[HTML][HTML] Sequential and efficient neural-population coding of complex task information
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 …
neocortical area. How can these many neural representations be organized together without …
[HTML][HTML] Nonlinear mixed selectivity supports reliable neural computation
WJ Johnston, SE Palmer… - PLoS computational …, 2020 - journals.plos.org
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 …
reliable. How does the brain achieve this? Here, we show that the conjunctive coding of …
What makes a neural code convex?
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 …
Combinatorial codes, comprised of binary patterns of neural activity, encode information via …
[HTML][HTML] A thesaurus for a neural population code
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 …
unreliable neurons. This noise limits the capacity of the neural code and determines how …
[HTML][HTML] Robust exponential memory in Hopfield networks
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 …
which collections of symmetrically coupled McCulloch–Pitts binary neurons interact to …
Pattern completion in symmetric threshold-linear networks
C Curto, K Morrison - Neural computation, 2016 - ieeexplore.ieee.org
Threshold-linear networks are a common class of firing rate models that describe recurrent
interactions among neurons. Unlike their linear counterparts, these networks generically …
interactions among neurons. Unlike their linear counterparts, these networks generically …
[HTML][HTML] Algebraic signatures of convex and non-convex codes
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 …
open convex sets in some Euclidean space. Convex codes are relevant to neuroscience as …
Encoding binary neural codes in networks of threshold-linear neurons
Networks of neurons in the brain encode preferred patterns of neural activity via their
synaptic connections. Despite receiving considerable attention, the precise relationship …
synaptic connections. Despite receiving considerable attention, the precise relationship …
A combinatorial model for dentate gyrus sparse coding
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 …
sparse version of the signal. Concurrent with this increase in sparsity, a widely accepted …
[HTML][HTML] Neural ideals and stimulus space visualization
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 …
patterns of a set of neurons. Our focus is on neural codes arising from place cells, neurons …