1. MODELING THE RECPTIVE FIELD ORGANIZATION OF
OPTIC FLOW SELECTIVE MST NEURONS
Chen-Ping Yu+, William K. Page*, Roger Gaborski+, and Charles J. Duffy
Dept. of Neurology, Univ. of Rochester, Rochester, NY 14642
+Dept. of Computer Science, Rochester Institute of Technology, Rochester, NY 14623
INTRODUCTION
The radial pattern of optic flow surrounds the moving observer and provides robust cues about the direction of self-movement as the flow field’s focus of expansion (FOE).
Local Motion Composition Of
The Global Pattern In Optic Flow
The optic flow field contains a spectrum of local directional segments each of which contains somewhat different directions of approximately planar local motion.
Here we examine whether simultaneously presented patches of local motion reveal MST neuronal response interactions that might support global pattern selectivity.
Single site, local motion data yield dual-Gaussian fits combining an excitatory and inhibitory mechanism, or two excitatory, or two inhibitory mechanisms. In the latter cases, the two can be so similar as to be construed as a single mechanism.
The local motion model of 819R09 shows an irregular fit to the optic flow response data, suggesting local motion mechanisms partially account for the global pattern selectivity.
Dual Gaussian Model of MST Response Field Can Fit Optic Flow Data
Dual Gaussian Model
(derived from single site, local motion data)
Excitatory
Inhibitory
Gaussian
Parameters
Length=Gain
Head=Width
Local Motion Stimuli
Optic Flow Stimuli
Model Training Fit
Model Testing Fit
Neuron
Model
Firing Rate (spks/s)
We applied the genetic algorithm to modify the dual Gaussian, single stimulus receptive field model for each Hot Spot direction.
We interpolated between dual stimulus data sets to create versions that represent effects at intermediate Hot Spot directions.
Responses to optic flow were predicted by the version of the model having the local motion direction at the tested Hot Spot.
2 Site Data Transforms 1 Site Model for Each Hot Spot Direction
Singles Model
Center Hot Spot Right
Center Hot Spot Up
Center Hot Spot Down
Center Hot Spot Left
Neuron 819R34
METHODS: MST Neuronal Responses to Optic Flow and Local Motion
We first recorded the responses of MST neurons in monkeys viewing dot pattern optic flow stimuli simulating movement in 3D space during centered visual fixation on a 90o X 90o rear projection screen.
We then recorded the responses of these neurons to 30o X 30o patches of local planar motion by presenting dot pattern motion in four cardinal directions on an otherwise blank screen.
Local Motion Stimuli
(4 directions of local motion at 9 sites 30o2)
Optic Flow Responses
Simulate Observer Movement in 16 Directions
Optic Flow Stimulus 80 60 40 20
Discharge
Rate (
spk
/sec)
Neuron 819R09
Dual Local Motion Stimuli Reveal Direction Selective Interactions
We hypothesized that interactions between local response mechanisms might alter the net directionality of MST receptive fields and promote global pattern selectivity. We tested this hypothesis by simultaneously presenting local motion stimuli at two sites in the receptive field, revealing a diverse set of complex interactions.
Hot
Spot
Dual Simultaneous Stimulation
Two Hot Spot Directions X 4 Test Spot Directions
Single Site Stimulation
One Site X 4 Directions
Neuron
819R10 50
spks/s
500 ms
Test
Optic Flow Responses Predicted By Model With Its Hot Spot Direction
We compared single and dual stimulus models by ability to fit optic flow responses. Responses were divided in to three levels by k-means cluster analysis (typically either: no / small / large response or inhibitory / no / excitatory response). The diverse set of results is assessed by the number of points that matched cluster classification.
Optic Flow Stimuli
Normalized Firing Rate (spks/s)
Single Stimulus Model of Optic Flow Responses
Dual Stimulus Model of Optic Flow Responses
Neuron 819R34
Class Error: 15
Class Error: 5
Neuron
Model
METHODS: Dual Gaussian Response Field Modeling
Training with Genetic Algorithm
Randomly Generate 2550 Gaussian Models
Assess fit of each model to neuron response data
Each model: 18 Gaussians (2 for each of 9 sites)
Gain Direction Width Polarity
25 models with
least total error
across stimuli
(firing rate)
fewest response
group error
(3 clusters)
Cross-over models at random sites to yield 2550 new models
….. X
Repeat across 75 generations (asymptotic error reduction)
9 Site, Dual Gaussian Model
Of MST Receptive Fields
Single site, local motion stimuli yield directional response profiles, typically modeled by combined excitatory and inhibitory mechanisms.
Excitatory
Gaussian
Inhibitory
Vector differences between the single and the dual site data represent dual site interactions with that Hot Spot direction.
Transforms for sites not in dual site study are then interpolated from neighboring sites.
Dual site data is used to modify the singles data receptive field model for optic flow having that local motion direction at that Hot Spot location.
METHODS: 2 Site Data Changes 1 Site Model of Optic Flow Response
Dual Site Data
(Lt-Up Hot Spot Lt)
Single Site Data
(Only Dual Sites Shown)
Single to Dual Transform
(Lt-Up w/ Lt; interpolate sites)
( )
Single Site Model
Transformed Model
(For Flow w/ Lt-Up w/ Lt )
Interpolated Transform
(Lt-Up w/ Lt + interpolation)
PRELIMINARY SUMMARY
MST neuronal responses to optic flow are not accounted for by the array of local motion responses.
Dual Gaussian models derived by genetic algorithm fit single site local motion, but not optic flow responses.
Dual simultaneous stimuli reveal dynamic interactions between sites throughout the receptive field.
Fits to optic flow responses can be improved by transforming models using dual site response interactions.
This work was supported by grants from NEI (R01EY10287, P30EY01319).
Evaluate model across sample of 60 neurons recorded with optic flow, single site, and selected dual site stimuli.
Assess impact of the dual site transforms in modeling early phasic responses versus late tonic responses to local motion and optic flow stimuli.
Create a Monte Carlo simulation of dual site transforms by applying each neuron’s dual site transforms to a) other sites in the neuron, & b) all sites in all other neurons.
CONTINUED DEVELOPMENT