Neural population coding: combining insights from microscopic and mass signals

By Stefano Panzeri, Jakob H. Macke, Joachim Gross, Christoph Kayser. 2015

220 citations recorded by the source corpus.

Highlights • Neural population codes are organized at multiple spatial scales.• Microscopic organization of neural codes reveals a key role of neural heterogeneity.• Microscopic and population dynamics interact to make processing state-dependent.• Additional computational analyses of neural activity across resolutions are needed.

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

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Citations in the corpus

Multisensory Processes: A Balancing Act across the Lifespan (2016, background): “It has now become evident that interactions based on the physical attributes of multisensory inputs do not fully account for the products of multisensory interactions [92,93].”
Computational neuroethology: a call to action (2019, background): “It is no coincidence that naturalistic behavior shares many characteristics with neural activity: high dimensionality, time-evolving structure, variability and organization at multiple temporal and spatial scales (Panzeri et al., 2015).”
Deconstructing multivariate decoding for the study of brain function (2017, background): “This is because the inactivity and activity in both cases carry information about the presence or absence of an object (Panzeri et al., 2015).”
Finding Neurons in a Haystack: Case Studies with Sparse Probing (2023, background): “Finally population coding theorizes the responses of a whole brain region are relevant, analogous to the computations of a full layer being required to compute or represent a feature [62, 63].”
A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula (2016, methodology): “We have focused here on application to M/EEG data, but we emphasize that GCMI can be applied to any signal, facilitating the comparative study of neural information coding across experimental methodologies and scales of brain measurement [Panzeri et al., 2015].”
The multisensory function of the human primary visual cortex. (2016, background): “…between single-unit activity and BOLD/ERP etc. responses is not forcibly straightforward, and phenomena at the synaptic level might not be a direct translation of those at the single-unit level (we would defer the interested reader to Panzeri et al. (2015) for a recent treatment of this issue).”
Neural interactions in the human frontal cortex dissociate reward and punishment learning (2024, background): “This view is in line with recent literature showing that prefrontal cortex contains single neurons exhibiting mixed selectivity for multiple task variables (Meyers et al., 2008; Rigotti et al., 2013; Stokes et al., 2013; Panzeri et al., 2015; Parthasarathy et al., 2017; Bernardi et al., 2020).”