• DocumentCode
    2773126
  • 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
  • fYear
    2012
  • fDate
    10-15 June 2012
  • Firstpage
    1
  • Lastpage
    8
  • 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;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), The 2012 International Joint Conference on
  • Conference_Location
    Brisbane, QLD
  • ISSN
    2161-4393
  • Print_ISBN
    978-1-4673-1488-6
  • Electronic_ISBN
    2161-4393
  • Type

    conf

  • DOI
    10.1109/IJCNN.2012.6252578
  • Filename
    6252578