Time Course and Time-Distance Relationships for Surround Suppression in Macaque V1 Neurons

Abstract

Iso-orientation surround suppression is a powerful form of visual contextual modulation in which a stimulus of the preferred orientation of a neuron placed outside the classical receptive field (CRF) of the neuron suppresses the response to stimuli within the CRF. This suppression is most often attributed to orientation-tuned signals that propagate laterally across the cortex, activating local inhibition. By studying the temporal properties of surround suppression, we have uncovered characteristics that challenge standard notions of surround suppression. We found that the latency of suppression depended on its strength. Across cells, strong suppression arrived on average 30 msec earlier than weak suppression, and suppression sometimes arrived faster than the excitatory CRF response. We compared the relative latency of CRF response onset and offset with the relative latency of suppression onset and offset. Response onset was delayed relative to response offset in the CRF but not in the surround. This is not the expected result if neurons targeted by suppression are like those that generate it. We examined the time course of suppression as a function of distance of the surround stimulus from the CRF and found that suppression was predominantly sustained for nearby stimuli and predominantly transient for distant stimuli. By comparing the latency of suppression for nearby and distant stimuli, we found that orientation-tuned suppression could effectively propagate across 6 -8 mm of cortex at ϳ1 m/sec. This is considerably faster than expected for horizontal cortical connections previously implicated in surround suppression. We offer refinements to circuits for surround suppression that account for these results and describe how feedback from cells with large CRFs can account for the rapid propagation of suppression within V1.

Dynamics of Suppression in Macaque Primary Visual Cortex

“…We recorded extracellularly from single units in area V1 of 19 Cynomolgus macaques (Macaca fascicularis), one Bonnet macaque (M. radiata), and two pig-tailed macaques (M. nemestrina), ranging in weight from 3.0 to 6.0 kg. The number of animals was large because they were also used for experiments other than those reported here (notably those of Bair et al, 2003). Of 112 complex cells studied, 43 were used for both cross-orientation suppression and surround suppression measurements.…”

“…Finally, feedback from higher cortical areas has been a popular alternative explanation, based on the latency and spatial distribution of signals from the surround (Knierim and Van Essen, 1992;Zipser et al, 1996;Cavanaugh et al, 2002;Angelucci et al, 2002;Levitt and Lund, 2002). Recently, the temporal properties of surround suppression have been used to analyze its mechanism: the relatively sluggish dynamics and their modest dependence on cortical distance seem to be most consistent with the feedback hypotheses (Bair et al, 2003).…”

Contextual Masking of Oriented Lines: Interactions Between Surface Segmentation Cues

“…Differences in the temporal dynamics of responses elicited by CRF stimuli under different contextual conditions have been used previously to draw inferences about the sources of modulatory signals and the underlying mechanisms (e.g., Bair et al 2003;Knierim and van Essen 1992;Lamme 1995;Zipser et al 1996). To enable such inferences here, we performed a temporal analysis of spike rates for different stimulus conditions averaged across cells.…”

“…In principle, both are contextual signals that will elevate CRF responsivity in the presence of center-surround contrast, but the differential sources of these hypothesized signals allows for differential timing and magnitudes of their respective effects on CRF responses. Alternatively, the surround modulatory signals may reflect feedback projections from higher cortical areas (Bair et al 2003), such as V2, which convey orientation and polarity-specific information.…”

Feature detection using spikes: The greedy approach

“…This is therefore a simple and biologically plausible implementation of a MAP estimate using the parallel architecture of the network which is in contrast with the complexity of this implementation on a single-processor computer. To implement the greedy algorithm, we then need to implement a lateral interaction on the neighboring neuron similar to the observed lateral propagation of information in V1 [10,2]. In our scheme the interaction Figure 2: Model of a neuronal layer as a communication channel.…”