Neural correlations, population coding and computation

By Bruno B. Averbeck, Peter E. Latham, Alexandre Pouget. 2006

1,770 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Re-evaluating circuit mechanisms underlying pattern separation (2019, positive): “Theoretical work has highlighted the strong impact that correlations in neural activity can have on information processing and population coding in neural circuits (Averbeck et al., 2006; Kohn et al., 2016).”
Hierarchical excitatory synaptic connectivity in mouse olfactory cortex (2013, background): “We expect that with inhibition intact, coherence will be temporally restricted to smaller, although still distributed, ensembles, providing a substrate for fast distributed population coding (47).”
Visual image reconstruction from human brain activity using a combination of multiscale local image decoders. (2008, methodology): “The data were created by shufﬂing the order of the trials with the same stimulus label in each voxel (Averbeck et al., 2006).”
Learning Spike-Based Population Codes by Reward and Population Feedback (2010, background): “Next, in population coding (Pouget et al., 2000; Averbeck et al., 2006), such postsynaptic neuronal responses are again aggregated by a read-out which may be a decision making circuitry (Wang, 2002).”
Macroscopic description for networks of spiking neurons (2015, background): “3(e) has some underlying structure that may be visualized ordering the same set of neu rons according to their intrinsic currents as ηk < ηk+1, with k ∈ [1, 300], see Fig.”
The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data (2015, background): “Although a number of brain regions show overall changes in activity selective for speciﬁc stimulus categories (e.g., Kanwisher et al., 1997), stimuli can be encoded in a more distributed manner (Averbeck et al., 2006).”
Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4. (2009, background): “Response variability affects how reliably information is encoded by neuronal signals (Parker and Newsome, 1998; Zohary et al., 1994; Shadlen et al., 1996; Averbeck et al., 2006; Pillow et al., 2008).”
Space and time in visual context (2007, methodology): “Operating under the Poisson spiking neuron model (we assume that neurons in the population are independent, although correlations between neurons might also affect the result; for a recent review see Ref”