The capacity to anticipate the timing of environmental cues allows us to allocate sensory resources at the right time and prepare actions. Such anticipation requires knowledge of elapsed time and of the probability that an event will occur. Here we show that neurons in the parietal cortex represent the probability, as a function of time, that a salient event is likely to occur. Rhesus monkeys were trained to make eye movements to peripheral targets after a light dimmed. Within a block of trials, the 'go' times were drawn from either a bimodal or unimodal distribution of random numbers. Neurons in the lateral intraparietal area showed anticipatory activity that revealed an internal representation of both elapsed time and the probability that the 'go' signal was about to occur (termed the hazard rate). The results indicate that the parietal cortex contains circuitry for representing the time structure of environmental cues over a range of seconds.

Abstract-Computational modeling of the primate visual system yields insights of potential relevance to some of the challenges that computer vision is facing, such as object recognition and categorization, motion detection and activity recognition or vision-based navigation and manipulation. This article reviews some functional principles and structures that are generally thought to underlie the primate visual cortex, and attempts to extract biological principles that could further advance computer vision research. Organized for a computer vision audience, we present functional principles of the processing hierarchies present in the primate visual system considering recent discoveries in neurophysiology. The hierarchal processing in the primate visual system is characterized by a sequence of different levels of processing (in the order of ten) that constitute a deep hierarchy in contrast to the flat vision architectures predominantly used in today's mainstream computer vision. We hope that the functional description of the deep hierarchies realized in the primate visual system provides valuable insights for the design of computer vision algorithms, fostering increasingly productive interaction between biological and computer vision research.

Differences in the horizontal positions of retinal images-binocular disparity-provide important cues for three-dimensional object recognition and manipulation. We investigated the neural coding of three-dimensional shape defined by disparity in anterior intraparietal (AIP) area. Robust selectivity for disparity-defined slanted and curved surfaces was observed in a high proportion of AIP neurons, emerging at relatively short latencies. The large majority of AIP neurons preserved their three-dimensional shape preference over different positions in depth, a hallmark of higher-order disparity selectivity. Yet both stimulus type (concave-convex) and position in depth could be reliably decoded from the AIP responses. The neural coding of three-dimensional shape was based on first-order (slanted surfaces) and second-order (curved surfaces) disparity selectivity. Many AIP neurons tolerated the presence of disparity discontinuities in the stimulus, but the population of AIP neurons provided reliable information on the degree of curvedness of the stimulus. Finally, AIP neurons preserved their three-dimensional shape preference over different positions in the frontoparallel plane. Thus, AIP neurons extract or have access to three-dimensional object information defined by binocular disparity, consistent with previous functional magnetic resonance imaging data. Unlike the known representation of three-dimensional shape in inferior temporal cortex, the neural representation in AIP appears to emphasize object parameters required for the planning of grasping movements.