Synergy, Redundancy, and Independence in Population Codes

By Elad Schneidman, William Bialek, Michael J. Berry. Journal of Neuroscience, 2003

536 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/196e306afa4e767852d139a4402e82b2216a336c

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Citations in the corpus

Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems (2014, background): “Indeed, intrinsic variability and diverse activation patterns are often identified as fundamental aspects of neural computation for information maximization and transmission [30], [148]–[150].”
Information Capacity and Transmission Are Maximized in Balanced Cortical Networks with Neuronal Avalanches (2010, background): “Increased interactions between sites always reduce H because of the increased redundancy of the information at different sites (Schneidman et al., 2003).”
Correcting for the sampling bias problem in spike train information measures. (2007, methodology): “Thus we define a normalized degree of synergy of information about the spikes (as measured by the classification algorithm) as (Schneidman et al. 2003) syn F1, F2 S : I S; F1, F2 I S; F1 I S; F2 I S; F1, F2 (4) This measure ranges from 1 in the case of completely redundant information to 1 for…”
Neural population coding: combining insights from microscopic and mass signals (2015, background): “…can inﬂuence neural population coding in more complex way than described above: depending on the precise pattern of signal and noise correlations, and the choice of readout mechanisms, correlations can increase, decrease, or leave unaffected the information carried by a population [1,15,40,49–51].”
Encoding of Naturalistic Stimuli by Local Field Potential Spectra in Networks of Excitatory and Inhibitory Neurons (2008, unknown): “It is therefore useful to introduce the following ‘‘information redundancy’’ [41,81,82]:”
A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula (2016, background): “One quantity of interest is the overlap, termed the redundancy [Panzeri et al., 2008; Schneidman et al., 2003; Timme et al., 2013], that quantiﬁes the MI which is shared between the two responses.”
Triple dissociation of information processing in dorsal striatum, ventral striatum, and hippocampus on a learned spatial decision task (2010, unknown): “Decoding provides access to representational content, allowing analysis of not just how much information a given ensemble contains but also of what that information actually is (Schneidman et al., 2003).”
Low-Frequency Local Field Potentials and Spikes in Primary Visual Cortex Convey Independent Visual Information (2008, background): “The difference I(S; Rf 1) I(S; Rf 2) I(S; Rf 1 Rf 2) is called information “redundancy” (Panzeri et al., 1999; Pola et al., 2003; Schneidman et al., 2003).”
Neural interactions in the human frontal cortex dissociate reward and punishment learning (2024, background): “Synergistic and redundant pairs of neurons can be identified by estimating the amount of information contained in the joint representation minus the sum of the information carried by individual neurons (Schneidman et al., 2003).”