scholarly journals Sparse deep predictive coding captures contour integration capabilities of the early visual system

2021 ◽  
Vol 17 (1) ◽  
pp. e1008629
Author(s):  
Victor Boutin ◽  
Angelo Franciosini ◽  
Frederic Chavane ◽  
Franck Ruffier ◽  
Laurent Perrinet

Both neurophysiological and psychophysical experiments have pointed out the crucial role of recurrent and feedback connections to process context-dependent information in the early visual cortex. While numerous models have accounted for feedback effects at either neural or representational level, none of them were able to bind those two levels of analysis. Is it possible to describe feedback effects at both levels using the same model? We answer this question by combining Predictive Coding (PC) and Sparse Coding (SC) into a hierarchical and convolutional framework applied to realistic problems. In the Sparse Deep Predictive Coding (SDPC) model, the SC component models the internal recurrent processing within each layer, and the PC component describes the interactions between layers using feedforward and feedback connections. Here, we train a 2-layered SDPC on two different databases of images, and we interpret it as a model of the early visual system (V1 & V2). We first demonstrate that once the training has converged, SDPC exhibits oriented and localized receptive fields in V1 and more complex features in V2. Second, we analyze the effects of feedback on the neural organization beyond the classical receptive field of V1 neurons using interaction maps. These maps are similar to association fields and reflect the Gestalt principle of good continuation. We demonstrate that feedback signals reorganize interaction maps and modulate neural activity to promote contour integration. Third, we demonstrate at the representational level that the SDPC feedback connections are able to overcome noise in input images. Therefore, the SDPC captures the association field principle at the neural level which results in a better reconstruction of blurred images at the representational level.

2004 ◽  
Vol 44 (17) ◽  
pp. 2083-2089 ◽  
Author(s):  
Tobi Delbrück ◽  
Shih-Chii Liu

2019 ◽  
Vol 5 (1) ◽  
pp. 427-449 ◽  
Author(s):  
Alison I. Weber ◽  
Kamesh Krishnamurthy ◽  
Adrienne L. Fairhall

Adaptation is a common principle that recurs throughout the nervous system at all stages of processing. This principle manifests in a variety of phenomena, from spike frequency adaptation, to apparent changes in receptive fields with changes in stimulus statistics, to enhanced responses to unexpected stimuli. The ubiquity of adaptation leads naturally to the question: What purpose do these different types of adaptation serve? A diverse set of theories, often highly overlapping, has been proposed to explain the functional role of adaptive phenomena. In this review, we discuss several of these theoretical frameworks, highlighting relationships among them and clarifying distinctions. We summarize observations of the varied manifestations of adaptation, particularly as they relate to these theoretical frameworks, focusing throughout on the visual system and making connections to other sensory systems.


2001 ◽  
Vol 187 (7) ◽  
pp. 549-558 ◽  
Author(s):  
Stefan Wilke ◽  
Andreas Thiel ◽  
Christian Eurich ◽  
Martin Greschner ◽  
Markus Bongard ◽  
...  

2017 ◽  
Vol 118 (1) ◽  
pp. 374-382 ◽  
Author(s):  
Suchitra Ramachandran ◽  
Travis Meyer ◽  
Carl R. Olson

Exposing monkeys, over the course of days and weeks, to pairs of images presented in fixed sequence, so that each leading image becomes a predictor for the corresponding trailing image, affects neuronal visual responsiveness in area TE. At the end of the training period, neurons respond relatively weakly to a trailing image when it appears in a trained sequence and, thus, confirms prediction, whereas they respond relatively strongly to the same image when it appears in an untrained sequence and, thus, violates prediction. This effect could arise from prediction suppression (reduced firing in response to the occurrence of a probable event) or surprise enhancement (elevated firing in response to the omission of a probable event). To identify its cause, we compared firing under the prediction-confirming and prediction-violating conditions to firing under a prediction-neutral condition. The results provide strong evidence for prediction suppression and limited evidence for surprise enhancement. NEW & NOTEWORTHY In predictive coding models of the visual system, neurons carry signed prediction error signals. We show here that monkey inferotemporal neurons exhibit prediction-modulated firing, as posited by these models, but that the signal is unsigned. The response to a prediction-confirming image is suppressed, and the response to a prediction-violating image may be enhanced. These results are better explained by a model in which the visual system emphasizes unpredicted events than by a predictive coding model.


2018 ◽  
Vol 105 ◽  
pp. 218-226 ◽  
Author(s):  
Qingqun Kong ◽  
Jiuqi Han ◽  
Yi Zeng ◽  
Bo Xu

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 59-59
Author(s):  
J M Zanker ◽  
M P Davey

Visual information processing in primate cortex is based on a highly ordered representation of the surrounding world. In addition to the retinotopic mapping of the visual field, systematic variations of the orientation tuning of neurons are described electrophysiologically for the first stages of the visual stream. On the way to understanding the relation of position and orientation representation, in order to give an adequate account of cortical architecture, it will be an essential step to define the minimum spatial requirements for detection of orientation. We addressed the basic question of spatial limits for detecting orientation by comparing computer simulations of simple orientation filters with psychophysical experiments in which the orientation of small lines had to be detected at various positions in the visual field. At sufficiently high contrast levels, the minimum physical length of a line whose orientation can just be resolved is not constant when presented at various eccentricities, but covaries inversely with the cortical magnification factor. A line needs to span less than 0.2 mm on the cortical surface in order to be recognised as oriented, independently of the actual eccentricity at which the stimulus is presented. This seems to indicate that human performance for this task approaches the physical limits, requiring hardly more than approximately three input elements to be activated, in order to detect the orientation of a highly visible line segment. Combined with the estimates for receptive field sizes of orientation-selective filters derived from computer simulations, this experimental result may nourish speculations of how the rather local elementary process underlying orientation detection in the human visual system can be assembled to form much larger receptive fields of the orientation-sensitive neurons known to exist in the primate visual system.


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