Michael S. Lewicki

A Corrigendum on Scene analysis in the natural environment by Lewicki, M. S., Olshausen, B. A., Surlykke, A., and Moss, C. F. (2014). Front. Psychol. 5:199. doi: 10.3389/fpsyg.2014.00199 In the original article, the caption of Figure 5 contained an error of acknowledgment for the figure source. It should be: (redrawn from von der Emde, 1999). The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. von der Emde, G. (1999). Active electrolocation of objects in weakly electric fish. J. Exp. Biol. 202, 1205–1215. Pubmed Abstract | Pubmed Full Text | Google Scholar Keywords: active perception, auditory streaming, echolocation, vision, electroreception, scene analysis, neuroethology, top-down processes Citation: Lewicki MS (2014) Corrigendum: Scene analysis in the natural environment. Front. Psychol. 5:1119. doi: 10.3389/fpsyg.2014.01119 Received: 03 September 2014; Accepted: 15 September 2014; Published online: 09 October 2014. Edited and reviewed by: Peter Neri, University of Aberdeen, UK Copyright © 2014 Lewicki. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: michael.lewicki@case.edu

The problem of scene analysis has been studied in a number of different fields over the past decades. These studies have led to a number of important insights into problems of scene analysis, but not all of these insights are widely appreciated. Despite this progress, there are also critical shortcomings in current approaches that hinder further progress. Here we take the view that scene analysis is a universal problem solved by all animals, and that we can gain new insight by studying the problems that animals face in complex natural environments. In particular, the jumping spider, songbird, echolocating bat, and electric fish, all exhibit behaviors that require robust solutions to scene analysis problems encountered in the natural environment. By examining the behaviors of these seemingly disparate animals, we emerge with a framework for studying analysis comprising four essential properties: 1) the ability to solve ill-posed problems, 2) the ability to integrate and store information across time and modality, 3) efficient recovery and representation of 3D scene structure, and 4) the use of optimal motor actions for acquiring information to progress towards behavioral goals.

Robust coding has been proposed as a solution to the problem of minimizing decoding error in the presence of neural noise. Many real-world problems, however, have degradation in the input signal, not just in neural representations. This generalized problem is more relevant to biological sensory coding where internal noise arises from limited neural precision and external noise from distortion of sensory signal such as blurring and phototransduction noise. In this note, we show that the optimal linear encoder for this problem can be decomposed exactly into two serial processes that can be optimized separately. One is Wiener filtering, which optimally compensates for input degradation. The other is robust coding, which best uses the available representational capacity for signal transmission with a noisy population of linear neurons. We also present spectral analysis of the decomposition that characterizes how the reconstruction error is minimized under different input signal spectra, types and amounts of degradation, degrees of neural precision, and neural population sizes.

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The theoretical principles that underlie the representation and computation of higher-order structure in natural images are poorly understood. Recently, there has been considerable interest in using information theoretic techniques, such as independent component analysis, to derive representations for natural images that are optimal in the sense of coding efficiency. Although these approaches have been successful in explaining properties of neural representations in the early visual pathway and visual cortex, because they are based on a linear model, the types of image structure that can be represented are very limited. Here, we present a hierarchical probabilistic model for learning higher-order statistical regularities in natural images. This non-linear model learns an efficient code that describes variations in the underlying probabilistic density. When applied to natural images the algorithm yields coarse-coded, sparse-distributed representations of abstract image properties such as object location, scale and texture. This model offers a novel description of higher-order image structure and could provide theoretical insight into the response properties and computational functions of lower level cortical visual areas.