Predictive coding in sensory systems and behavior
Predictive coding has been proposed as a method to create efficient neural codes. One flavor of predictive coding focuses on the brain’s ability to predict sensory inputs in the near future through learning. Another theory of predictive coding postulates that cortical circuits learn and represent information about the world through hierarchical interactions across cortical areas and layers. Our goal is to understand the neural correlates of predictive coding and its detailed mechanisms in computation of sensory information and behavior. To achieve this, we incorporate biologically-realistic connectivity and neural dynamics to study predictive coding models of cortical and other neural circuitries.
Related publications (selected):
2023, D.G. Wyrick, N. Cain, R.S. Larsen, J. Lecoq, M. Valley, R. Ahmed, J. Bowlus, G. Boyer, S. Caldejon, L. Casal, M. Chvilicek, M. DePartee, P.A.Groblewski, C. Huang, K. Johnson, I. Kato, J. Larkin, E. Lee, E, Liang, J. Luviano, K. Mace, C. Nayan, T. Nguyen, M. Reding, S. Seid, J. Sevigny, M. Stoecklin, A. Williford, H. Choi*+, M. Garrett*+, L. Mazzucato*+, “Differential encoding of temporal context and expectation under representational drift across hierarchically connected areas” bioRxiv preprint: doi.org/10.1101/2023.06.02.543483
2023, A. Sharafeldin*, N. Imam+, H. Choi+, “Active sensing with predictive coding and uncertainty minimization”, arXiv preprint: 2307.00668 [cs.LG]
2018, H. Choi*, A.Pasupathy, E. Shea-Brown, “Predictive coding in area V4: dynamical shape discrimination under partial occlusion”, Neural Computation 30(5):1209–1257
2017, A.M. Fyall+, Y. El-Shamayleh+, H. Choi, E. Shea-Brown, A. Pasupathy, “Dynamic representation of partially occluded objects in primate prefrontal and visual cortex”, eLife 6:e25784
Related presentations (selected):
2023, A. Balwani, S. Cho, H. Choi, “Exploring the architectural biases of the canonical cortical microcircuit” Cosyne, Montreal, Canada. Watch Aish’s talk here!
Topology and dynamics of brain networks
The brain is composed of networks of many different scales, ranging from cell-to-cell circuitries in cortical columns to macroscale connections between large brain regions. The brain networks at multiple scales feature uniquely complex connectivity structure among different types of neuronal populations, giving emergence to diverse neural dynamics at the network level. How information is processed in these complex, dynamic networks has been a long standing problem in neuroscience. In collaboration with experimental labs, we study the links between network architecture, dynamics, and computation using data-driven mathematical models of biological neuronal networks.
Related publications (selected):
2023, J. Del Rosario, S. Coletta+, S. H. Kim+, Z. Mobille, K. Peelman, B. Williams, A. J. Otsuki, A. Del Castillo Valerio, K. Worden, L. T. Blanpain, L. Lovell, H. Choi, B. Haider*, “Lateral inhibition in V1 controls neural and perceptual contrast sensitivity“, bioRxiv preprint: doi.org/10.1101/2023.11.10.566605
2023, C. Li, S. H. Kim, C. Rodgers, H. Choi, A. Wu*, “One-hot generalized linear model for switching brain state discovery”, arXiv preprint: 2310.15263 [q-bio.NC] (DOI: 10.48550/arXiv.2310.15263)
2023, D. Tang*, J. Zylberberg*+, X. Jia*+, H. Choi*+, “Stimulus-dependent functional network topology in mouse visual cortex”, bioRxiv preprint: doi.org/10.1101/2023.07.03.547364
2023, U.B. Sikandar*, H. Choi, J. Putney, H. Yang, S. Ferrari, S. Sponberg, “Predicting visually modulated precisely-timed spikes across a coordinated and comprehensive motor program”, International Joint Conference on Neural Networks (IJCNN) :1–8
2020, J.H. Siegle+, X. Jia+, S. Durand, S. Gale, C. Bennett, N. Graddis, G. Heller, T. Ramirez, H. Choi, J.A. Luviano, …, S.R. Olsen, C. Koch, “Survey of spiking in the mouse visual system reveals functional hierarchy”, Nature 592:86–92
2019, J.A. Harris+, S. Mihalas+, K.E. Hirokawa, J.D. Whitesell, H. Choi, …, C. Koch, H. Zeng, “Hierarchical organization of cortical and thalamic connectivity”, Nature 575:195-202
2019, H. Choi*, S. Mihalas, “Synchronization dependent on spatial structures of a mesoscopic whole-brain network”, PLOS Computational Biology 15(4): e1006978
2014, H. Choi, L. Zhang, M.S. Cembrowski, C.F. Sabottke, A.L. Markowitz, D.A. Butts, W.L. Kath, J.H. Singer*, and H. Riecke*, “Intrinsic bursting of AII amacrine cells underlies oscillations in rd1 mouse retina”, Journal of Neurophysiology 112: 1491-1504