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Mechanisms underlying the basal forebrain enhancement of top‐down and bottom‐up attention
Authors:Michael C. Avery  Nikil Dutt  Jeffrey L. Krichmar
Affiliation:1. Department of Cognitive Sciences, University of California, , Irvine, CA, 92697‐5100 USA;2. Department of Computer Sciences, University of California Donald Bren School of Information and Computer Sciences, , CA, 92697‐3435
Abstract:Both attentional signals from frontal cortex and neuromodulatory signals from basal forebrain (BF) have been shown to influence information processing in the primary visual cortex (V1). These two systems exert complementary effects on their targets, including increasing firing rates and decreasing interneuronal correlations. Interestingly, experimental research suggests that the cholinergic system is important for increasing V1's sensitivity to both sensory and attentional information. To see how the BF and top‐down attention act together to modulate sensory input, we developed a spiking neural network model of V1 and thalamus that incorporated cholinergic neuromodulation and top‐down attention. In our model, activation of the BF had a broad effect that decreases the efficacy of top‐down projections and increased the reliance of bottom‐up sensory input. In contrast, we demonstrated how local release of acetylcholine in the visual cortex, which was triggered through top‐down gluatmatergic projections, could enhance top‐down attention with high spatial specificity. Our model matched experimental data showing that the BF and top‐down attention decrease interneuronal correlations and increase between‐trial reliability. We found that decreases in correlations were primarily between excitatory–inhibitory pairs rather than excitatory–excitatory pairs and suggest that excitatory–inhibitory decorrelation is necessary for maintaining low levels of excitatory–excitatory correlations. Increased inhibitory drive via release of acetylcholine in V1 may then act as a buffer, absorbing increases in excitatory–excitatory correlations that occur with attention and BF stimulation. These findings will lead to a better understanding of the mechanisms underyling the BF's interactions with attention signals and influences on correlations.
Keywords:computational model  correlated firing  neuromodulation  vision
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