MODELING MST OPTIC FLOW RESPONSES USING RECEPTIVE FIELD SEGMENTAL INTERACTIONS

1. Recorded Data Model Training Recorded Data Model Training 819R64 40 Dual Gaussian Model of Singles Stimuli Fit to Single Segment Local Motion Responses 20 Neuron 819R64 Recorded Data Model Fit 35 15 Firing Rate (sp/s) 30 10 5 25 0 20 Stimulus Condition 15 METHODS: MST Neuronal Responses to Optic Flow Dual Site Stimuli Can Improve Model Fits to Optic Flow Responses Dual Gaussian Model Prediction of Optic Flow Response Profiles We recorded MST neuronal responses to optic flow in monkeys viewing dot pattern stimuli simulating movement in 3D space during centered visual fixation on a 90o X 90o rear projection screen. Each neuron yielded an optic flow response selectivity profile in which average firing rate during stimulus presentation and its variance (+/-sem) can be compared to baseline activity. Three dual Gaussian models, differing in initial random conditions, were derived from the nine segment local motion responses of each neuron. Each model was tested with the 16 optic flow stimuli to obtain predicted optic flow response profiles for each model of each neuron. Comparison to recorded optic flow response profiles reveals a wide range of residual errors, varying more between then within neurons. Responses to single and dual site local motion stimuli were combined to create a model for each of the 16 optic flow responses in each of the neurons. 10 Gain Modulated Model Dual Site Model (CCW) Singles Model We compared optic flow from the singles, gain modulated, and dual site models. Neurons with poorer singles and modulated fits improved with the use of dual site response data, despite the limited number of dual site stimuli that could be tested. 5 0 Single Neuron Optic Flow Response Profile Single Neuron Responses to Optic Flow Singles Model Fit to Optic Flow Responses Total Error for 3 Model Runs per Neuron Neuron 819R64 Neuron 819R64 50 Neuron 819R34 40 60 60 60 Firing Rate (sp/s +/-sem) Firing Rate (sp/s +/- sem) 30 Total Error of Model Fit Across Optic Flow Responses (normalized firing rate) 40 40 40 Firing Rate (sp/s) 20 20 20 20 10 0 0 10 20 30 40 50 0 0 Neuron (from best to worst fit) Optic Flow Stimulus Stimulus Condition Stimulus Condition Stimulus Condition Optic Flow Stimulus Gain Modulated Dual Gaussian Models Improve Fit to Optic Flow METHODS: MST Neuronal Responses to Local Motion We tested the hypothesis that the relative influence of each Gaussian might be altered during co-stimulation by optic flow. The relative weights of the 18 Gaussians were first randomized, maintaining the same total activation, and then modulated by the genetic algorithm to optimize the fit to that neuron’s optic flow responses. The gain modulated models showed improved fits to optic flow with regional transformations within the model. We then presented planar motion in nine 30o X 30o patches covering the 90o X 90o rear projection screen. Four planar motion directions were presented across the nine patches in a random sequence. Local motion evoked spatially and directionally selective responses with different direction selectivities seen at different locations across the screen. Model Fit Comparisons Gain Modulated Error 120 100 80 Local Motion Responses 60 40 20 Four Directionsof Local Motion Stimuli Nine Sites of Local Motion Stimuli Singles Model Error 0 Fits to Optic Flow Responses Singles Model to Local Motion Gain Modulated Model Model Transform Recorded Data Neuron 712R02 Singles Model Gain Modulated Firing Rate (sp/s) Stimulus Condition MODELING MST OPTIC FLOW RESPONSES USING RECEPTIVE FIELD SEGMENTAL INTERACTIONS 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 Dual Gaussian Model Derived from Local Motion Responses Dual Site Local Motion Stimulation Shows Regional Interactions INTRODUCTION Local Motion Composition Of The Global Pattern In Optic Flow Four direction, single segment stimuli presented in nine segments were fit by a genetic algorithm to a dual Gaussian model of response amplitude X stimulus direction. The dual Gaussian fit allows for MST’s common superposition of inhibitory and excitatory processes, but often yielded best fits to local motion stimuli by two excitatory or two inhibitory response mechanisms. All fits were very good. The regional interactions suggested by the success of the gain modulated models were assessed by presenting dual, simultaneous local motion stimuli. Four local motion directions evoked different direction selectivities at different sites within the receptive field. When those stimuli were presented together they revealed direction specific interactions that yielded a variety of unique direction selective effects. Forward translational movement through the environment creates a radial pattern of optic flow that surrounds the moving observer and provides cues about heading direction. Optic flow contains different directions of local planar motion in different parts of the visual field and in different segments of the large receptive fields of MST neurons. Here we extend our efforts to derive models of MST receptive fields that rely on local motion processing to create optic flow heading selective responses. CONCLUSIONS SUMMARY • The optic flow responses of MST neurons are associated with diverse arrays of local motion effects. • Dual Gaussian models of local motion responses can predict optic flow responses in some neurons. • Modulating the relative influence of different sites can improve the fits of the models to optic flow responses. • Direction selective interactions between sites explain differences in local motion and optic flow responses. • Optic flow responses reflect local motion directional mechanisms arranged within the receptive fields of MST neurons. • Optic flow’s simultaneous stimulation of local motion mechanisms alters the relative influence of different sites. • The presentation of dual local motion stimuli reveals direction selective interactions between sites within MST receptive fields that can enhance optic flow heading direction selectivity. This work was supported by grants from NEI (R01EY10287, P30EY01319).