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

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Model Spatial Tuning

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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

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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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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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