29 citations · 99 across the 20 of their papers we have counts for
7 papers · 1 filter
From Chaos to Coherence: Effects of High-Order Synaptic Correlations on Neural Dynamics
Nimrod Sherf, Xaq Pitkow, Krešimir Josić +1
Recurrent Neural Network models have elucidated the interplay between structure and dynamics in biological neural networks, particularly the emergence of irregular and rhythmic act…
Attention when you need
Lokesh Boominathan, Grayson Matthew, Yizhou Chen +2
Paying attention improves performance, but attention is metabolically costly, so how should a resource-efficient agent allocate it? We study optimal allocation strategies using a n…
Inferring Inference
Rajkumar Vasudeva Raju, Zhe Li, Scott Linderman +1
Patterns of microcircuitry suggest that the brain has an array of repeated canonical computational units. Yet neural representations are distributed, so the relevant computations m…
Phase transitions in when feedback is useful
Lokesh Boominathan, Xaq Pitkow
Sensory observations about the world are invariably ambiguous. Inference about the world's latent variables is thus an important computation for the brain. However, computational c…
How the brain might work: statistics flowing in redundant population codes
Xaq Pitkow, Dora Angelaki
It is widely believed that the brain performs approximate probabilistic inference to estimate causal variables in the world from ambiguous sensory data. To understand these computa…
Inference by Reparameterization in Neural Population Codes
Rajkumar Vasudeva Raju, Xaq Pitkow
Behavioral experiments on humans and animals suggest that the brain performs probabilistic inference to interpret its environment. Here we present a new general-purpose, biological…