CTN: Gaia Tavoni

Title: A Unified Framework for Sensory Coding in Feedback-Modulated Canonical Networks

Abstract: In recent decades, the principles of neural coding have largely been studied at the level of single neurons or unimodal sensory networks. However, brain networks interact in complex ways, often integrating information across sensory modalities. Notably, we lack a theoretical framework for understanding coding in interacting networks, where information can converge from different sources. In this talk, I will introduce a fully analytical normative framework for neural coding in feedback-modulated canonical networks, a ubiquitous motif in the brain. In our model, feedback is exogenous rather than endogenous to a given modality, mediating interactions between the senses. Our theory demonstrates that predictive coding is an emergent property of efficient codes, unifying two primary coding schemes. It further demonstrates how the computational principles of efficient and predictive coding can be implemented at the algorithmic level by a shared neural substrate, with different network components performing distinct and interpretable mathematical operations. Finally, our theory explains a variety of observed unimodal and multimodal sensory effects within the same normative framework and makes new predictions about the role of feedback in optimizing multimodal codes. I will conclude by showing how optimal sensory codes can be learned in biological networks through distributed Hebbian learning. Altogether, our theory provides a unifying view of computational, algorithmic, and implementational principles of sensory coding in feedback-modulated canonical networks.