Emergence of Auditory Receptive Fields based on Surprise

Yashaswini, Sneha Dash, Sharba Bandyopadhyay

Keywords: Auditory receptive fields, Bayesian surprise, sparse coding, Oddball paradigm, predictive inference, Autoregressive generative modeling, efficient sensory coding, biologically inspired learning

TL;DR: This work presents a biologically sound theory of auditory receptive field formation, showing how sparse, frequency-selective representations emerge from surprise-driven predictive inference using 2 computational frameworks: CochleaNet and Kalman-MI.

Abstract: Understanding how sensory systems efficiently encode natural stimuli is a fundamental challenge in neuroscience. While the efficient coding hypothesis explains many aspects of sensory processing, its role in processing surprising auditory inputs remains unclear. We present two computational frameworks modeling the development of auditory neural receptive fields via unsupervised learning to address this challenge. In the first framework, a single-layer network’s synaptic weights adapt to auditory inputs to maximize activations for surprising events while minimizing overall activity. The weights are adjusted using three factors $(\alpha, \beta, \gamma)$ and the gradient of the $l1$ norm of activations. An autoregressive generative model (CochleaNet), trained on LibriSpeech, provides the joint probability distribution to calculate surprise, defined as the negative log probability of time-frequency bin energy conditioned on previous time steps and other frequency channels. We find learning to be fast, with robust convergence of weights using random speech samples. This approach yields spectrotemporal receptive fields (STRFs) whose tuning properties closely match neurophysiological observations. Second, we propose a principled Kalman-MI formulation in which the generative prior, latent auditory state, and synaptic weights are jointly updated online. Mutual-information gradients, serving as a normative proxy for expected surprise reduction, drive adaptation in a linear-Gaussian state-space model, producing deviant-selective receptive fields in an oddball paradigm. Together, these approaches aim to refine the interplay between sparse coding and surprise-driven learning, offering new insights into efficient sensory encoding.

Submission Number: 123