Stimulus driven correlation gain modulation in neuronal networks

Gain modulation of recurrent networks

Gain modulation is an important mechanism by which attentional and other inputs modify the amplitude of neuronal responses without changing their selectivity. Gain modulation has been studied previously in feedforward circuits but not in recurrent neural networks. We show how gain modulation modi"es the response of a recurrent network to feedforward inputs. Even modest gain modulation of the re...

Informational cost in correlation-based neuronal networks

The cost of maintaining a given level of activity in a neuronal network depends on its size and degree of connectivity. Should a neural function require large-size fully-connected networks, the cost can easily exceed metabolic resources, especially for high level neural functions. We show that, even in this case, the cost can still match the energetic resources provided the function is broken d...

Dynamic stability of sequential stimulus representations in adapting neuronal networks

The ability to acquire and maintain appropriate representations of time-varying, sequential stimulus events is a fundamental feature of neocortical circuits and a necessary first step toward more specialized information processing. The dynamical properties of such representations depend on the current state of the circuit, which is determined primarily by the ongoing, internally generated activ...

Goal-driven modulation of stimulus-driven attentional capture in multiple-cue displays.

Six location-cuing experiments were conducted to examine the goal-driven control of attentional capture in multiple-cue displays. In most of the experiments, the cue display consisted of the simultaneous presentation of a red direct cue that was highly predictive of the target location (the unique cue) and three gray direct cues (the standard cues) that were not predictive of the location. The ...

Stimulus-Driven Unsupervised Synaptic Pruning in Large Neural Networks