414 citations · 916 across the 8 of their papers we have counts for
8 papers
A brain basis of dynamical intelligence for AI and computational neuroscience
Joseph D. Monaco, Kanaka Rajan, Grace M. Hwang
The deep neural nets of modern artificial intelligence (AI) have not achieved defining features of biological intelligence, including abstraction, causal learning, and energy-effic…
Efficient and robust multi-task learning in the brain with modular latent primitives
Christian David Márton, Léo Gagnon, Guillaume Lajoie +1
Biological agents do not have infinite resources to learn new things. For this reason, a central aspect of human learning is the ability to recycle previously acquired knowledge in…
full-FORCE: A Target-Based Method for Training Recurrent Networks
Brian DePasquale, Christopher J. Cueva, Kanaka Rajan +2
Trained recurrent networks are powerful tools for modeling dynamic neural computations. We present a target-based method for modifying the full connectivity matrix of a recurrent n…
Recurrent Network Models Of Sequence Generation And Memory
Kanaka Rajan, Christopher D Harvey, David W Tank
Sequential activation of neurons is a common feature of network activity during a variety of behaviors, including working memory and decision making. Previous network models for se…
Learning quadratic receptive fields from neural responses to natural stimuli
Kanaka Rajan, Olivier Marre, Gašper Tkačik
Models of neural responses to stimuli with complex spatiotemporal correlation structure often assume that neurons are only selective for a small number of linear projections of a p…
Maximally informative "stimulus energies" in the analysis of neural responses to natural signals
Kanaka Rajan, William Bialek
The concept of feature selectivity in sensory signal processing can be formalized as dimensionality reduction: in a stimulus space of very high dimensions, neurons respond only to…
Interactions between Intrinsic and Stimulus-Evoked Activity in Recurrent Neural Networks
L F Abbott, Kanaka Rajan, Haim Sompolinsky
Trial-to-trial variability is an essential feature of neural responses, but its source is a subject of active debate. Response variability (Mast and Victor, 1991; Arieli et al., 19…
Stimulus-Dependent Suppression of Chaos in Recurrent Neural Networks
Kanaka Rajan, L F Abbott, Haim Sompolinsky
Neuronal activity arises from an interaction between ongoing firing generated spontaneously by neural circuits and responses driven by external stimuli. Using mean-field analysis,…