How self-organization can guide evolution.

Self-organization and natural selection are fundamental forces that shape the natural world. Substantial progress in understanding how these forces interact has been made through the study of abstract models. Further progress may be made by identifying a model system in which the interaction between self-organization and selection can be investigated empirically.

A Self-Organising Model of Thermoregulatory Huddling.

Endotherms such as rats and mice huddle together to keep warm. The huddle is considered to be an example of a self-organising system, because complex properties of the collective group behaviour are thought to emerge spontaneously through simple interactions between individuals. Groups of rodent pups display two such emergent properties.

Using reaction times and binary responses to estimate psychophysical performance: an information theoretic analysis.

As the strength of a stimulus increases, the proportions of correct binary responses increases, which define the psychometric function. Simultaneously, mean reaction times (RT) decrease, which collectively define the chronometric function. However, RTs are traditionally ignored when estimating psychophysical parameters, even though they may provide additional Shannon information.

Marr: an appreciation.

The following is based largely on sections of our two recent books: Frisby and Stone (2010, Seeing MIT Press), and Stone (2012, Vision and Brain MIT Press). Those books are aimed at student/ novice audiences, and so we have eliminated material of a wholly introductory nature for this special issue of Perception. However, various debates we have had at vision conferences recently suggest to us that going over basic material on Marr could be useful to many current vision researchers who have had little contact with his work, so we have left in some content of that kind.

Footprints sticking out of the sand. Part 2: children's Bayesian priors for shape and lighting direction.

The shading information in images that depict surfaces of 3-D objects cannot be perceived correctly unless the direction of the illuminating light source is known and, in the absence of this knowledge, perception in adults is consistent with a light-from-above Bayesian prior assumption. In order to investigate if children make use of a similar assumption, 171 children between the ages of 4.6 and 10.

Footprints sticking out of the sand. Part 1: Children's perception of naturalistic and embossed symbol stimuli.

The shading information in images that depict surfaces of three-dimensional objects cannot be perceived correctly unless the direction of the illuminating light source is known, and, in the absence of this knowledge, adults interpret such images by assuming that light comes from above. In order to investigate if children make use of a similar assumption, we analysed data from 171 children between the ages of 4.6 and 10.

Falling towards forgetfulness: synaptic decay prevents spontaneous recovery of memory.

Long after a new language has been learned and forgotten, relearning a few words seems to trigger the recall of other words. This "free-lunch learning" (FLL) effect has been demonstrated both in humans and in neural network models. Specifically, previous work proved that linear networks that learn a set of associations, then partially forget them all, and finally relearn some of the associations, show improved performance on the remaining (i.

Distributed representations accelerate evolution of adaptive behaviours.

Animals with rudimentary innate abilities require substantial learning to transform those abilities into useful skills, where a skill can be considered as a set of sensory-motor associations. Using linear neural network models, it is proved that if skills are stored as distributed representations, then within-lifetime learning of part of a skill can induce automatic learning of the remaining parts of that skill. More importantly, it is shown that this "free-lunch" learning (FLL) is responsible for accelerated evolution of skills, when compared with networks which either 1) cannot benefit from FLL or 2) cannot learn.

Independent component analysis: an introduction.

Independent component analysis (ICA) is a method for automatically identifying the underlying factors in a given data set. This rapidly evolving technique is currently finding applications in analysis of biomedical signals (e.g.

Recurrent cerebellar architecture solves the motor-error problem.

Current views of cerebellar function have been heavily influenced by the models of Marr and Albus, who suggested that the climbing fibre input to the cerebellum acts as a teaching signal for motor learning. It is commonly assumed that this teaching signal must be motor error (the difference between actual and correct motor command), but this approach requires complex neural structures to estimate unobservable motor error from its observed sensory consequences. We have proposed elsewhere a recurrent decorrelation control architecture in which Marr-Albus models learn without requiring motor error.

Visual awareness and the cerebellum: possible role of decorrelation control.

The two roles in awareness most often suggested for the cerebellum are (i) keeping the details of motor skills away from forebrain computation, and (ii) signaling to the forebrain when a sensory event is not predictable from prior motor commands. However, it is unclear how current models of the cerebellum could carry out these roles. Their architecture, based on the seminal ideas of Marr and Albus, appears to need 'motor error' to learn correct motor commands.

Decorrelation control by the cerebellum achieves oculomotor plant compensation in simulated vestibulo-ocular reflex.

We introduce decorrelation control as a candidate algorithm for the cerebellar microcircuit and demonstrate its utility for oculomotor plant compensation in a linear model of the vestibulo-ocular reflex (VOR). Using an adaptive-filter representation of cerebellar cortex and an anti-Hebbian learning rule, the algorithm learnt to compensate for the oculomotor plant by minimizing correlations between a predictor variable (eye-movement command) and a target variable (retinal slip), without requiring a motor-error signal. Because it also provides an estimate of the unpredicted component of the target variable, decorrelation control can simplify both motor coordination and sensory acquisition.

Making connections about brain connectivity.

The Functional Brain Connectivity workshop was organized by Rolf Kötter and Karl Friston, and held in Düsseldorf, Germany, on 4-6 April, 2002.