Title :
Spiking neuron model of basal forebrain enhancement of visual attention
Author :
Avery, Michael ; Krichmar, Jeffrey L. ; Dutt, Nikil
Author_Institution :
Dept. of Cognitive Sci., Univ. of California, Irvine, CA, USA
Abstract :
Attentional mechanisms allow the brain to enhance the representation and transmission of certain signals at the expense of others. The basal forebrain has been shown to play an important role in attention through its diverse set of interactions with sensory and associational areas. A recent empirical study indicates that the nucleus basalis, a subset of neurons located in the basal forebrain, is important for improving sensory processing by increasing reliability and decreasing redundancy in the cortex and thalamus [1, 2]. We developed a spiking neural network model that simulates the nucleus basalis´ interaction with the thalamus and visual cortex. In this model, we simulated two modes of action by which it is thought that the nucleus basalis may be influencing sensory processing: (1) inhibitory projections from the nucleus basalis to the thalamic reticular nucleus, which disinhibit the lateral geniculate nucleus (LGN) and gate information into the cortex, and (2) cholinergic excitation of inhibitory neurons in the visual cortex. We showed that the inhibition of the thalamic reticular nucleus GABAergic neurons leads to an increase in the reliability of spikes in the LGN and cortex. We observed that a decrease in the burst to tonic firing ratio in the LGN, coupled with the cholinergic system increasing inhibition in the visual cortex caused decorrelation in the cortex. These findings will help us better understand the mechanisms behind the control of attention by the basal forebrain and shed light on how the orchestrated action of the basal forebrain on multiple target areas can improve information processing in the brain.
Keywords :
brain models; decorrelation; medical signal processing; neural nets; redundancy; GABAergic neurons; LGN; attentional mechanisms; basal forebrain enhancement; brain information processing improvement; cholinergic excitation; decorrelation; inhibitory neurons; lateral geniculate nucleus; nucleus basalis interaction simulation; redundancy; reliability; sensory processing; signal representation enhancement; signal transmission enhancement; spiking neuron model; thalamic reticular nucleus inhibition; tonic firing ratio; visual attention; visual cortex; Brain modeling; Correlation; Decorrelation; Neurons; Niobium; Reliability; Visualization; attention; computational neuroscience; microcircuit; neuromodulation; spiking neurons; vision;
Conference_Titel :
Neural Networks (IJCNN), The 2012 International Joint Conference on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1488-6
Electronic_ISBN :
2161-4393
DOI :
10.1109/IJCNN.2012.6252578