Predictive coding in sensory systems

Choi, Pasupathy, Shea-Brown (2018) Neural Computation
Choi, Pasupathy, Shea-Brown (2018) Neural Computation

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 sensory systems, particularly in visual cortex. To achieve this, we incorporate biologically-realistic cortical connectivity and neural dynamics to study predictive coding models of cortical circuitries.

Related publications:

2020, H. Choi, M. Garrett, R. Larsen, N. Cain, , Matthew Valley, Yazan Billeh, Jérôme Lecoq, Peter Groblewski, Kyla Mace, Kat North, Chelsea Nayan, Ali Williford, Jedediah Perkins, Kara Ronellenfitch, and OpenScope Project at the Allen Institute, “Unraveling the neural circuitry of predictive coding in mouse visual cortex” Cosyne Abstracts 2020, Denver USA

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

Structure and dynamics of brain networks

Harris, Mihalas, Hirokawa, Whitesell, Choi et al (2019) Nature

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, underlying 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 scientists at the Allen Institute, we study the links between network architecture, dynamics, and computation using data-driven mathematical models of mammalian whole brain and cortico-thalamic networks.

Related publications:

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