Gain modulation as a mechanism for the selection of

functional circuits

Emilio Salinas

Melanie WyderNick Bentley

Dept. of Neurobiology and AnatomyWake Forest University School of Medicine

Winston-Salem, NC

Banbury Center, May, 2004

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 experiencescurrent goalsconstraints

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

Solution 1: multiple sensory networks switched by context

S1

M1

context 1

S2

M2

Solution 1: multiple sensory networks switched by context

S1

M1

context 2

S2

M2

Solution 2: single network of sensory neurons modulated by context

M1 M2

context 1

Solution 2: single network of sensory neurons modulated by context

M1 M2

context 2

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

Brotchie PR, Andersen RA, Snyder LH (1995) Nature 375:232

Location of stimulus (degrees)

Act

ivit

y (s

pik

es/s

ec)

(R) (U) (L) (D) (R)

Network Architecture

rj = f(x) g(y)

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

primary input (stim position) modulatory input (context)

GM sensory

motor

Model GM responses

40

Fir

ing

rate

Stimulus location

GM neuron 1

0 20-20

Model GM responses

Fir

ing

rate

Stimulus location

GM neuron 2

0 20-20

Simulation

Gain modulation by context

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

• A population of sensory neurons gain-modulated 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