Abstract
To discriminate visual features such as corners and contours, the brain must be sensitive to spatial correlations between multiple points in an image. Consistent with this, macaque V2 neurons respond selectively to patterns with well-defined multipoint correlations. Here, we show that a standard feedforward model (a cascade of linear–non-linear filters) does not capture this multipoint selectivity. As an alternative, we developed an artificial neural network model with two hierarchical stages of processing and locally recurrent connectivity. This model faithfully reproduced neurons’ selectivity for multipoint correlations. By probing the model, we gained novel insights into early form processing. First, the diverse selectivity for multipoint correlations and complex response dynamics of the hidden units in the model were surprisingly similar to those observed in V1 and V2. This suggests that both transient and sustained response dynamics may be a vital part of form computations. Second, the model self-organized units with speed and direction selectivity that was correlated with selectivity for multipoint correlations. In other words, the model units that detected multipoint spatial correlations also detected space-time correlations. This leads to the novel hypothesis that higher-order spatial correlations could be computed by the rapid, sequential assessment and comparison of multiple low-order correlations within the receptive field. This computation links spatial and temporal processing and leads to the testable prediction that the analysis of complex form and motion are closely intertwined in early visual cortex.
Highlights
Form perception is often described as the detection of corners and junctions (Das and Gilbert, 1999), contours (von der Heydt and Peterhans, 1989; Lee and Nguyen, 2001; Ito and Komatsu, 2004), arcs and circles (Hegdé and Van Essen, 2000), and the segregation of figure and ground (Qiu and von der Heydt, 2005)
Taken together these results show that the recurrent form analysis model (RFAM) model captured the essence of multipoint spatial correlation textures (MSCT) tuning observed in individual V2 neurons
A network with two recurrently connected hidden layers captured the selectivity of V1 and V2 neurons for multipoint correlations and generalized to new examples from the texture classes
Summary
Form perception is often described as the detection of corners and junctions (Das and Gilbert, 1999), contours (von der Heydt and Peterhans, 1989; Lee and Nguyen, 2001; Ito and Komatsu, 2004), arcs and circles (Hegdé and Van Essen, 2000), and the segregation of figure and ground (Qiu and von der Heydt, 2005). Four-point correlations signal contours (von der Heydt and Peterhans, 1989; Lee and Nguyen, 2001; Ito and Komatsu, 2004), even illusory ones (von der Heydt et al, 1984), and three-point correlations provide information on figure/ground segregation (Victor and Conte, 1991; Yu et al, 2015) This suggests that a framework based on multipoint correlations can be fruitful to understand form perception. We propose a novel mechanism by which neurons in V1 and V2 generate such selectivity
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