1. 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 INTRODUCTION Forward translational movement through the environment creates a radial pattern of optic flow that surrounds the moving observer and provides cues about heading direction. Local Motion Composition Of The Global Pattern In Optic Flow 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. 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. Dual Gaussian Model Derived from Local Motion Responses 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. This work was supported by grants from NEI (R01EY10287, P30EY01319). 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. CONCLUSIONS Dual Site Local Motion Stimulation Shows Regional Interactions 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. Dual Gaussian Model of Singles Stimuli 20 10 5 15 Neuron 819R64 Recorded Data Model Fit 0 Firing Rate (sp/s) Stimulus Condition Fit to Single Segment Local Motion Responses METHODS: MST Neuronal Responses to Local Motion Nine Sites of Local Motion Stimuli Four Directions of Local Motion Stimuli 819R64 Local Motion Responses We then presented planar motion in nine 30 o X 30 o patches covering the 90 o X 90 o 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. Dual Gaussian Model Prediction of Optic Flow Response Profiles 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. Optic Flow Stimulus Singles Model Fit to Optic Flow Responses Firing Rate (sp/s +/- sem) Neuron 819R64 Recorded Data Model Training Neuron (from best to worst fit) 1020304050 Total Error of Model Fit Across Optic Flow Responses (normalized firing rate) 30 20 10 0 40 50 Total Error for 3 Model Runs per Neuron METHODS: MST Neuronal Responses to Optic Flow 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 90 o X 90 o 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. Optic Flow Stimulus 30 20 10 5 25 35 15 0 40 Neuron 819R64 Single Neuron Responses to Optic Flow Single Neuron Optic Flow Response Profile Firing Rate (sp/s +/-sem) Dual Site Stimuli Can Improve Model Fits to Optic Flow Responses 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. 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. Firing Rate (sp/s) 60 40 20 0 Stimulus Condition Recorded Data Model Training 40 20 0 60 Stimulus Condition Dual Site Model (CCW)Gain Modulated Model 40 20 0 60 Stimulus Condition Neuron 819R34 Singles Model 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. Gain Modulated Dual Gaussian Models Improve Fit to Optic Flow 120 80 60 100 0 40 20 Neuron 712R02 Recorded Data Singles Model Gain Modulated Firing Rate (sp/s) Stimulus Condition Fits to Optic Flow Responses Singles Model to Local Motion Singles Model Error Gain Modulated Error Model Fit Comparisons Model Transform Gain Modulated Model