The Role of V1 Surround Suppression in MT Motion Integration

Abstract

Tsui JMG, Hunter JN, Born RT, Pack CC. The role of V1 surround suppression in MT motion integration. J Neurophysiol 103: 3123-3138, 2010. First published March 24, 2010 doi:10.1152/jn.00654.2009. Neurons in the primate extrastriate cortex are highly selective for complex stimulus features such as faces, objects, and motion patterns. One explanation for this selectivity is that neurons in these areas carry out sophisticated computations on the outputs of lower-level areas such as primary visual cortex (V1), where neuronal selectivity is often modeled in terms of linear spatiotemporal filters. However, it has long been known that such simple V1 models are incomplete because they fail to capture important nonlinearities that can substantially alter neuronal selectivity for specific stimulus features. Thus a key step in understanding the function of higher cortical areas is the development of realistic models of their V1 inputs. We have addressed this issue by constructing a computational model of the V1 neurons that provide the strongest input to extrastriate cortical middle temporal (MT) area. We find that a modest elaboration to the standard model of V1 direction selectivity generates model neurons with strong end-stopping, a property that is also found in the V1 layers that provide input to MT. With this computational feature in place, the seemingly complex properties of MT neurons can be simulated by assuming that they perform a simple nonlinear summation of their inputs. The resulting model, which has a very small number of free parameters, can simulate many of the diverse properties of MT neurons. In particular, we simulate the invariance of MT tuning curves to the orientation and length of tilted bar stimuli, as well as the accompanying temporal dynamics. We also show how this property relates to the continuum from component to pattern selectivity observed when MT neurons are tested with plaids. Finally, we confirm several key predictions of the model by recording from MT neurons in the alert macaque monkey. Overall our results demonstrate that many of the seemingly complex computations carried out by high-level cortical neurons can in principle be understood by examining the properties of their inputs.

Integration and segregation of multiple motion signals by neurons in area MT of primate

“…Simoncelli and Heeger's model (1998) is not the only one that can emulate pattern cells' responses to plaids. For instance, end-stopping V1 neurons can be exploited to determine the average direction of plaid (Pack et al 2003;Tsui et al 2010). Alternatively, the cascade model, in which V1 neurons are subject to gain control and their responses summed by MT neurons, with an accelerating nonlinearity output, can also predict pattern cells' responses to plaids (Rust et al 2006).…”

Hierarchical processing of complex motion along the primate dorsal visual pathway

“…Regardless of its precise functional interpretation, the compressive nonlinear operation could plausibly be implemented through inhibitory interactions among MT neurons with similar receptive field positions and stimulus selectivities; a similar "selfnormalization" operation at the level of V1 has been posited to be of primary importance in explaining selectivity in MT cells (18,34,57). An alternate explanation is synaptic depression at the level of the MT-MST synapse (58).…”

“…Stated in more general terms, the stimulus selectivity of the hierarchical MST model is too similar to that of its inputs, and there appears to be no spatial arrangement of inputs that can bring this model into closer agreement with the data. This result suggests that MST selectivity requires a nonlinear operation that transforms the output of one area before summation by the next (2,18,34); indeed such a mechanism has been proposed in other contexts throughout the primate visual system (3,5,30,35). We therefore examined the consequences of adding a nonlinearity (Fig.…”

Sensory versus motor loci for integration of multiple motion signals in smooth pursuit eye movements and human motion perception

“…It is possible that the nonlinear interaction of overlapping stimuli occurs in the inputs to MT from the primary visual cortex (V1), where contrast normalization is an important feature (Geisler and Albrecht 1992;Rust et al 2005;Tsui et al 2010). Sheliga et al (2006Sheliga et al ( , 2008 have proposed the primary visual cortex as the locus of the local interaction of multiple stimuli for ocular following.…”