Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework
Fig 2
Perceptual decision-making task.
(A) Inputs (upper) and target outputs (lower) for a perceptual decision-making task with variable stimulus duration, which we refer to as VS here. The choice 1 output must hold low during fixation (fix.), then high during the decision (dec.) period if the choice 1 input is larger than choice 2 input, low otherwise, and similarly for the choice 2 output. There are no constraints on output during the stimulus period. (B) Inputs and target outputs for the reaction-time version of the integration task, which we refer to as RT. Here the outputs are encouraged to respond after a short delay following the onset of stimulus. The reaction time is defined as the time it takes for the outputs to reach a threshold. (C) Psychometric function for the VS version, showing the percentage of trials on which the network chose choice 1 as a function of the signed coherence. Coherence is a measure of the difference between evidence for choice 1 and evidence for choice 2, and positive coherence indicates evidence for choice 1 and negative for choice 2. Solid line is a fit to a cumulative Gaussian distribution. (D) Psychometric function for the RT version. (E) Percentage of correct responses as a function of stimulus duration in the VS version, for each nonzero coherence level. (F) Reaction time for correct trials in the RT version as a function of coherence. Inset: Distribution of reaction times on correct trials. (G) Example activity of a single unit in the VS version across all correct trials, averaged within conditions after aligning to the onset of the stimulus. Solid (dashed) lines denote positive (negative) coherence. (H) Example activity of a single unit in the RT version, averaged within conditions and across all correct trials aligned to the reaction time.