158 research outputs found
Platonic model of mind as an approximation to neurodynamics
Hierarchy of approximations involved in simplification of microscopic theories, from sub-cellural to the whole brain level, is presented. A new approximation to neural dynamics is described, leading to a Platonic-like model of mind based on psychological spaces. Objects and events in these spaces correspond to quasi-stable states of brain dynamics and may be interpreted from psychological point of view. Platonic model bridges the gap between neurosciences and psychological sciences. Static and dynamic versions of this model are outlined and Feature Space Mapping, a neurofuzzy realization of the static version of Platonic model, described. Categorization experiments with human subjects are analyzed from the neurodynamical and Platonic model points of view
Computational physics of the mind
In the XIX century and earlier such physicists as Newton, Mayer, Hooke, Helmholtz and Mach were actively engaged in the research on psychophysics, trying to relate psychological sensations to intensities of physical stimuli. Computational physics allows to simulate complex neural processes giving a chance to answer not only the original psychophysical questions but also to create models of mind. In this paper several approaches relevant to modeling of mind are outlined. Since direct modeling of the brain functions is rather limited due to the complexity of such models a number of approximations is introduced. The path from the brain, or computational neurosciences, to the mind, or cognitive sciences, is sketched, with emphasis on higher cognitive functions such as memory and consciousness. No fundamental problems in understanding of the mind seem to arise. From computational point of view realistic models require massively parallel architectures
Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1
Previous studies in that line suggested that lateral interactions of V1 cells
are responsible, among other visual effects, of bottom-up visual attention
(alternatively named visual salience or saliency). Our objective is to mimic
these connections in the visual system with a neurodynamic network of
firing-rate neurons. Early subcortical processes (i.e. retinal and thalamic)
are functionally simulated. An implementation of the cortical magnification
function is included to define the retinotopical projections towards V1,
processing neuronal activity for each distinct view during scene observation.
Novel computational definitions of top-down inhibition (in terms of inhibition
of return and selection mechanisms), are also proposed to predict attention in
Free-Viewing and Visual Search conditions. Results show that our model
outpeforms other biologically-inpired models of saliency prediction as well as
to predict visual saccade sequences during free viewing. We also show how
temporal and spatial characteristics of inhibition of return can improve
prediction of saccades, as well as how distinct search strategies (in terms of
feature-selective or category-specific inhibition) predict attention at
distinct image contexts.Comment: 32 pages, 19 figure
Autism and ADHD – two ends of the same spectrum?
Analysis of dynamics of biologically motivated neural networks allows for studying non-linear processes responsible for cognitive functions and thus provides adequate language to understand complex mental processes, including psychiatric syndromes and disorders. Problems with attention shifts that are at the roots of Autism Spectrum Disorders (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD), have been investigated using network model of Posner Visual Orienting Task (PVOT). Changing parameters that control biophysical properties of model neurons and cause network dysfunctions provides plausible explanations of many strange ASD and ADHD phenomena
A role for recurrent processing in object completion: neurophysiological, psychophysical and computational"evidence
Recognition of objects from partial information presents a significant
challenge for theories of vision because it requires spatial integration and
extrapolation from prior knowledge. We combined neurophysiological recordings
in human cortex with psychophysical measurements and computational modeling to
investigate the mechanisms involved in object completion. We recorded
intracranial field potentials from 1,699 electrodes in 18 epilepsy patients to
measure the timing and selectivity of responses along human visual cortex to
whole and partial objects. Responses along the ventral visual stream remained
selective despite showing only 9-25% of the object. However, these visually
selective signals emerged ~100 ms later for partial versus whole objects. The
processing delays were particularly pronounced in higher visual areas within
the ventral stream, suggesting the involvement of additional recurrent
processing. In separate psychophysics experiments, disrupting this recurrent
computation with a backward mask at ~75ms significantly impaired recognition of
partial, but not whole, objects. Additionally, computational modeling shows
that the performance of a purely bottom-up architecture is impaired by heavy
occlusion and that this effect can be partially rescued via the incorporation
of top-down connections. These results provide spatiotemporal constraints on
theories of object recognition that involve recurrent processing to recognize
objects from partial information
Perceptual abstraction and attention
This is a report on the preliminary achievements of WP4 of the IM-CleVeR project on abstraction for cumulative learning, in particular directed to: (1) producing algorithms to develop abstraction features under top-down action influence; (2) algorithms for supporting detection of change in motion pictures; (3) developing attention and vergence control on the basis of locally computed rewards; (4) searching abstract representations suitable for the LCAS framework; (5) developing predictors based on information theory to support novelty detection. The report is organized around these 5 tasks that are part of WP4. We provide a synthetic description of the work done for each task by the partners
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