1. Natural Stimulus Statistics Alter the Receptive Field Structure of V1 Neurons
Stephen V David, William E Vinje, Jack L Gallant
J Neurosci, Aug 2004
yan karklin – cns meeting – 9/7/05

2. executive summary goal: model V1 neurons’ response to natural stimuli
approach: phase-sep spatio-temporal Fourier domain filters
natural vision movies, flashing gratings, hybrid data
evaluation: receptive field analysis
PSTH prediction
results: 0-90% accuracy of prediction (correlation coef) (mean = 42%)
for 55% of neurons, nat stim yield better predicting models for 7% of neurons, nat stim yield worse predicting models
nat. stim. = better predictions for nat. stim gratings = better predictions for gratings
temporal and spatial inhibitory components account for most of prediction improvement

3. setup stimuli: natural vision movies (nat)
- natural images (landscapes, man-made obj, people)
- model saccades
- uniform distribution of saccade direction
- empirical distr of velocities/lengths
- circular patches sampled along saccade path
gratings (syn)
- flashing randomly oriented, random sp freq, phase
natural image sequences (hyb)
- natural spatial stats
- no temporal correlation
neurons: parafoveal V1, 2 awake monkeys, extracelullar recording
estimate CRF, present stimuli 2-4x CRF diameter
hold-out set for testing of prediction accuracy

4. neural response

5. model also tried time/space inseparable phase-sep Fourier transform
expansive, sigmoidal non-linearities
phase-sep Fourier transform
linear kernel estimation (reverse corr)
time/space separable
threshold rectification

6. model estimation 2. linear filter estimation by cross-correlation
1. phase-sep Fourier domain representation
3. correction for stimulus correlations
4. time-space sepatation (see next slide)
5. final rate output

7. model estimation (contd)
estimate space-time insep STF
regularize (jack-knife, shrinkage estim)
convert to space-time sep STF (SVD = approx sol)
iteratively estimate spatial, temporal functions
stimuli downsampled to 18x18
responses smoothed and binned to 14ms
STRF – phase-sep Fourier domain (spatial), ~200msec (temporal)
10% hold-out set for testing (i.e. comparing one fitted model to another)
only after all params fixed

8. one neuron’s STRF estimated with natural vision movies
estimated with gratings

9. another neuron’s STRF estimated with natural vision movies
and with residual spatial bias corrected
estimated with gratings

10. which parts of the spatial STRFs are different when estimated with natural images vs gratings?
computed correlation between nat STRF and normalized syn STF
the whole spatial STRF
positive component only
negative component only

11. predicting response to natural vision movies (neuron 1)

12. predicting response to natural vision movies (neuron 2)

13. what makes prediction better?
prediction evaluated on natural vision movies
using models estimated on nat, syn, and hyb stimuli
signif better
signif worse
24 neurons (55%) better. 3 neurons (7%) worse +
mean r=0.42
mean r=0.34 +
mean r=0.19
mean r=0.30
mean r=0.35

14. temporal responses averaged across all neurons
natural vision
gratings
relative amount of inhibition
natural vision
gratings
temp response, normalized and averaged
integral of temp response

15. what affects neural response properties?
it’s possible that natural spatial statistics are all that’s required for neuron to change properties
test with new class of stimuli: natural image sequences
natural spatial stats
no temporal correlation

16. executive summary goal: model V1 neurons’ response to natural stimuli
approach: phase-sep spatio-temporal Fourier domain filters
natural vision movies, flashing gratings, hybrid data
evaluation: receptive field analysis
PSTH prediction
results: 0-90% accuracy of prediction (correlation coef) (mean = 42%)
for 55% of neurons, nat stim yield better predicting models for 7% of neurons, nat stim yield worse predicting models
nat. stim. = better predictions for nat. stim gratings = better predictions for gratings
temporal and spatial inhibitory components account for most of prediction improvement

19. how good are the predictions?
prediction evaluated on natural vision movies
using models estimated on nat, syn, and hyb stimuli
trained on
nat
syn
0.42
0.19
0.11
0.31
tested on