The Role of V1 Surround Suppression in MT Motion Integration
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J Neurophysiol 103: 3123–3138, 2010. First published March 24, 2010; doi:10.1152/jn.00654.2009. The Role of V1 Surround Suppression in MT Motion Integration James M. G. Tsui,1 J. Nicholas Hunter,2 Richard T. Born,2 and Christopher C. Pack1 1Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; and 2Department of Neurobiology, Harvard Medical School, Boston, Massachusetts Submitted 24 July 2009; accepted in final form 18 March 2010 Tsui JMG, Hunter JN, Born RT, Pack CC. The role of V1 surround The earliest stage of the primate dorsal visual stream is the suppression in MT motion integration. J Neurophysiol 103: 3123–3138, V1, where receptive fields are generally Ͻ1° in diameter. 2010. First published March 24, 2010; doi:10.1152/jn.00654.2009. Neurons in the middle temporal (MT) area have receptive Neurons in the primate extrastriate cortex are highly selective for complex stimulus features such as faces, objects, and motion patterns. fields tenfold this size and receptive fields in the medial One explanation for this selectivity is that neurons in these areas carry superior temporal (MST) area are larger still. Because most of out sophisticated computations on the outputs of lower-level areas the visual input in these higher areas comes directly or indi- such as primary visual cortex (V1), where neuronal selectivity is often rectly from V1, receptive fields in MT and MST are presum- modeled in terms of linear spatiotemporal filters. However, it has long ably derived by spatially integrating the outputs of many been known that such simple V1 models are incomplete because they neurons with smaller receptive fields. fail to capture important nonlinearities that can substantially alter Spatial integration may serve many purposes, but in the Downloaded from neuronal selectivity for specific stimulus features. Thus a key step in understanding the function of higher cortical areas is the development domain of motion processing it is likely to be of crucial of realistic models of their V1 inputs. We have addressed this issue by importance for overcoming a class of computational challenges constructing a computational model of the V1 neurons that provide that can collectively be described as correspondence problems the strongest input to extrastriate cortical middle temporal (MT) area. (Ullman 1979). One example is the aperture problem (Fig. 1A), We find that a modest elaboration to the standard model of V1 in which the measurement of the velocity of a moving edge is direction selectivity generates model neurons with strong end-stop- rendered ambiguous by the fact that any point along the edge jn.physiology.org ping, a property that is also found in the V1 layers that provide input can be associated with any other point at a subsequent instant to MT. With this computational feature in place, the seemingly complex properties of MT neurons can be simulated by assuming that in time (Wallach 1939). Consequently, there exists a family of they perform a simple nonlinear summation of their inputs. The local velocity measurements that are consistent with the global resulting model, which has a very small number of free parameters, motion of the edge. can simulate many of the diverse properties of MT neurons. In Physiological studies of the aperture problem have made use particular, we simulate the invariance of MT tuning curves to the of various kinds of visual stimuli. One of the best-known on July 27, 2010 orientation and length of tilted bar stimuli, as well as the accompa- examples is the plaid stimulus (Adelson and Movshon 1982), nying temporal dynamics. We also show how this property relates to which is composed of two gratings that are combined to form the continuum from component to pattern selectivity observed when a single motion pattern (Fig. 1B). In general the perceived MT neurons are tested with plaids. Finally, we confirm several key motion of the plaid corresponds to the motion of neither predictions of the model by recording from MT neurons in the alert grating, even though most neurons in V1 respond to the motion macaque monkey. Overall our results demonstrate that many of the seemingly complex computations carried out by high-level cortical of these components. However, many neurons in MT respond neurons can in principle be understood by examining the properties of to the motion of the pattern (Movshon et al. 1986) and this has their inputs. been interpreted as evidence that these neurons solve the aperture problem. Other MT neurons have responses similar to those of V1 cells, in that they respond to the motion of the plaid INTRODUCTION components. Studies using additive, sinusoidal plaids have A striking feature of the primate visual system is the increase generally reported roughly equal numbers of component-selec- in the complexity of stimulus selectivity as one ascends the tive and pattern-selective neurons in MT (Movshon et al. hierarchy of extrastriate cortical regions. Whereas most neu- 1986). rons in the primary visual cortex (V1) respond well to oriented A second class of stimuli used in physiological studies of the edges at a particular point in space, neurons in the temporal and aperture problem contains moving features that provide locally parietal processing streams respond well to specific faces or unambiguous motion signals. One example is a tilted bar complex motion patterns. This is often thought to reflect an stimulus (Li et al. 2001; Lorenceau et al. 1993; Pack and Born increase in the complexity of the computations performed by 2001), the endpoints of which provide velocity signals that can the higher-level areas, but another possibility is that important in principle be extracted by very small receptive fields (Fig. A computations are performed in the neurons that provide input 1 ). For these stimuli one finds that the vast majority of to the extrastriate cortex. In this study we examine this latter macaque MT cells accurately signal motion direction, in the possibility in the context of a simple model of motion process- sense that their responses to motion depend very little on the ing in the dorsal visual pathway of the macaque monkey. orientation of the bars that comprise the stimulus (Pack and Born 2001). In these experiments the neurons were not classi- Address for reprint requests and other correspondence: C. C. Pack, McGill fied as pattern- or component-selective, so the relationship University, Montreal Neurological Institute, 3801 University St., Montreal, QC between the responses to the two types of stimuli is not entirely H3A 2B4, Canada (E-mail: [email protected]). clear. www.jn.org 0022-3077/10 Copyright © 2010 The American Physiological Society 3123 3124 J.M.G. TSUI, J. N. HUNTER, R. T. BORN, AND C. C. PACK FIG. 1. Stimuli used in the simulations. A: tilted bar ABGrating 1 Grating 2 stimulus. The orientation of the bar is rotated 45° counter- Veridical 2-D Feature clockwise with respect to its direction of motion. The motion direction of motion measured at the endpoints contains direction 2-dimensional features corresponding to the veridical mo- tion direction. The edge of the bar contains only one- dimensional features that correspond to the direction of 1-D Features Grating 1+2 motion perpendicular to stimulus orientation. The ambigu- ity of these signals results from the aperture problem. B: plaid stimulus. The plaid stimulus is composed of 2 gratings drifting in different directions of motion (top). When the gratings are superimposed (bottom) the perceived direction of the resulting pattern corresponds to the motion of neither 2-D Feature component. Component cells are typically modeled using a simple mo- component to pattern selectivity simply reflects the variation in tion energy detector (Adelson and Bergen 1985), which has direction tuning bandwidth observed in this area. These results been highly successful in accounting for a variety of physio- thus demonstrate that complex properties found in the extra- logical and psychophysical findings. A key feature of the striate cortex can in principle be attributed to computations that motion energy model is that its receptive field is a linear filter are already present in the inputs from V1. that effectively responds to only one plaid component at any point in time (thus the component selectivity). The responses METHODS Downloaded from of such a model to the tilted bar stimuli described earlier have not been examined, but as we show in the following text, Modeling methods motion energy models, in contrast to MT neurons, make large MT receptive fields are composed of multiple, nearly identical errors in signaling the motion of the tilted bar (see Fig. 3). subunits (Livingstone et al. 2001) (Supplemental Fig. S1) whose Numerous models have been designed to simulate pattern responses can be approximated as motion energy detectors (Pack et al. selectivity by combining the outputs of motion energy detec- 2006).1 We have therefore constructed a model of a single MT tors in various ways. A common feature of these models is the receptive field consisting of a population of identical motion energy jn.physiology.org existence of nonlinear interactions among V1 neurons (Simo- subunits tiled across space (Fig. 2B). We have extended this basic ncelli and Heeger 1998), the result of which is sometimes model by incorporating suppressive input from neighboring detectors arranged along the length of the excitatory receptive field, to simulate described as normalization (Heeger 1992). Indeed recent mod- in a straightforward way the effects of end-stopping (Supplemental eling work has suggested that such interactions are necessary Fig. S2A). This suppressive influence is modeled with standard for pattern selectivity in MT (Rust et al. 2006), although in that divisive normalization (Heeger 1992). The outputs of these end- work the spatial form of the normalization was not specified. stopped neurons are then fed into a model MT neuron, which sums on July 27, 2010 In this work we suggest that all of the above-mentioned them over space and time.