Conflict Resolution as Near-Threshold Decision-Making: A Spiking Neural Circuit Model with Two-Stage Competition for Antisaccadic Task
Fig 4
1 Simulated neural activity in prosaccade and antisaccade tasks.
The model exhibits distinct neuronal activity (population firing rate) between prosaccade and antisaccade and between fast and slow errors, giving insight into how errors are produced. Here we display population firing rates from four sample trials in the Gap task and the visual target is on the left in all panels. Neural activity in the NoGap or Overlap tasks is qualitatively similar with that of the Gap task displayed here. A. Neurons in the direct map (Dir neurons) are dominant by default. Therefore they exhibit stronger response to the target signal in prosaccade trials. As a result, the corresponding downstream decision neurons (in DecL) on the left win the competition against the decision neurons (in DecR) on the right. DecL then activates the downstream saccade neurons (SacL) and triggers a prosaccade. B. In antisaccade, the top-down control suppresses neurons in the direct map pathway (DirL) while facilitates neurons in the inverted pathway (InvL). The strongly responded InvL neurons drive downstream DecR decision neurons which in turn activate saccade neurons (in SacR) and trigger an antisaccade. The model exhibits two types of errors, fast and slow, in antisaccade trials. C Fast errors are produced due to the subjects being unable to withhold a saccade against the direct target signal input. The decisions were not yet reached in the decision layer in the remapping module when the erroneous saccades were triggered by the action-selection module. D Slow errors originate from wrong decisions made in the decision layer (middle panel). In these trials the subjects were able to withhold a saccade initially against the strong input from the target signal. But the subsequently arrived signal from the decision module carries the wrong information.