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Gain modulation as a mechanism for the selection of functional circuits

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. sensory information. past experiences current goals

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Gain modulation as a mechanism for the selection of functional circuits

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  1. 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

  2. sensory information past experiences current goals constraints behavior 1 behavior 2 pick up with left hand pick up with right hand The problem: many possible responses to a stimulus

  3. How to get information to the right place depending on the context?

  4. Solution 1: multiple sensory networks switched by context context 1 S1 S2 M1 M2

  5. Solution 1: multiple sensory networks switched by context context 2 S1 S2 M1 M2

  6. context 1 Solution 2: single network of sensory neurons modulated by context M1 M2

  7. context 2 Solution 2: single network of sensory neurons modulated by context M1 M2

  8. In a neural population, small changes in gain are equivalent to a full switch

  9. 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

  10. Activity (spikes/sec) (R) (U) (L) (D) (R) Location of stimulus (degrees) Brotchie PR, Andersen RA, Snyder LH (1995) Nature375:232

  11. Network Architecture modulatory input (context) primary input (stim position) rj = f(x) g(y) GM sensory 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

  12. GM neuron 1 40 Firing rate -20 0 20 Stimulus location Model GM responses

  13. GM neuron 2 Firing rate -20 0 20 Stimulus location Model GM responses

  14. Simulation

  15. 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

  16. 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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