This article concerns the nature of evoked brain responses and the principles underlying their generation. We start with the premise that the sensory brain has evolved to represent or infer the causes of changes in its sensory inputs. The problem of inference is well formulated in statistical terms. The statistical fundaments of inference may therefore afford important constraints on neuronal implementation. By formulating the original ideas of Helmholtz on perception, in terms of modern-day statistical theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. It turns out that the problems of inferring the causes of sensory input (perceptual inference) and learning the relationship between input and cause (perceptual learning) can be resolved using exactly the same principle. Specifically, both inference and learning rest on minimizing the brain’s free energy, as defined in statistical physics. Furthermore, inference and learning can proceed in a biologically plausible fashion. Cortical responses can be seen as the brain’s attempt to minimize the free energy induced by a stimulus and thereby encode the most likely cause of that stimulus. Similarly, learning emerges from changes in synaptic efficacy that minimize the free energy, averaged over all stimuli encountered. The underlying scheme rests on empirical Bayes and hierarchical models of how sensory input is caused. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of cortical organization and responses. The aim of this article is to encompass many apparently unrelated anatomical, physiological and psychophysical attributes of the brain within a single theoretical perspective. In terms of cortical architectures, the theoretical treatment predicts that sensory cortex should be arranged hierarchically, that connections should be reciprocal and that forward and backward connections should show a functional asymmetry (forward connections are driving, whereas backward connections are both driving and modulatory). In terms of synaptic physiology, it predicts associative plasticity and, for dynamic models, spike-timing-dependent plasticity. In terms of electrophysiology, it accounts for classical and extra classical receptive field effects and long-latency or endogenous components of evoked cortical responses. It predicts the attenuation of responses encoding prediction error with perceptual learning and explains many phenomena such as repetition suppression, mismatch negativity (MMN) and the P300 in electroencephalography. In psychophysical terms, it accounts for the behavioural correlates of these physiological phenomena, for example, priming and global precedence. The final focus of this article is on perceptual learning as measured with the MMN and the implications for empirical studies of coupling among cortical areas using evoked sensory responses.

/pdf/a-theory-of-cortical-responses-2aiw9zqf1h.pdf

A theory of cortical responses

An important component of routine visual behavior is the ability to find one item in a visual world filled with other, distracting items. This ability to performvisual search has been the subject of a large body of research in the past 15 years. This paper reviews the visual search literature and presents a model of human search behavior. Built upon the work of Neisser, Treisman, Julesz, and others, the model distinguishes between a preattentive, massively parallel stage that processes information about basic visual features (color, motion, various depth cues, etc.) across large portions of the visual field and a subsequent limited-capacity stage that performs other, more complex operations (e.g., face recognition, reading, object identification) over a limited portion of the visual field. The spatial deployment of the limited-capacity process is under attentional control. The heart of the guided search model is the idea that attentional deployment of limited resources isguided by the output of the earlier parallel processes. Guided Search 2.0 (GS2) is a revision of the model in which virtually all aspects of the model have been made more explicit and/or revised in light of new data. The paper is organized into four parts: Part 1 presents the model and the details of its computer simulation. Part 2 reviews the visual search literature on preattentive processing of basic features and shows how the GS2 simulation reproduces those results. Part 3 reviews the literature on the attentional deployment of limited-capacity processes in conjunction and serial searches and shows how the simulation handles those conditions. Finally, Part 4 deals with shortcomings of the model and unresolved issues.

/pdf/guided-search-2-0-a-revised-model-of-visual-search-1qi4v6qhev.pdf

Guided Search 2.0 A revised model of visual search

The "independent components" of natural scenes are edge filters.

The distributed brain systems associated with performance of a verbal fluency task were identified in a nondirected correlational analysis of neurophysiological data obtained with positron tomography. This analysis used a recursive principal-component analysis developed specifically for large data sets. This analysis is interpreted in terms of functional connectivity, defined as the temporal correlation of a neurophysiological index measured in different brain areas. The results suggest that the variance in neurophysiological measurements, introduced experimentally, was accounted for by two independent principal components. The first, and considerably larger, highlighted an intentional brain system seen in previous studies of verbal fluency. The second identified a distributed brain system including the anterior cingulate and Wernicke's area that reflected monotonic time effects. We propose that this system has an attentional bias.

https://journals.sagepub.com/doi/pdf/10.1038/jcbfm.1993.4

Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets

There is a bewildering diversity of signals, sensory systems, and signaling behavior. A consideration of how these traits affect each other's evolution explains some of this diversity. Natural selection favors signals, receptors, and signaling behavior that maximize the received signals relative to background noise and minimize signal degradation. Properties of sensory systems bias the direction of evolution of the signals that they receive. For example, females may prefer males whose signals they can perceive more easily, and this will lead to the spread of more easily perceived male traits. Environmental conditions during signal transmission and detection also affect signal perception. Specific environmental conditions will bias the evolutionary direction of behavior, which affects the time and place of signaling as well as microhabitat preferences. Increased specialization of microhabitats and signaling behavior may lead to biased evolution of the sensory systems to work more efficiently. Thus, sensory...

/pdf/signals-signal-conditions-and-the-direction-of-evolution-1s36taz7km.pdf

Signals, signal conditions, and the direction of evolution

Professor Colin Blakemore presents a fascinating insight to all the major topics in visual science research. Experts from around the world show how this vast subject can be unified from the viewpoint that describes the way in which visual systems efficiently encode and represent the outside world. The approach, which is both rigorous and general, was championed by H. B. Barlow in the fifties and has recently acquired a new significance in the light of exciting developments in computer science and artificial intelligence and vision. The book is essential reading for advanced undergraduates, postgraduates and researchers in the field of vision research and neuroscience.

Vision: Coding and Efficiency

Part I: The Body of the Artist Part II: Bodies of Masculinity Part III: Bodies of Femininity Part IV: The Body as Language.

The body imaged : the human form and visual culture since the Renaissance

Statistical limits to image understanding

The Body Imaged: The Human Form and Visual Culture since the Renaissance@@@The Female Nude: Art, Obscenity and Sexuality

The design of compound eyes

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