Population rate dynamics and multineuron firing patterns in sensory cortex
Identifieur interne : 002272 ( Main/Merge ); précédent : 002271; suivant : 002273Population rate dynamics and multineuron firing patterns in sensory cortex
Auteurs : Michael Okun [Royaume-Uni] ; Pierre Yger [Royaume-Uni] ; Stephan Marguet [États-Unis] ; Florian Gerard-Mercier [Royaume-Uni, France] ; Andrea Benucci [États-Unis] ; Steffen Katzner [États-Unis] ; Laura Busse [États-Unis] ; Matteo Carandini [Royaume-Uni, États-Unis] ; Kenneth D. Harris [Royaume-Uni, États-Unis]Source :
- The Journal of neuroscience : the official journal of the Society for Neuroscience [ 0270-6474 ] ; 2012.
Abstract
Cortical circuits encode sensory stimuli through the firing of neuronal ensembles, and also produce spontaneous population patterns in the absence of sensory drive. This population activity is often characterized experimentally by the distribution of multineuron “words” (binary firing vectors), and a match between spontaneous and evoked word distributions has been suggested to reflect learning of a probabilistic model of the sensory world. We analyzed multineuron word distributions in sensory cortex of anesthetized rats and cats, and found that they are dominated by fluctuations in population firing rate rather than precise interactions between individual units. Furthermore, cortical word distributions change when brain state shifts, and similar behavior is seen in simulated networks with fixed, random connectivity. Our results suggest that similarity or dissimilarity in multineuron word distributions could primarily reflect similarity or dissimilarity in population firing rate dynamics, and not necessarily the precise interactions between neurons that would indicate learning of sensory features.
Url:
DOI: 10.1523/JNEUROSCI.1831-12.2012
PubMed: 23197704
PubMed Central: 3520056
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<front><div type="abstract" xml:lang="en"><p id="P1">Cortical circuits encode sensory stimuli through the firing of neuronal ensembles, and also produce spontaneous population patterns in the absence of sensory drive. This population activity is often characterized experimentally by the distribution of multineuron “words” (binary firing vectors), and a match between spontaneous and evoked word distributions has been suggested to reflect learning of a probabilistic model of the sensory world. We analyzed multineuron word distributions in sensory cortex of anesthetized rats and cats, and found that they are dominated by fluctuations in population firing rate rather than precise interactions between individual units. Furthermore, cortical word distributions change when brain state shifts, and similar behavior is seen in simulated networks with fixed, random connectivity. Our results suggest that similarity or dissimilarity in multineuron word distributions could primarily reflect similarity or dissimilarity in population firing rate dynamics, and not necessarily the precise interactions between neurons that would indicate learning of sensory features.</p>
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