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Transcript
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 The problem: many possible responses to a stimulus sensory information past experiences current goals constraints behavior 1 behavior 2 pick up with left hand pick up with right hand How to get information to the right place depending on the context? Solution 1: multiple sensory networks switched by context context 1 S1 S2 M1 M2 Solution 1: multiple sensory networks switched by context context 2 S1 S2 M1 M2 Solution 2: single network of sensory neurons modulated by context context 1 M1 M2 Solution 2: single network of sensory neurons modulated by context context 2 M1 M2 In a neural population, small changes in gain are equivalent to a full switch Gain modulation Gain modulation is a nonlinear interaction between two inputs to a neuron Primary input: defines sensory selectivity Modulatory input: affects the amplitude of the response to a primary input, but not its selectivity Classic example: parietal cortex Activity (spikes/sec) (R) (U) (L) (D) Location of stimulus (degrees) Brotchie PR, Andersen RA, Snyder LH (1995) Nature 375:232 (R) Network Architecture modulatory input (context) primary input (stim position) GM sensory rj = f(x) g(y) motor Ri = ∑ wij rj j • wij - connection from GM neuron j to output neuron i • Encoded target location is center of mass of output units • wij set to minimize difference between desired and driven output Model GM responses GM neuron 1 Firing rate 40 -20 0 Stimulus location 20 Model GM responses Firing rate GM neuron 2 -20 0 Stimulus location 20 Simulation Gain modulation by context • In a neural population, small changes in gain are equivalent to a full switch • A population of sensory neurons gainmodulated by context can be used to change the functional connectivity between sensory and motor networks 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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