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Data
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Towards a biologically plausible model of LGN-V1 pathways (Lian et al 2019)
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Yanbo Lian
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"Increasing evidence supports the hypothesis that the visual system
employs a sparse code to represent visual stimuli, where information
is encoded in an efficient way by a small population of cells that
respond to sensory input at a given time. This includes simple cells
in primary visual cortex (V1), which are defined by their linear
spatial integration of visual stimuli. Various models of sparse coding
have been proposed to explain physiological phenomena observed in
simple cells. However, these models have usually made the simplifying
assumption that inputs to simple cells already incorporate linear
spatial summation. This overlooks the fact that these inputs are known
to have strong non-linearities such as the separation of ON and OFF
pathways, or separation of excitatory and inhibitory
neurons. Consequently these models ignore a range of important
experimental phenomena that are related to the emergence of linear
spatial summation from non-linear inputs, such as segregation of ON
and OFF sub-regions of simple cell receptive fields, the push-pull
effect of excitation and inhibition, and phase-reversed
cortico-thalamic feedback. Here, we demonstrate that a two-layer model
of the visual pathway from the lateral geniculate nucleus to V1 that
incorporates these biological constraints on the neural circuits and
is based on sparse coding can account for the emergence of these
experimental phenomena, diverse shapes of receptive fields and
contrast invariance of orientation tuning of simple cells when the
model is trained on natural images. The model suggests that sparse
coding can be implemented by the V1 simple cells using neural circuits
with a simple biologically plausible architecture."
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Lian Y, Grayden DB, Kameneva T, Meffin H, Burkitt AN (2019) Show
Other
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LianEtAl2019
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Lian, Yanbo [yanbol at student.unimelb.edu.au] Show
Other
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yanbol@student.unimelb.edu.au
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V1 simple cells
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Yanbo Lian
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