Abstract
Neurons in the dorsal pathway of the visual cortex are thought to be involved in motion processing. The first site of motion processing is the primary visual cortex (V1), encoding the direction of motion in local receptive fields, with higher order motion processing happening in the middle temporal area (MT). Complex motion properties like optic flow are processed in higher cortical areas of the Medial Superior Temporal area (MST). In this study, a hierarchical neural field network model of motion processing is presented. The model architecture has an input layer followed by either one or cascade of two neural fields (NF): the first of these, NF1, represents V1, while the second, NF2, represents MT. A special feature of the model is that lateral connections used in the neural fields are trained by asymmetric Hebbian learning, imparting to the neural field the ability to process sequential information in motion stimuli. The model was trained using various traditional moving patterns such as bars, squares, gratings, plaids, and random dot stimulus. In the case of bar stimuli, the model had only a single NF, the neurons of which developed a direction map of the moving bar stimuli. Training a network with two NFs on moving square and moving plaids stimuli, we show that, while the neurons in NF1 respond to the direction of the component (such as gratings and edges) motion, the neurons in NF2 (analogous to MT) responding to the direction of the pattern (plaids, square object) motion. In the third study, a network with 2 NFs was simulated using random dot stimuli (RDS) with translational motion, and show that the NF2 neurons can encode the direction of the concurrent dot motion (also called translational flow motion), independent of the dot configuration. This translational RDS flow motion is decoded by a simple perceptron network (a layer above NF2) with an accuracy of 100% on train set and 90% on the test set, thereby demonstrating that the proposed network can generalize to new dot configurations. Also, the response properties of the model on different input stimuli closely resembled many of the known features of the neurons found in electrophysiological studies.
Highlights
Visual motion is experienced by living organisms either due to self-motion with respect to the environment or by the motion of individual objects in the environment
The model reproduces the motion-selective properties of cells in Primary Visual Cortex (V1), middle temporal area (MT), and Medial Superior Temporal area (MST)
We used a hierarchical architecture consisting of neural fields to model the direction-selective cells in V1 and pattern selective cells in MT, and translational flow selective cells in MST complex
Summary
Visual motion is experienced by living organisms either due to self-motion with respect to the environment or by the motion of individual objects in the environment. The first cortical stage of primate motion processing starts at V1 where a subset of cells is highly direction selective (Hubel and Wiesel, 1968; Movshon and Newsome, 1996) These cells have relatively small spatiotemporal receptive fields (Hubel and Wiesel, 1974) and encode the direction of motion of local features. These motion cues are often different from the motion of the visual pattern; locally encoded motion cues are ambiguous (Wallach, 1976) and result in the so-called aperture problem (Fennema and Thompson, 1979; Wuerger et al, 1996; Pack et al, 2001, 2003). From optical imaging and single-cell recording studies we know that MST cells receive projections from MT, and respond selectively to the higher order optic flow motion, including translation, radial, rotation and combinations of the latter two (Tanaka and Saito, 1989; Duffy and Wurtz, 1991; Orban et al, 1995; Morrone et al, 2000)
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