MODELING THE RECPTIVE FIELD ORGANIZATION OF OPTIC FLOW SELECTIVE MST NEURONS

1. 2 Site Data Transforms 1 Site Model for Each Hot Spot Direction INTRODUCTION Dual Gaussian Model of MST Response Field Can Fit Optic Flow Data Local Motion Composition Of The Global Pattern In Optic Flow Dual Gaussian Model (derived from single site, local motion data) 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. Center Hot Spot Up 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). 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. Excitatory Inhibitory Gaussian Parameters Length=Gain Head=Width Center Hot Spot Left Center Hot Spot Right Singles Model The optic flow field contains a spectrum of local directional segments each of which contains somewhat different directions of approximately planar local motion. Model Testing Fit Model Training Fit Center Hot Spot Down Here we examine whether simultaneously presented patches of local motion reveal MST neuronal response interactions that might support global pattern selectivity. Firing Rate (spks/s) Neuron Neuron Model Model Neuron 819R34 Local Motion Stimuli Optic Flow Stimuli Dual Local Motion Stimuli Reveal Direction Selective Interactions METHODS: MST Neuronal Responses to Optic Flow and Local Motion 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 responseorinhibitory / no / excitatory response). The diverse set of results is assessed by the number of points that matched cluster classification. 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. 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. Single Stimulus Model of Optic Flow Responses Dual Stimulus Model of Optic Flow Responses Optic Flow Responses Simulate Observer Movement in 16 Directions Dual Simultaneous Stimulation Single Site Stimulation Local Motion Stimuli (4 directions of local motion at 9 sites 30o2) Neuron Neuron Model Model Two Hot Spot Directions X 4 Test Spot Directions Class Error: 15 Class Error: 5 One Site X 4 Directions 80 80 Hot Spot /sec) /sec) 60 60 spk spk Test Spot Test Spot Test Spot Normalized Firing Rate (spks/s) Normalized Firing Rate (spks/s) Rate ( Rate ( 40 40 50 50 50 50 spks/s spks/s spks/s spks/s Discharge Discharge 20 20 500 ms 500 ms 500 ms 500 ms 0 0 Neuron 819R10 Optic Flow Stimuli Optic Flow Stimuli Neuron 819R34 Optic Flow Stimulus Optic Flow Stimulus Neuron 819R09 Neuron 819R09 METHODS: Dual Gaussian Response Field Modeling CONTINUED DEVELOPMENT METHODS: 2 Site Data Changes 1 Site Model of Optic Flow Response PRELIMINARY SUMMARY 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. 9 Site, Dual Gaussian Model Of MST Receptive Fields Single Site Data (Only Dual Sites Shown) Dual Site Data (Lt-Up Hot Spot Lt) Single to Dual Transform (Lt-Up w/ Lt; interpolate sites) • 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. • 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. Training with Genetic Algorithm Randomly Generate 2550 Gaussian Models Each model: 18 Gaussians (2 for each of 9 sites) Gain Direction WidthPolarity 0011100 101110101 1100101 001101100 Excitatory Gaussian ( ) Assess fit of each model to neuron response data Inhibitory Gaussian 25 models with least total error across stimuli (firing rate) 25 models with fewest response group error (3 clusters) Interpolated Transform (Lt-Up w/ Lt + interpolation) Transformed Model (For Flow w/ Lt-Up w/ Lt ) Single Site Model Single site, local motion stimuli yield directional response profiles, typically modeled by combined excitatory and inhibitory mechanisms. ( ) X 00111001….. 00111001….. Cross-over models at random sites to yield 2550 new models Repeat across 75 generations (asymptotic error reduction) This work was supported by grants from NEI (R01EY10287, P30EY01319). 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