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Neuronal variability and choice variability in a hierarchical network model of perceptual decisions
Klaus Wimmer1 and Genis Prat Ortega1,2 and Albert Compte1 and Alex Roxin1,2 and Diogo Peixoto3,4 and Alfonso Renart3 and Jaime de la Rocha1
1IDIBAPS, Barcelona, Spain 2CRM, Barcelona, Spain 3Champalimaud, Lisbon, Portugal 4Stanford Univ., Stanford, CA, USA

The trial-to-trial variability of cortical neurons correlates with variability in behavioral reports. This relationship can arise from sensory variability biasing behaviour, as the result of cortical population dynamics, and from top- down signals reflecting behaviour [1,2]. Here, we study the impact of the dynamics of sensory evidence integration in perceptual decision tasks on the "choice probability" (CP) of sensory neurons. To examine the interaction of the different 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.

[1] K. Wimmer, D.Q. Nykamp, C. Constantinidis, and A.Compte. Nat. Neurosci. 17(3):431-439 (2014).
[2] K. Wimmer, A. Compte, A. Roxin, D. Peixoto, A. Renart, and J. de la Rocha. Nat. Commun. 6:6177 (2015).