Abstract
Sensory processing relies on efficient computation driven by a combination of low-level unsupervised, statistical structural learning, and high-level task-dependent learning. In the earliest stages of sensory processing, sparse and independent coding strategies are capable of modeling neural processing using the same coding strategy with only a change in the input (e.g., grayscale images, color images, and audio). We present a consolidated review of Independent Component Analysis (ICA) as an efficient neural coding scheme with the ability to model early visual and auditory neural processing. We created a self-contained, accessible Jupyter notebook using Python to demonstrate the efficient coding principle for different modalities following a consistent five-step strategy. For each modality, derived receptive field models from natural and non-natural inputs are contrasted, demonstrating how neural codes are not produced when the inputs sufficiently deviate from those animals were evolved to process. Additionally, the demonstration shows that ICA produces more neurally-appropriate receptive field models than those based on common compression strategies, such as Principal Component Analysis. The five-step strategy not only produces neural-like models but also promotes reuse of code to emphasize the input-agnostic nature where each modality can be modeled with only a change in inputs. This notebook can be used to readily observe the links between unsupervised machine learning strategies and early sensory neuroscience, improving our understanding of flexible data-driven neural development in nature and future applications.
Highlights
Bridging the gap between neuroscience and computational approaches presents a mutual benefit to both neuroscientists and computer scientists
The nature of biological systems to perform with high accuracy and extraordinary efficiency in complicated and uncertain environments has led brain-inspired modeling to be a natural frame of reference for advances in Artificial Intelligence (AI) (Fong et al 2018)
Measurements of primary visual cortex (V1) simple cell responses to stimuli demonstrate response properties that can be approximated by a 2D Gabor wavelet code (Fig. 1) (Hubel and Wiesel 1962, 1968; Jones and Palmer 1987b), but why such a code among all the alternative coding strategies? The efficient coding hypothesis proposes that the goal of early sensory processing is to reduce redundancy (Barlow 1961; Field 1987)
Summary
Bridging the gap between neuroscience and computational approaches presents a mutual benefit to both neuroscientists and computer scientists. Only efficient encoding objectives which are appropriate for neural representations have been found to produce more efficient representations; such representations can be contrasted to compact efficient codes such as PCA or other traditional factor analysis techniques (Field 1994). It is only these neurally-appropriate efficient strategies, such as sparse coding or ICA, applied to natural images that yield filters resembling the 2D Gabor functions seen in early sensory processing
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