Attentional recruitment of inter-areal recurrent networks for selective gain control.
There is strong anatomical and physiological evidence that neurons with large receptive fields located in higher visual areas are recurrently connected to neurons with smaller receptive fields in lower areas. We have previously described a minimal neuronal network architecture in which top-down attentional signals to large receptive field neurons can bias and selectively read out the bottom-up sensory information to small receptive field neurons (Hahnloser, Douglas, Mahowald, & Hepp, 1999). Here we study an enhanced model, where the role of attention is to recruit specific inter-areal feedback loops (e.g., drive neurons above firing threshold). We first illustrate the operation of recruitment on a simple example of visual stimulus selection. In the subsequent analysis, we find that attentional recruitment operates by dynamical modulation of signal amplification and response multistability. In particular, we find that attentional stimulus selection necessitates increased recruitment when the stimulus to be selected is of small contrast and of small distance away from distractor stimuli. The selectability of a low-contrast stimulus is dependent on the gain of attentional effects; for example, low-contrast stimuli can be selected only when attention enhances neural responses. However, the dependence of attentional selection on stimulus-distractor distance is not contingent on whether attention enhances or suppresses responses. The computational implications of attentional recruitment are that cortical circuits can behave as winner-take-all mechanisms of variable strength and can achieve close to optimal signal discrimination in the presence of external noise.
Hahnloser
Richard H R
RH
Howard Hughes Medical Institute, Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139 USA. rhahnloser@mit.edu
Douglas
Rodney J
RJ
Hepp
Klaus
K
D000818:Animals; D016477:Artifacts; D001288:Attention / Q000502:physiology*; D015350:Contrast Sensitivity / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D014793:Visual Cortex / Q000166:cytology / Q000502:physiology*; D014795:Visual Pathways / Q000166:cytology / Q000502:physiology*