INTRODUCTION
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Y-cell responses to texture stimuli predicted by a nonlinear receptive field model based on bipolar cell subunits
1Integrated Program in Neuroscience, McGill Univ.; 2Dept of Ophthalmology, McGill Vision Res. Unit, McGill University, Montreal, Canada Amol Gharat1and Curtis Baker2
METHODS
RESULTS
CONCLUSIONS 1. Rosenberg et al (2010). Subcortical representation of non-fourier image features. J Neuroscience. 30(6):1985-1993. 2. Demb et al (2001a). Cellular basis for the response to second-order motion cues in Y retinal ganglion cells. Neuron. 32:711-721. 3. Demb et al (2001b). Bipolar cells contribute to nonlinear spatial summation in the brisk-transient (Y) ganglion cell in mammalian retina. J
Neuroscience. 21(19):7447-7454. 4. Borghuis et al (2013) Two-photon imaging of nonlinear glutamate release dynamics at bipolar cell synapses in the mouse retina. J
Neuroscience. 33(27):10972-10985. 5. Hochstein & Shapley (1976). Linear and nonlinear spatial subunits in Y cat retinal ganglion cells. 262:265-284. 6. http://strflab.berkeley.edu/ Funded by CIHR grant MOP-119498 to CB. Vanier Canada Graduate Scholarship to AG. Many thanks to Guangxing Li and Vargha Talebi.
Y cell “subunit” receptive field X cell receptive field
Variety of Y cell receptive fields
time
Y-cell model
Model Spatial Tuning
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Linear HWR FWR0
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- A prime aim of visual neuroscience is to estimate neuronal receptive field models that can predict responses to arbitrary stimuli.
- X (linear) and Y (non-linear) type cells form two major categories in mammalian retina and LGN.
- Y type cells may be an important early stage for processing second-order information in visual scenes (Demb et al., 2001a; Rosenberg et al., 2010).
- A linear receptive field model with static output nonlinearity (LN model) cannot account for nonlinear responses of Y-cells to high resolution texture stimuli.
- A model with spatial pooling of rectified inputs from several small subunit receptive fields (Demb et al., 2001b) might better predict responses of Y-cells to arbitrary texture stimuli.
Stimuli - Synthetic Naturalistic Texture
images at 75Hz - 3 datasets training (7500 images) regularization (1875 images) validation (1875 images) - Sinusoidal gratings (Drifting or
Contrast reversing) Animal Preparation - Anesthetized and paralyzed cats - Extracellular, single-unit recordings in LGN using single &
multi-channel electrodes - Manual spike sorting using Plexon Offline Sorter &
SpikeSorter (UBC) Classification of Cells (X or Y) - Measured responses of neurons to contrast reversing
gratings at series of spatial frequencies - If response at second harmonic (F2) was greater than first
harmonic (F1), neuron was classified as Y-type Receptive Field Estimation - Preprocessing with Gaussian subunits - Estimation of subunit weights with gradient descent
optimization.(STRFlab Software) - Regularization (early stopping) - Measure model performance for series of subunit sizes
and rectification types
- Estimated nonlinear subunit model of Y-cells explains a significant fraction of response variance to texture movies.
- The subunit model also predicts the “Y-cell signature” spatial frequency tuning to grating stimuli.
- Estimated Y cell models have varying subunit rectification while X cell models have no rectification.
Spatial Frequency Tuning
Linear HWR FWR0
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Subunit Rectification Type
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Spatial Frequency Tuning
Demb et al., 2001b
Hochstein & Shapley, 1976
“Predicted” responses are measured by running simulations on estimated subunit model of the neuron.
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Spatial Frequency (cpd)
Firin
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ActualPredicted
F1 F2
0.0 ms 13.3 ms 26.7 ms
40.0 ms 53.3 ms
! = 0.125°
1°
0.0 ms 13.3 ms 26.7 ms
40.0 ms 53.3 ms 66.7 ms
80.0 ms 93.3 ms
! = 0.25°
1°
! = 0.25°
VAF = 64%
! = 0.125° ! = 0.25°
VAF = 62% VAF = 49.6% VAF = 37%
! = 0.375°
0.5°3° 2.5° 2.5°
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