2. 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

3. 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

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

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

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

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

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

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

10. 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

11. Brotchie PR, Andersen RA, Snyder LH (1995) Nature 375:232 Location of stimulus (degrees) Activity (spikes/sec) (R) (U)(L)(D)(R)

12. 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

13. Model GM responses 40 Firing rate Stimulus location GM neuron 1 020-20

14. Model GM responses Firing rate Stimulus location GM neuron 2 020-20

15. Simulation

16. 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

17. 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