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Massimiliano Versace, Stephen Grossberg; From spikes to objects: How multiple levels of thalamic and cortical interactions control visual learning. attention, and recognition. Journal of Vision 2006;6(6):885. doi: https://doi.org/10.1167/6.6.885.
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© ARVO (1962-2015); The Authors (2016-present)
How are multiple levels of brain organization coordinated to control visual learning, attention, and recognition? A realistic model of cortico-cortical and thalamo-cortical visual learning and information processing investigates how higher-order specific thalamic nuclei as well as nonspecific thalamic nuclei, such as the midline and intralaminar nuclei, interact with multiple cortical areas for this purpose. The model embodies detailed predictions about how the various layers of cortical and thalamic regions interact. It hereby proposes how synchronization of neuronal spiking occurs within and across multiple brain regions and thalamic nuclei and simulates the functional link between synchronization and spike-timing-dependent synaptic plasticity. The model is described in terms of spiking neurons that obey Hodgkin-Huxley type dynamics. Its operations explicate across multiple levels of description (biophysical, neurophysiological, field potentials, anatomical, and perceptual) operations from Adaptive Resonance Theory, in particular how bottom-up/top-down matches can lead either to attention, resonance, and learning, and how mismatches can cause reset and hypothesis testing. The model links data about cortical and thalamic neurophysiology and anatomy and single cell biophysics by explaining their role in realizing functional properties such as resonance/learning, reset/search, top-down attention, and synchrony. In particular, the model quantitatively simulates data on single cell biophysics, both cortical and subcortical, aggregate cell recordings (current-source densities and local field potentials), and single cell and large-scale oscillations in the gamma and beta frequency bands, while clarifying the functional meaning of different oscillation frequencies during visual learning, attention, and information processing. Supported in part by AFOSR, NSF and ONR
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