__ $$aBiological Sciences Division
000007504 690__ $$aPritzker School of Medicine
000007504 691__ $$aComputational Neuroscience
000007504 7001_ $$aZhu, Yuqing$$uUniversity of Chicago
000007504 72012 $$aJason N. MacLean
000007504 72014 $$aJohn H. R Maunsell
000007504 72014 $$aStephanie E. Palmer
000007504 72014 $$aDavid J. Freedman
000007504 8564_ $$98f3babc4-fd76-49ef-ab4a-baebff0687b7$$s11110714$$uhttps://knowledge.uchicago.edu/record/7504/files/Zhu_uchicago_0330D_17021.pdf$$ePublic
000007504 909CO $$ooai:uchicago.tind.io:7504$$pDissertations$$pGLOBAL_SET
000007504 983__ $$aDissertation