"Dynamics of perceptual decision-making under temporal uncertainty", Nestor Parga

Dear All,

Neural Computation Unit (Doya Unit) would like to invite you to a seminar as follows.

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Date: Wednesday, November 25, 2015
Time: 15:00-16:00
Venue: Meeting Room D015 - L1 Bldg
Speaker: Dr. Nestor Parga
Depto. de Fisica Teorica, Universidad Autonoma de Madrid

Title: Dynamics of perceptual decision-making under temporal uncertainty.

Abstract:　Perceptual detection requires the extraction of information about the world from noisy sensory signals. Under uncertainty, the brain uses previous knowledge acquired during learning to transform the sensory inputs into the percepts on which decisions are based. How is this achieved when the uncertainty lies in the timing of the task? We studied this issue using a computational model and data recorded in monkeys performing a perceptual detection task in which the stimulation time is variable [1].

Previous knowledge about the task should be reflected in the probability of erroneously reporting a stimulus in stimulus-absent trials (false alarms). We found that premotor cortex neurons' activity during single false alarm trials shows localized fluctuations that resemble their activity under stimulation. We devised a procedure to extract the times of these events and found that their distribution over time is not uniform but increases with the probability of stimulation.

What mechanisms support detection under temporal uncertainty? We used a trained recurrent neural network to answer this question. Importantly, the information provided during training was restricted to the behavioral outcome. The resulting network learns to perform the task. Furthermore, it is able to infer the task's temporal structure as shown by the probability of false alarm events. In addition, reverse engineering the model allows us to uncover possible dynamical principles underlying detection under temporal uncertainty.

[1] Carnevale F, de Lafuente V, Romo R, Barak O, Parga N (2015). Neuron 86, 1067–1077.

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We hope to see many of you.

Sincerely,　

Emiko Asato
Neural Computation Unit