Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions

Abstract

While it is universally accepted that the brain makes predictions, there is little agreement about how this is accomplished and under which conditions. Accurate prediction requires neural circuits to learn and store spatiotemporal patterns observed in the natural environment, but it is not obvious how such information should be stored, or encoded. Information theory provides a mathematical formalism that can be used to measure the efficiency and utility of different coding schemes for data transfer and storage. This theory shows that codes become efficient when they remove predictable, redundant spatial and temporal information. Efficient coding has been used to understand retinal computations and may also be relevant to understanding more complicated temporal processing in visual cortex. However, the literature on efficient coding in cortex is varied and can be confusing since the same terms are used to mean different things in different experimental and theoretical contexts. In this work, we attempt to provide a clear summary of the theoretical relationship between efficient coding and temporal prediction, and review evidence that efficient coding principles explain computations in the retina. We then apply the same framework to computations occurring in early visuocortical areas, arguing that data from rodents is largely consistent with the predictions of this model. Finally, we review and respond to criticisms of efficient coding and suggest ways that this theory might be used to design future experiments, with particular focus on understanding the extent to which neural circuits make predictions from efficient representations of environmental statistics.

Learned response dynamics reflect stimulus timing and encode temporal expectation violations in superficial layers of mouse V1

“…Consistent with principles of efficient coding (Price & Gavornik 2022), we found that training had the effect of creating relatively sparse response patterns. Using two different measures, we estimate that the training reduced the number of visually modulated cells by 20-30%.…”

“…Since information theory holds that codes gain efficiency by eliminating redundant information (see Price & Gavornik 2022 of how this relates to predictive coding in the visual system), we also examined correlation coefficients between stimuli to determine if our day 5 activity was less correlated than day 0 activity. For each sequence presentation, we calculated the Pearson correlation coefficients between all pairs of stimuli, yielding a collection of coefficients for each sequence on each day (figure 3C).…”

Gaming expertise induces meso-scale brain plasticity and efficiency mechanisms as revealed by whole-brain modeling

“…However, there is a gap in mechanistic explanations, particularly regarding how structural alterations driven by neural plasticity impact brain dynamics at the meso-and macro-scale levels. Similarly, how the specific brain dynamics associated with expertise can become more efficient [17][18][19], e. g., promoting a more expert early and late processing of visual information [20,21]. Whole-brain computational models provide a way to explore structure-function relationships [22,23] by generating brain activity based on anatomical connections and the dynamics of individual brain regions.…”

“…We explored this hypothesis by analyzing association maps obtained using Neurosynth [31], allowing us to generalize our results from video games to other cognitive domains. Lastly, our final hypothesis proposed that VGPs' connectomes exhibit a higher signal-to-noise ratio within the parieto-occipital loop [21,32,33], resulting in robustness to stimulation in noisy contexts because of videogame expertise [17]. We explored this hypothesis by applying in-silico external stimulation to homotopic pairs of occipital brain areas [34][35][36].…”

Toward a personalized closed-loop stimulation of the visual cortex: Advances and challenges

“…To achieve the ambitious objectives envisioned by cortical visual prostheses, we should be able to stimulate the occipital cortex in a way as similar as possible to the physiological response to visual stimuli, mimicking the human visual pathway ( Nirenberg and Pandarinath, 2012 ; Qiao et al, 2019 ; Brackbill et al, 2020 ; Li et al, 2022 ; Price and Gavornik, 2022 ). In this framework, we should consider that closed-loop circuits exist in just about every part of the nervous system ( Farkhondeh Tale Navi et al, 2022 ; Khodagholy et al, 2022 ).…”