A theory of direction selectivity for macaque primary visual cortex

This paper offers a theory for the origin of direction selectivity (DS) in the macaque primary visual cortex, V1. DS is essential for the perception of motion and control of pursuit eye movements. In the macaque visual pathway, neurons with DS first appear in V1, in the Simple cell population of the Magnocellular input layer 4Cα. The lateral geniculate nucleus (LGN) cells that project to these cortical neurons, however, are not direction selective. We hypothesize that DS is initiated in feed-forward LGN input, in the summed responses of LGN cells afferent to a cortical cell, and it is achieved through the interplay of 1) different visual response dynamics of ON and OFF LGN cells and 2) the wiring of ON and OFF LGN neurons to cortex. We identify specific temporal differences in the ON/OFF pathways that, together with item 2, produce distinct response time courses in separated subregions; analysis and simulations confirm the efficacy of the mechanisms proposed. To constrain the theory, we present data on Simple cells in layer 4Cα in response to drifting gratings. About half of the cells were found to have high DS, and the DS was broadband in spatial and temporal frequency (SF and TF). The proposed theory includes a complete analysis of how stimulus features such as SF and TF interact with ON/OFF dynamics and LGN-to-cortex wiring to determine the preferred direction and magnitude of DS.

This paper proposes a solution to a longstanding question in visual neuroscience, namely, the origin of direction selectivity (DS) in the visual cortex of macaque monkeys. Motion perception is a vital visual capability well developed in primates. As perceiving motion requires perceiving the direction in which a target moves, DS, the ability of visual neurons to sense the direction of movement, is essential for motion perception (1) and for the control of pursuit eye movements (2). For these reasons, understanding DS is an important first step toward understanding how the cortex processes motion signals.DS in cortical neurons was first documented in the cat (3). Since then, it has been found in neurons all along the visual dorsal stream (an area associated with motion processing) in primates like macaque monkeys (4–7), whose vision is like that of humans. Neurons with DS are, in fact, present across species; they are widespread among visual mammals, an experimental fact that testifies to their biological significance.In the visual pathway of macaques, DS appears first in the primary visual cortex (V1), in the Simple cell population of the input layer 4Cα (8). These neurons provide feed-forward direction-selective signals to subsequent cortical layers and brain regions in the dorsal pathway. Thus, to discover the origin of DS, one is led to examining how neurons in layer 4Cα acquire their DS—and that is where it gets interesting: The neurons that provide visual signals to layer 4Cα, the Magnocellular cells in the lateral geniculate nucleus (LGN), are not direction selective (9–12). Yet many of the cells in the input layer of V1 to which they project are direction selective. A fundamental scientific question, therefore, is how 4Cα neurons acquire their DS. That is the question we would like to answer in this paper.Although many papers have been written on DS since its discovery over half a century ago, and there is continued interest in the subject (13–16), no satisfactory mechanistic explanation for the origin of DS in primate cortex has been proposed before now: Early conceptual models of how DS may arise, such as the Reichardt multiplier (17) or the motion energy model (18), were not concerned with biological mechanisms. Later work proposed neural mechanisms for the motion energy model (19), but they are not sufficient for explaining DS in primate cortex. See Discussion for comparisons of different model mechanisms.It is widely accepted that the DS computation requires spatiotemporal inseparability (STI); that is, different subregions of the receptive field have different time courses of response (18, 20, 21). What were lacking were biological mechanisms that could produce STI, and a clear understanding of how DS depends on the interaction between STI and the spatial and temporal character of the visual stimulus. These are the issues we address in this paper.We hypothesize that a plausible biological mechanism is the interplay between 1) the different dynamics of ON and OFF LGN cells and 2) the specific wiring that connects ON and OFF cells to V1. Item 2 refers here to the well-known fact that OFF and ON LGN cells are wired to segregated V1 receptive field subregions (3, 22, 23). Our main contribution is item 1: We identify, in Results, dynamic differences in the ON/OFF pathways that, together with item 2, produce distinct response time courses in separated receptive-field subregions. The mechanisms we propose are biologically grounded, and, as we show, they are sufficient for initiating DS in the feed-forward LGN input to cortical cells.To constrain our theory, we present experimental results on the responses of macaque 4Cα Simple cells to drifting gratings. Most Simple cells we recorded in 4Cα were unambiguously direction selective, preferring, consistently, the same direction over their entire visible ranges of spatial frequency (SF) and temporal frequency (TF); about half of the cells had high DS. Our data reveal also an important characteristic of neurons with DS, namely, the approximate invariance of DS with SF and TF. Explaining the broadband character of DS (in TF and SF) is a challenge for all previous theories. Our theory includes a complete analysis of how stimulus features like SF and TF interact with ON/OFF dynamics and LGN-to-cortex wiring to explain the broadband character of DS. The theoretical predictions are in good agreement with data.With regard to broader implications, although the theory as described in this paper is specifically about DS, an important message is that, when combining information from multiple channels, slight biases in their temporal filters can greatly enhance the capability of a system. Thus, it may be possible to exploit the temporal axis further in the processing of biological and nonbiological signals, especially in the neural processing of sensory inputs and, possibly, in computer vision.