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Weak pairwise correlations imply strongly correlated network states in a neural population
TLDR
It is shown, in the vertebrate retina, that weak correlations between pairs of neurons coexist with strongly collective behaviour in the responses of ten or more neurons, and it is found that this collective behaviour is described quantitatively by models that capture the observed pairwise correlations but assume no higher-order interactions.
Synergy, Redundancy, and Independence in Population Codes
TLDR
This work distinguishes questions about how information is encoded by a population of neurons from how that information can be decoded, and shows that these measures form an interrelated framework for evaluating contributions of signal and noise correlations to the joint information conveyed about the stimulus.
Ion Channel Stochasticity May Be Critical in Determining the Reliability and Precision of Spike Timing
TLDR
It is suggested that the noise inherent in the operation of ion channels enables neurons to act as smart encoders and channel stochasticity should be considered in realistic models of neurons.
Network information and connected correlations.
TLDR
The information theoretic analog of connected correlation functions is constructed: irreducible N-point correlation is measured by a decrease in entropy for the joint distribution of N variables relative to the maximum entropy allowed by all the observed N-1 variable distributions.
Searching for Collective Behavior in a Large Network of Sensory Neurons
TLDR
The properties of the neural vocabulary are explored by estimating its entropy, which constrains the population's capacity to represent visual information, and classifying activity patterns into a small set of metastable collective modes, showing that the neural codeword ensembles are extremely inhomogenous.
Sparse low-order interaction network underlies a highly correlated and learnable neural population code
TLDR
It is shown that because of the sparse nature of the neural code, the higher-order interactions can be easily learned using a novel model and that a very sparse low-order interaction network underlies the code of large populations of neurons.
Redundancy in the Population Code of the Retina
TLDR
A high degree of retinal redundancy suggests that design principles beyond coding efficiency may be important at the population level.
Optimal population coding by noisy spiking neurons
TLDR
This work extended the linear-nonlinear-Poisson model of single neural response to include pairwise interactions, yielding a stimulus-dependent, pairwise maximum-entropy model, which suggests that networks in the brain should adapt their function to changing noise and stimulus correlations.
The Architecture of Functional Interaction Networks in the Retina
TLDR
This work explored the architecture of maximum entropy models of the functional interaction networks underlying the response of large populations of retinal ganglion cells, in adult tiger salamander retina, responding to natural and artificial stimuli and demonstrated the existence of local network motifs in the interaction map of the retina.
Axons as computing devices: Basic insights gained from models
TLDR
The present paper summarizes the main insights that were gained from simplified models of axon and highlights the stochastic nature of axons, a topic that was largely neglected in classical models ofAxons.
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