MODELING THE RECPTIVE FIELD ORGANIZATION OF OPTIC FLOW SELECTIVE MST NEURONS
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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. 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 0011100 101110101 1100101 001101100 25 models with least total error across stimuli (firing rate) 25 models with fewest response group error (3 clusters) Cross-over models at random sites to yield 2550 new models 00111001….. 00111001….. 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 Gaussian 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 Neuron Model Firing Rate (spks/s) 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 Spo t 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 50 spks/s 50 spks/s 50 spks/s 500 ms 500 ms 500 ms 500 ms Test Spot Test Spot Test Spot 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 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 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 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 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) Normalized Firing Rate (spks/s) Single Stimulus Model of Optic Flow Responses Dual Stimulus Model of Optic Flow Responses Optic Flow Stimuli Neuron 819R34 Class Error: 15 Class Error: 5 Neuron Model Neuron Model 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 90 o X 90 o rear projection screen. We then recorded the responses of these neurons to 30 o X 30 o patches of local planar motion by presenting dot pattern motion in four cardinal directions on an otherwise blank screen. SIN G LE SITE STIM U LI 4 directions SIN G LE SITE STIM U LI 4 directions Local Motion Stimuli (4 directions of local motion at 9 sites 30 o2 ) Optic Flow Responses Simulate Observer Movement in 16 Directions Optic Flow Stimulus 80 60 40 20 0 Discharge Rate (spk /sec) Neuron 819R09 Optic Flow Stimulus 80 60 40 20 0 Discharge Rate (spk /sec) Neuron 819R09
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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). - PowerPoint PPT Presentation
Transcript of MODELING THE RECPTIVE FIELD ORGANIZATION OF OPTIC FLOW SELECTIVE MST NEURONS
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
INTRODUCTIONThe 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 OfThe 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.
METHODS: Dual Gaussian Response Field ModelingTraining 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 Polarity0011100 101110101 1100101 001101100
25 models with least total erroracross stimuli
(firing rate)
25 models with fewest response
group error(3 clusters)
Cross-over models at random sites to yield 2550 new models00111001….. 00111001…..X
Repeat across 75 generations (asymptotic error reduction)
9 Site, Dual Gaussian ModelOf MST Receptive Fields
Single site, local motion stimuli yield directional response profiles, typically modeled by combined excitatory and inhibitory mechanisms.
ExcitatoryGaussian
InhibitoryGaussian
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 DataDual Gaussian Model
(derived from single site, local motion data) Excitatory
Inhibitory
GaussianParameters
Length=GainHead=Width
Local Motion Stimuli Optic Flow Stimuli
Model Training Fit Model Testing FitNeuron Model Neuron Model
Fir
ing
Rat
e (s
pks
/s)
Dual Local Motion Stimuli Reveal Direction Selective InteractionsWe 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.
HotSpot
Dual Simultaneous StimulationTwo Hot Spot Directions X 4 Test Spot Directions
Single Site StimulationOne Site X 4 Directions
Neuron819R10
50
sp
ks
/s
50
sp
ks
/s
50
sp
ks
/s
50
sp
ks
/s
500 ms 500 ms 500 ms 500 ms
TestSpot
TestSpot
TestSpot
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 ResponseDual 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
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
Optic Flow Responses Predicted By Model With Its Hot Spot DirectionWe 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
No
rmal
ized
Fir
ing
Rat
e (s
pks
/s)
No
rmal
ized
Fir
ing
Rat
e (s
pks
/s)
Single Stimulus Model of Optic Flow Responses Dual Stimulus Model of Optic Flow Responses
Optic Flow Stimuli Neuron 819R34
Class Error: 15 Class Error: 5
Neuron
Model
Neuron
Model
METHODS: MST Neuronal Responses to Optic Flow and Local MotionWe 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.
SINGLE SITE STIMULI4 directions
SINGLE SITE STIMULI4 directions
Local Motion Stimuli(4 directions of local motion at 9 sites 30o2)
Optic Flow ResponsesSimulate Observer Movement in 16
Directions
Optic Flow Stimulus
80
60
40
20
0
Dis
ch
arg
eR
ate
(s
pk/
se
c)
Neuron 819R09Optic Flow Stimulus
80
60
40
20
0
Dis
ch
arg
eR
ate
(s
pk/
se
c)
Neuron 819R09
