Speaker
Description
Efficient coding theory proposes that perceptual systems maximize information while minimizing redundancy by tuning neural representations to natural signal statistics (Simoncelli & Olshausen, 2001). In vision, these statistics take the form of local spatial correlations in the light distribution, known as multipoint correlations. Multipoint correlations that vary most across natural scenes should be more informative and perceptually salient. For configurations of up to four pixels, natural-image variability is highest for 2-point correlations, followed by 4-point and 3-point correlations. Human sensitivity follows the same ranking (Hermundstad et al., 2014), as shown using Maximum Entropy Textures (METs), synthetic binary textures that allow precise manipulation of individual correlations (Victor & Conte, 2012). However, the neural mechanisms underlying this efficient sensitivity remain unclear. The rat visual system offers a useful model for investigating these mechanisms, particularly because rats exhibit a perceptual sensitivity ranking for multipoint correlations similar to that reported in humans (Caramellino et al., 2021). We used METs to examine how these statistics are represented along the rat homolog of the primate ventral visual stream. Multi-unit extracellular activity was recorded with Neuropixels probes in early areas V1 and LM and higher-level areas LI and LL. Using a population decoding analysis, we found that the behavioral sensitivity ranking previously reported in rats is not reflected in neural responses upstream of area LI. Higher-level areas also showed greater visual invariance, consistent with progressive abstraction of stimulus representations along the hierarchy. Additionally, higher-order correlations elicited firing-rate suppression relative to noise in early areas, suggesting limited or indirect encoding. They also produced more heterogeneous response patterns across units than lower-order correlations, indicating a diverse population code. These findings provide a neural account of efficient sensitivity to multipoint correlations, suggesting that it develops progressively along the visual hierarchy through contextual modulation and higher-level representational transformations.
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