nips nips2006 nips2006-145 nips2006-145-reference knowledge-graph by maker-knowledge-mining
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Author: Jason M. Samonds, Brian R. Potetz, Tai S. Lee
Abstract: Although there has been substantial progress in understanding the neurophysiological mechanisms of stereopsis, how neurons interact in a network during stereo computation remains unclear. Computational models on stereopsis suggest local competition and long-range cooperation are important for resolving ambiguity during stereo matching. To test these predictions, we simultaneously recorded from multiple neurons in V1 of awake, behaving macaques while presenting surfaces of different depths rendered in dynamic random dot stereograms. We found that the interaction between pairs of neurons was a function of similarity in receptive fields, as well as of the input stimulus. Neurons coding the same depth experienced common inhibition early in their responses for stimuli presented at their nonpreferred disparities. They experienced mutual facilitation later in their responses for stimulation at their preferred disparity. These findings are consistent with a local competition mechanism that first removes gross mismatches, and a global cooperative mechanism that further refines depth estimates. 1 In trod u ction The human visual system is able to extract three-dimensional (3D) structures in random noise stereograms even when such images evoke no perceptible patterns when viewed monocularly [1]. Bela Julesz proposed that this is accomplished by a stereopsis mechanism that detects correlated shifts in 2D noise patterns between the two eyes. He also suggested that this mechanism likely involves cooperative neural processing early in the visual system. Marr and Poggio formalized the computational constraints for solving stereo matching (Fig. 1a) and devised an algorithm that can discover the underlying 3D structures in a variety of random dot stereogram patterns [2]. Their algorithm was based on two rules: (1) each element or feature is unique (i.e., can be assigned only one disparity) and (2) surfaces of objects are cohesive (i.e., depth changes gradually across space). To describe their algorithm in neurophysiological terms, we can consider neurons in primary visual cortex as simple element or feature detectors. The first rule is implemented by introducing competitive interactions (mutual inhibition) among neurons of different disparity tuning at each location (Fig. 1b, blue solid horizontal or vertical lines), allowing only one disparity to be detected at each location. The second rule is implemented by introducing cooperative interactions (mutual facilitation) among neurons tuned to the same depth (image disparity) across different spatial locations (Fig. 1b, along the red dashed diagonal lines). In other words, a disparity estimate at one location is more likely to be correct if neighboring locations have similar disparity estimates. A dynamic system under such constraints can relax to a stable global disparity map. Here, we present neurophysiological evidence of interactions between disparity-tuned neurons in the primary visual cortex that is consistent with this general approach. We sampled from a variety of spatially distributed disparity tuned neurons (see electrodes Fig. 1b) while displaying DRDS stimuli defined at various disparities (see stimulus Fig.1b). We then measured the dynamics of interactions by assessing the temporal evolution of correlation in neural responses. a Left Image b Right Image Electrodes Disparity Left Image ? Stimulus Right Image Figure 1: (a) Left and right images of random dot stereogram (right image has been shifted to the right). (b) 1D graphical depiction of competition (blue solid lines) and cooperation (red dashed lines) among disparity-tuned neurons with respect to space as defined by Marr and Poggio’s stereo algorithm [2]. 2 2.1 Methods Recording and stimulation a Posterior - Anterior Recordings were made in V1 of two awake, behaving macaques. We simultaneously recorded from 4-8 electrodes providing data from up to 10 neurons in a single recording session (some electrodes recorded from as many as 3 neurons). We collected data from 112 neurons that provided 224 pairs for cross-correlation analysis. For stimuli, we used 12 Hz dynamic random dot stereograms (DRDS; 25% density black and white pixels on a mean luminance background) presented in a 3.5-degree aperture. Liquid crystal shutter goggles were used to present random dot patterns to each eye separately. Eleven horizontal disparities between the two eyes, ranging from ±0.9 degrees, were tested. Seventy-four neurons (66%) had significant disparity tuning and 99 pairs (44%) were comprised of neurons that both had significant disparity tuning (1-way ANOVA, p<0.05). b 5mm Medial - Lateral 100µV 0.2ms 1° Figure 2: (a) Example recording session from five electrodes in V1. (b) Receptive field (white box—arrow represents direction preference) and random dot stereogram locations for same recording session (small red square is the fixation spot). 2.2 Data analysis Interaction between neurons was described as
[1] Julesz, B. (1971) Foundations of cyclopean perception. Chicago: University of Chicago Press.
[2] Marr, D. & Poggio, T. (1976) Cooperative computation of stereo disparity. 194(4262):283-287. Science
[3] Aertsen, A.M., Gerstein, G.L., Habib, M.K. & Palm, G. (1989) Dynamics of neuronal firing correlation: modulation of
[4] Efron, B. & Tibshirani, R. (1993) An Introduction to the Bootstrap. New York: Chapman & Hall.
[5] Brody, C.D. (1999) Correlations without synchrony. Neural Computation 11(7):1537-1551.
[6] Gerstein, G.L. & Kirkland, K.L. (2001) Neural assemblies: technical issues, analysis, and modeling. Neural Networks 14(6-7):589-598.
[7] Kohn, A. & Smith, M.A. (2005) Stimulus dependence of neuronal correlation in primary visual cortex of the macaque. Journal of Neuroscience 25(14):3661-3673.
[8] Menz, M. & Freeman, R.D. (2004) Functional connectivity of disparity-tuned neurons in the visual cortex. Journal of Neurophysiology 91(4):1794-1807.
[9] Gilbert, C.D. & Wiesel, T.N. (1989) Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex. Journal of Neuroscience 9(7):2432-2442.
[10] Moore, G.P., Segundo, J.P., Perkel, D.H. & Levitan, H. (1970) Statistical signs of synaptic interaction in neurons. Biophysics Journal 10(9):876-900.
[11] Menz, M. & Freeman, R.D. (2003) Stereoscopic depth processing in the visual cortex: a coarseto-fine mechanism. Nature Neuroscience 6(1):59-65.
[12] Bruno, R.M. & Sakmann, B. (2006) Cortex is driven by weak but synchronously active thalamocortical synapses. Science 312(5780):1622-1627.
[13] Prazdny, K. (1985) Detection of binocular disparities. Biological Cybernetics 52(2):93-99.
[14] Pollard, S.B., Mayhew, J.E., & Frisby, J.P. (1985) PMF: a stereo correspondence algorithm using a disparity gradient limit. Perception 14(4):449-470.
[15] Ringach, D.L., Hawken, M.J. & Shapley, R. (1997) Dynamics of orientation tuning in macaque primary visual cortex. Nature 387(6630):281-284.
[16] Samonds, J.M., Allison, J.D., Brown, H.A. & Bonds, A.B. (2004) Cooperative synchronized assemblies enhance orientation discrimination. Proceedings of the National Academy of Sciences USA 101(17):6722-6727.
[17] Ben-Shahar, O., Huggins, P.S., Izo, T. & Zucker, S.W. (2003) Cortical connections and early visual function: intra- and inter-columnar processing. Journal of Physiology (Paris) 97(2-3):191-208.