Dynamics and representation in the primary visual cortex

Abstract

We investigate the processing and representation of static visual patterns in the early visual system of mammals (especially cats and primates). We demonstrate that neurophysiological and anatomical findings can motivate theoretical considerations about the neural processing and vice versa. We explore “How?” and “Why?” questions in a close connection to each other. Methodologically this means using biologically detailed “bottom-up” computational models and abstract “top-down” models in parallel or in combination. Specifically, we focus on the contrastand orientation-processing in the primary visual cortex (V1) with a strong emphasis on the dynamics of the neural activity and synapses. We consider neural dynamics on three different time scales: (i) the fast time evolution of the cortical activity with a time constant of 16 20 msec; (ii) the intermediate modulation of the recurrent cortical competition strength with a time constant in the order of 100 200 msec (the approximate length of a fixation period); (iii) contrast adaptation by the slow modulation of the dynamic nature of the synaptic transmission with a time constant of 5 10 sec. Firstly, we explore how orientation selectivity could be generated in the primary visual cortex (V1) (chapters 2, 3). Orientation selectivity is a remarkable and well-explored feature of the simple cells in V1. However, there is still considerable debate about the neurophysiological and anatomical origin of the highly feature selective response of these cells. The major question concerns the extent to which the simple cell properties are determined by the structure of their feed-forward connectivity versus the recurrent projections. In contrast to previous models, in which the initial orientation bias is generated by convergent geniculate (feed-forward) input to the simple cells, and subsequently sharpened by the lateral circuits, our approach is based on anisotropic intracortical excitatory connections. We study the hypothesis that these recurrent projections provide both the initial orientation bias and its subsequent amplification and therefore orientation selectivity is generated purely intracortically. Our computational study shows that indeed the “intracortical hypothesis” is a plausible alternative to the other existing hypotheses. The model predicts that the dynamics of the orientation tuning could be indicative of the underlying neural mechanism. Therefore we investigate recurrent dynamics in a cortical orientation hypercolumn in a more biologically detailed statistical neural field model (chapter 3). Secondly, we study why the recurrent cortical re-processing of the feed-forward input is important for the representation of the image projected on the retina (chapter 4). We propose that the recurrent lateral connections implement competition between orientation selective simple cells with overlapping receptive fields. Then, we introduce the concept of “dynamic coding”, and investigate the short term dynamics of the recurrent competition in the primary visual cortex in terms of information processing. We find that information transfer is optimal in any increasing time window after stimulus onset if the recurrent cortical amplification decreases. In the model, the initially strong cortical competition decreases, and the role of the geniculate origin feed-forward projections becomes more important. These geniculo-cortical projections carry a topographic representation of the image projected to the retina. Motivated by information theory, our results offer a compromise between the “feed-forward” and the “recurrent” hypotheses for orientation selectivity. We suggest that both are valid, however, in different phases of the cortical processing during a fixation period. In the initial phase of processing, the recurrent competition is strong, and the salient orientation is signaled in a winner-take-all fashion. In the second phase, cortical competition becomes weaker, allowing the detection of multiple orientations. A detailed computational model provides experimentally testable