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Nat Commun. 2015 Feb 4;6:6177. doi: 10.1038/ncomms7177.

Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT.

Author information

1
Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), C/Rosselló 149, 08036 Barcelona, Spain.
2
1] Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), C/Rosselló 149, 08036 Barcelona, Spain [2] Centre de Recerca Matemàtica (CRM), Campus de Bellaterra, Edifici C, 08193 Barcelona, Spain.
3
1] Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, 1400-038 Lisbon, Portugal [2] Department of Neurobiology, Stanford University, 299 Campus Drive West , Stanford, California 94305-5125, USA.
4
Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, 1400-038 Lisbon, Portugal.

Abstract

Neuronal variability in sensory cortex predicts perceptual decisions. This relationship, termed choice probability (CP), can arise from sensory variability biasing behaviour and from top-down signals reflecting behaviour. To investigate the interaction of these mechanisms during the decision-making process, we use a hierarchical network model composed of reciprocally connected sensory and integration circuits. Consistent with monkey behaviour in a fixed-duration motion discrimination task, the model integrates sensory evidence transiently, giving rise to a decaying bottom-up CP component. However, the dynamics of the hierarchical loop recruits a concurrently rising top-down component, resulting in sustained CP. We compute the CP time-course of neurons in the medial temporal area (MT) and find an early transient component and a separate late contribution reflecting decision build-up. The stability of individual CPs and the dynamics of noise correlations further support this decomposition. Our model provides a unified understanding of the circuit dynamics linking neural and behavioural variability.

PMID:
25649611
PMCID:
PMC4347303
DOI:
10.1038/ncomms7177
[Indexed for MEDLINE]
Free PMC Article

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