Modeling Sensory Encoding: Computational Approaches to Feature Detection, Integration, and Adaptation

Sensory encoding is the vital process by which the nervous system transforms physical stimuli into meaningful neural representations. Traditionally, computational neuroscience has characterized this through receptive fields and stimulus-response functions. However, sensory systems do not operate in a vacuum; they must extract relevant features from high-dimensional, noisy environments while constantly adjusting to changing contexts. From the retina’s light adaptation to the auditory system’s frequency tuning, the brain employs sophisticated computational strategies to ensure efficient information transmission. As our ability to record from large sensory populations grows, we are moving beyond simple linear models toward theories that encompass non-linear dynamics, population-level interactions, and the influence of feedback. Understanding these principles is essential for deciphering how the brain constructs a coherent internal map of the external world.The central challenge in sensory neuroscience is explaining how the brain maintains an optimal balance between sensitivity and stability. Sensory circuits must detect minute signals in a sea of noise, yet they must also adapt their gain to prevent saturation when stimuli are intense. Furthermore, most current models treat sensory modalities in isolation, failing to account for how the brain integrates disparate cues, such as sight and sound, into a unified perceptual object.The goal of this Research Topic is to present innovative computational models that explore the mechanisms of feature detection, contextual integration, and sensory adaptation. We aim to address the fundamental trade-offs between metabolic efficiency, speed, and accuracy in sensory "front-ends." By bringing together research that utilizes information theory, Bayesian inference, and biophysically detailed simulations, we hope to uncover the universal principles of encoding that apply across different modalities. This collection will serve to bridge the gap between low-level physiological mechanisms and the high-level perceptual phenomena they support, providing a foundation for both basic research and the design of advanced artificial sensors.We invite contributors to submit articles that utilize computational methods to investigate the encoding of sensory information. We encourage studies that focus on the mathematical and theoretical foundations of how neurons, either individually or as populations, process sensory inputs across visual, auditory, somatosensory, olfactory, and gustatory systems.Specific themes of interest include:- Normative models of sensory adaptation and dynamic gain control.- Information-theoretic and Bayesian approaches to feature extraction and stimulus reconstruction.- Computational mechanisms of multi-sensory integration and cross-modal cue combination.- The role of neural noise and trial-to-trial variability in sensory encoding precision.- Population coding and manifold representations in primary sensory cortices.- Efficient coding principles and the impact of metabolic constraints on sensory circuits.- Predictive coding frameworks for sensory processing and error detection.- Developmental and experience-dependent plasticity in sensory maps. Topic editors: Shaobing Gao, Disheng Tang Source: https://www.frontiersin.org/research-topics/82992/modeling-sensory-encoding-computational-approaches-to-feature-detection-integration-and-adaptation