How Does the Cerebral Cortex Work? Development, Learning, Attention, and 3-D Vision by Laminar Circuits of Visual Cortex

Neural dynamics of autistic behaviors: cognitive, emotional, and timing substrates

What brain mechanisms underlie autism, and how do they give rise to autistic behavioral symptoms? This article describes a neural model, called the Imbalanced Spectrally Timed Adaptive Resonance Theory (iSTART) model, that proposes how cognitive, emotional, timing, and motor processes that involve brain regions such as the prefrontal and temporal cortex, amygdala, hippocampus, and cerebellum may interact to create and perpetuate autistic symptoms. These model processes were originally developed to explain data concerning how the brain controls normal behaviors. The iSTART model shows how autistic behavioral symptoms may arise from prescribed breakdowns in these brain processes, notably a combination of underaroused emotional depression in the amygdala and related affective brain regions, learning of hyperspecific recognition categories in the temporal and prefrontal cortices, and breakdowns of adaptively timed attentional and motor circuits in the hippocampal system and cerebellum. The model clarifies how malfunctions in a subset of these mechanisms can, through a systemwide vicious circle of environmentally mediated feedback, cause and maintain problems with them all.

Relearning toward motor recovery in stroke, spinal cord injury, and cerebral palsy: a cognitive neural systems perspective

In stroke, spinal cord injury, and cerebral palsy there is denervation of target neuron centers, which are self-organizing maps (SOMs) within the neuraxis. Compensatory reinnervation occurs within those SOMs by acquiring synaptic sprouts from whatever neurons in the neighborhood. Such reorganizations are more often maladaptive than beneficial. Motor recovery, if any appears, is incomplete and compromised. Cognitive systems studies indicate that motor paralysis is due to loss of learning <--> recall balance in those compensated SOMs, which had been locked into a stability <--> plasticity dilemma. Treatment/rehabilitation should aim therefore to first restore this learning related balance. The use of botulinum toxin as a neuromotor relearning tool to improve motor recovery is discussed.

Effects of Selective Attention on Perceptual Filling-in

After few seconds, a figure steadily presented in peripheral vision becomes perceptually filled-in by its background, as if it “disappeared”. We report that directing attention to the color, shape, or location of a figure increased the probability of perceiving filling-in compared to unattended figures, without modifying the time required for filling-in. This effect could be augmented by boosting attention. Furthermore, the frequency distribution of filling-in response times for attended figures could be predicted by multiplying the frequencies of response times for unattended figures with a constant. We propose that, after failure of figure–ground segregation, the neural interpolation processes that produce perceptual filling-in are enhanced in attended figure regions. As filling-in processes are involved in surface perception, the present study demonstrates that even very early visual processes are subject to modulation by cognitive factors.

Perceptual filling-in: More than the eye can see

When a gray figure is surrounded by a background of dynamic texture, fixating away from the figure for several seconds will result in an illusory replacement of the figure by its background. This visual illusion is referred to as perceptual filling-in. The study of filling-in is important, because the underlying neural processes compensate for imperfections in our visual system (e.g., the blind spot) and contribute to normal surface perception. A long-standing question has been whether perceptual filling-in results from symbolic tagging of surface regions in higher order cortex (ignoring the absence of information), or from active neural interpolation in lower order visual areas (active filling-in of information). The present chapter reviews a number of psychophysical studies in human subjects and physiological experiments in monkeys to evaluate the above two hypotheses. The data combined show that there is strong evidence for neural interpolation processes in retinotopically organized, lower order areas, but that there is also a role for higher order perceptual and cognitive factors such as attention.

The watercolor illusion and neon color spreading: a unified analysis of new cases and neural mechanisms

Coloration and figural properties of neon color spreading and the watercolor illusion are studied using phenomenal and psychophysical observations. Coloration properties of both effects can be reduced to a common limiting condition, a nearby color transition called the two-dot limiting case, which clarifies their perceptual similarities and dissimilarities. The results are explained by the FACADE neural model of biological vision. The model proposes how local properties of color transitions activate spatial competition among nearby perceptual boundaries, with boundaries of lower-contrast edges weakened by competition more than boundaries of higher-contrast edges. This asymmetry induces spreading of more color across these boundaries than conversely. The model also predicts how depth and figure-ground effects are generated in these illusions.

ARTSTREAM: a neural network model of auditory scene analysis and source segregation

Multiple sound sources often contain harmonics that overlap and may be degraded by environmental noise. The auditory system is capable of teasing apart these sources into distinct mental objects, or streams. Such an 'auditory scene analysis' enables the brain to solve the cocktail party problem. A neural network model of auditory scene analysis, called the ARTSTREAM model, is presented to propose how the brain accomplishes this feat. The model clarifies how the frequency components that correspond to a given acoustic source may be coherently grouped together into a distinct stream based on pitch and spatial location cues. The model also clarifies how multiple streams may be distinguished and separated by the brain. Streams are formed as spectral-pitch resonances that emerge through feedback interactions between frequency-specific spectral representations of a sound source and its pitch. First, the model transforms a sound into a spatial pattern of frequency-specific activation across a spectral stream layer. The sound has multiple parallel representations at this layer. A sound's spectral representation activates a bottom-up filter that is sensitive to the harmonics of the sound's pitch. This filter activates a pitch category which, in turn, activates a top-down expectation that is also sensitive to the harmonics of the pitch. Resonance develops when the spectral and pitch representations mutually reinforce one another. Resonance provides the coherence that allows one voice or instrument to be tracked through a noisy multiple source environment. Spectral components are suppressed if they do not match harmonics of the top-down expectation that is read-out by the selected pitch, thereby allowing another stream to capture these components, as in the 'old-plus-new heuristic' of Bregman. Multiple simultaneously occurring spectral-pitch resonances can hereby emerge. These resonance and matching mechanisms are specialized versions of Adaptive Resonance Theory, or ART, which clarifies how pitch representations can self-organize during learning of harmonic bottom-up filters and top-down expectations. The model also clarifies how spatial location cues can help to disambiguate two sources with similar spectral cues. Data are simulated from psychophysical grouping experiments, such as how a tone sweeping upwards in frequency creates a bounce percept by grouping with a downward sweeping tone due to proximity in frequency, even if noise replaces the tones at their intersection point. Illusory auditory percepts are also simulated, such as the auditory continuity illusion of a tone continuing through a noise burst even if the tone is not present during the noise, and the scale illusion of Deutsch whereby downward and upward scales presented alternately to the two ears are regrouped based on frequency proximity, leading to a bounce percept. Since related sorts of resonances have been used to quantitatively simulate psychophysical data about speech perception, the model strengthens the hypothesis that ART-like mechanisms are used at multiple levels of the auditory system. Proposals for developing the model to explain more complex streaming data are also provided.