4,164 research outputs found
Large-scale network organization in the avian forebrain: a connectivity matrix and theoretical analysis
Many species of birds, including pigeons, possess demonstrable cognitive capacities, and some are capable of cognitive feats matching those of apes. Since mammalian cortex is laminar while the avian telencephalon is nucleated, it is natural to ask whether the brains of these two cognitively capable taxa, despite their apparent anatomical dissimilarities, might exhibit common principles of organisation on some level. Complementing recent investigations of macro-scale brain connectivity in mammals, including humans and macaques, we here present the first large-scale wiring diagram for the forebrain of a bird. Using graph theory, we show that the pigeon telencephalon is organised along similar lines to that of a mammal. Both are modular, small-world networks with a connective core of hub nodes that includes prefrontal-like and hippocampal structures. These hub nodes are, topologically speaking, the most central regions of the pigeon's brain, as well as being the most richly connected, implying a crucial role in information flow. Overall, our analysis suggests that indeed, despite the absence of cortical layers and close to 300 million years of separate evolution, the connectivity of the avian brain conforms to the same organisational principles as the mammalian brain
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
Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks
A long-term goal of AI is to produce agents that can learn a diversity of
skills throughout their lifetimes and continuously improve those skills via
experience. A longstanding obstacle towards that goal is catastrophic
forgetting, which is when learning new information erases previously learned
information. Catastrophic forgetting occurs in artificial neural networks
(ANNs), which have fueled most recent advances in AI. A recent paper proposed
that catastrophic forgetting in ANNs can be reduced by promoting modularity,
which can limit forgetting by isolating task information to specific clusters
of nodes and connections (functional modules). While the prior work did show
that modular ANNs suffered less from catastrophic forgetting, it was not able
to produce ANNs that possessed task-specific functional modules, thereby
leaving the main theory regarding modularity and forgetting untested. We
introduce diffusion-based neuromodulation, which simulates the release of
diffusing, neuromodulatory chemicals within an ANN that can modulate (i.e. up
or down regulate) learning in a spatial region. On the simple diagnostic
problem from the prior work, diffusion-based neuromodulation 1) induces
task-specific learning in groups of nodes and connections (task-specific
localized learning), which 2) produces functional modules for each subtask, and
3) yields higher performance by eliminating catastrophic forgetting. Overall,
our results suggest that diffusion-based neuromodulation promotes task-specific
localized learning and functional modularity, which can help solve the
challenging, but important problem of catastrophic forgetting
Recognition without identification, erroneous familiarity, and déjà vu
Déjà vu is characterized by the recognition of a situation concurrent with the awareness that this recognition is inappropriate. Although forms of déjà vu resolve in favor of the inappropriate recognition and therefore have behavioral consequences, typical déjà vu experiences resolve in favor of the awareness that the sensation of recognition is inappropriate. The resultant lack of behavioral modification associated with typical déjà vu means that clinicians and experimenters rely heavily on self-report when observing the experience. In this review, we focus on recent déjà vu research. We consider issues facing neuropsychological, neuroscientific, and cognitive experimental frameworks attempting to explore and experimentally generate the experience. In doing this, we suggest the need for more experimentation and amore cautious interpretation of research findings, particularly as many techniques being used to explore déjà vu are in the early stages of development.PostprintPeer reviewe
Modular structure in C. elegans neural network and its response to external localized stimuli
Synchronization plays a key role in information processing in neuronal
networks. Response of specific groups of neurons are triggered by external
stimuli, such as visual, tactile or olfactory inputs. Neurons, however, can be
divided into several categories, such as by physical location, functional role
or topological clustering properties. Here we study the response of the
electric junction C. elegans network to external stimuli using the partially
forced Kuramoto model and applying the force to specific groups of neurons.
Stimuli were applied to topological modules, obtained by the ModuLand
procedure, to a ganglion, specified by its anatomical localization, and to the
functional group composed of all sensory neurons. We found that topological
modules do not contain purely anatomical groups or functional classes,
corroborating previous results, and that stimulating different classes of
neurons lead to very different responses, measured in terms of synchronization
and phase velocity correlations. In all cases, however, the modular structure
hindered full synchronization, protecting the system from seizures. More
importantly, the responses to stimuli applied to topological and functional
modules showed pronounced patterns of correlation or anti-correlation with
other modules that were not observed when the stimulus was applied to ganglia.Comment: 23 pages, 6 figure
Brain Modularity Mediates the Relation between Task Complexity and Performance
Recent work in cognitive neuroscience has focused on analyzing the brain as a
network, rather than as a collection of independent regions. Prior studies
taking this approach have found that individual differences in the degree of
modularity of the brain network relate to performance on cognitive tasks.
However, inconsistent results concerning the direction of this relationship
have been obtained, with some tasks showing better performance as modularity
increases and other tasks showing worse performance. A recent theoretical model
(Chen & Deem, 2015) suggests that these inconsistencies may be explained on the
grounds that high-modularity networks favor performance on simple tasks whereas
low-modularity networks favor performance on more complex tasks. The current
study tests these predictions by relating modularity from resting-state fMRI to
performance on a set of simple and complex behavioral tasks. Complex and simple
tasks were defined on the basis of whether they did or did not draw on
executive attention. Consistent with predictions, we found a negative
correlation between individuals' modularity and their performance on a
composite measure combining scores from the complex tasks but a positive
correlation with performance on a composite measure combining scores from the
simple tasks. These results and theory presented here provide a framework for
linking measures of whole brain organization from network neuroscience to
cognitive processing.Comment: 47 pages; 4 figure
Neurofly 2008 abstracts : the 12th European Drosophila neurobiology conference 6-10 September 2008 Wuerzburg, Germany
This volume consists of a collection of conference abstracts
- …