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URMC / Labs / Cognitive Neuroscience Lab / Projects / Receptive-Field-Dynamics

Receptive Field Dynamics

Location Motion Model

Location Motion Model. A. Mixture of Gaussians
model derived from local motion responses in the nine
stimulus segments. B. Corresponding fit of the local
motion model to neuronal responses to the 36 planar stimuli.
C. Corresponding fit for the local motion model (
to the 16 optic flow patterns showing a poor model fit to
the neuronal responses .

Optic flow informs moving observers about their heading direction. Neurons in monkey medial superior temporal (MST) cortex show heading selective responses to optic flow and planar direction selective responses to patches of local motion. We recorded MST neuronal responses to a 90° X 90° optic flow display, and to a 3 X 3 array of local motion patches covering the same area. Our goal was to test the hypothesis that the optic flow responses reflect the sum of the local motion responses. The local motion responses of each neuron were modeled as mixtures of Gaussians, combining the effects of two Gaussian response functions derived using a genetic algorithm, and then used to predict that neuron’s optic flow responses. Some neurons showed good correspondence between local motion models and optic flow responses, others showed substantial differences.

We used the genetic algorithm to modulate the relative strength of each local motion segment's responses to accommodate interactions between segments that might modulate their relative efficacy during co-activation by global patterns of optic flow. These gain modulated models showed uniformly better fits to the optic flow responses, suggesting that co-activation of receptive field segments alters neuronal response properties.

We tested this hypothesis by simultaneously presenting local motion stimuli at two different sites. These two-segment stimuli revealed that interactions between response segments have direction and location specific effects that can account for aspects of optic flow selectivity. We conclude that MST's optic flow selectivity reflects dynamic interactions between spatially distributed local planar motion response mechanisms.

Segment Gains

Segment Gains. D. Mixture of Gaussians model
after segmental gain modulation. E. Segmental
gain changes that were implemented by the model
to modulate the weight of the mixture of Gaussians
model in each of the nine segments (increases, ;
decreases, full-scale +/- 10x).
F. Fit of the segment gain modulated model to the 16
optic flow patterns shows a substantial improvement for
all optic flow stimuli compared to that of the unmodulated
local motion model shown in C.

Response Gains

Response Gains. G. Mixture of Gaussians model
after response gain modulation. H. Response
gain changes that were implemented by the model
to modulation the weight of each of the 18 Gaussians
(increases, above line; decreases, below line;
excitatory Gaussians, inhibitory
Gaussians, full-scale +/-10x).
I. Fit of the response gain model to the 16 optic
flow patterns shows a substantial improvement for
all optic flow stimuli compared to both the unmodulated
local motion model (C and the segment gain modulated model F).

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