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Gain modulation as a mechanism for the selection of functional circuits Emilio Salinas Melanie Wyder Nick Bentley Dept. of Neurobiology and Anatomy Wake Forest University School of Medicine Winston-Salem, NC Banbury Center, May, 2004
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The problem: many possible responses to a stimulus behavior 1 sensory information pick up with left hand behavior 2 pick up with right hand past experiences current goals constraints
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How to get information to the right place depending on the context?
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Solution 1: multiple sensory networks switched by context S1 M1 context 1 S2 M2
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Solution 1: multiple sensory networks switched by context S1 M1 context 2 S2 M2
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Solution 2: single network of sensory neurons modulated by context M1 M2 context 1
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Solution 2: single network of sensory neurons modulated by context M1 M2 context 2
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In a neural population, small changes in gain are equivalent to a full switch
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Gain modulation l Gain modulation is a nonlinear interaction between two inputs to a neuron l Primary input: defines sensory selectivity Modulatory input: affects the amplitude of the response to a primary input, but not its selectivity l Classic example: parietal cortex
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Brotchie PR, Andersen RA, Snyder LH (1995) Nature 375:232 Location of stimulus (degrees) Activity (spikes/sec) (R) (U)(L)(D)(R)
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Network Architecture r j = f(x) g(y) R i = ∑ w ij r j j w ij - connection from GM neuron j to output neuron i Encoded target location is center of mass of output units w ij set to minimize difference between desired and driven output primary input (stim position) modulatory input (context) GM sensory motor
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Model GM responses 40 Firing rate Stimulus location GM neuron 1 020-20
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Model GM responses Firing rate Stimulus location GM neuron 2 020-20
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Simulation
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Gain modulation by context In a neural population, small changes in gain are equivalent to a full switch A population of sensory neurons gain- modulated by context can be used to change the functional connectivity between sensory and motor networks
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Predictions Neurons should respond to both stimulus and context All combinations of preferred stimuli and contexts should be represented Stimulus-context interaction should be non-linear
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