70 citations · 242 across the 10 of their papers we have counts for
10 papers
Tensor decomposition of higher-order correlations by nonlinear Hebbian plasticity
Gabriel Koch Ocker, Michael A. Buice
Biological synaptic plasticity exhibits nonlinearities that are not accounted for by classic Hebbian learning rules. Here, we introduce a simple family of generalized nonlinear Heb…
Comparison Against Task Driven Artificial Neural Networks Reveals Functional Organization of Mouse Visual Cortex
Jianghong Shi, Eric Shea-Brown, Michael A. Buice
Partially inspired by features of computation in visual cortex, deep neural networks compute hierarchical representations of their inputs. While these networks have been highly suc…
From the statistics of connectivity to the statistics of spike times in neuronal networks
Gabriel Koch Ocker, Yu Hu, Michael A. Buice +4
An essential step toward understanding neural circuits is linking their structure and their dynamics. In general, this relationship can be almost arbitrarily complex. Recent theore…
Predicting how and when hidden neurons skew measured synaptic interactions
Braden A. W. Brinkman, Fred Rieke, Eric Shea-Brown +1
A major obstacle to understanding neural coding and computation is the fact that experimental recordings typically sample only a small fraction of the neurons in a circuit. Measure…
Linking structure and activity in nonlinear spiking networks
Gabriel Koch Ocker, Krešimir Josić, Eric Shea-Brown +1
Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a fra…
Generalized activity equations for spiking neural network dynamics
Michael A. Buice, Carson C. Chow
Much progress has been made in uncovering the computational capabilities of spiking neural networks. However, spiking neurons will always be more expensive to simulate compared to…