Recent work has highlighted that many types of variables are represented in each
neocortical area. How can these many neural representations be organized together without …

Neuronal activity in the brain is variable, yet both perception and behavior are generally
reliable. How does the brain achieve this? Here, we show that the conjunctive coding of …

Neural codes allow the brain to represent, process, and store information about the world.
Combinatorial codes, comprised of binary patterns of neural activity, encode information via …

Information is carried in the brain by the joint spiking patterns of large groups of noisy,
unreliable neurons. This noise limits the capacity of the neural code and determines how …

The Hopfield recurrent neural network is a classical auto-associative model of memory, in
which collections of symmetrically coupled McCulloch–Pitts binary neurons interact to …

Threshold-linear networks are a common class of firing rate models that describe recurrent
interactions among neurons. Unlike their linear counterparts, these networks generically …

A convex code is a binary code generated by the pattern of intersections of a collection of
open convex sets in some Euclidean space. Convex codes are relevant to neuroscience as …

Networks of neurons in the brain encode preferred patterns of neural activity via their
synaptic connections. Despite receiving considerable attention, the precise relationship …

The dentate gyrus forms a critical link between the entorhinal cortex and CA3 by providing a
sparse version of the signal. Concurrent with this increase in sparsity, a widely accepted …

A neural code C is a collection of binary vectors of a given length n that record the co-firing
patterns of a set of neurons. Our focus is on neural codes arising from place cells, neurons …