971 research outputs found
Consciousness, cognition, and the hierarchy of context: extending the global neuronal workspace model
We adapt an information theory analysis of interacting cognitive biological and social modules to the problem of the global neuronal workspace, the new standard neuroscience paradigm for consciousness. Tunable punctuation emerges in a natural way, suggesting the possibility of fitting appropriate phase transition power law, and away from transition, generalized Onsager relation expressions, to observational data on conscious reaction. The development can be extended in a straightforward manner to include psychosocial stress, culture, or other cognitive modules which constitute a structured, embedding hierarchy of contextual constraints acting at a slower rate than neuronal function itself. This produces a 'biopsychosociocultural' model of individual consciousness that, while otherwise quite close to the standard treatment, meets compelling philosophical and other objections to brain-only descriptions
Neural Network Models of Learning and Memory: Leading Questions and an Emerging Framework
Office of Naval Research and the Defense Advanced Research Projects Agency (N00014-95-1-0409, N00014-1-95-0657); National Institutes of Health (NIH 20-316-4304-5
Introducing Astrocytes on a Neuromorphic Processor: Synchronization, Local Plasticity and Edge of Chaos
While there is still a lot to learn about astrocytes and their
neuromodulatory role in the spatial and temporal integration of neuronal
activity, their introduction to neuromorphic hardware is timely, facilitating
their computational exploration in basic science questions as well as their
exploitation in real-world applications. Here, we present an astrocytic module
that enables the development of a spiking Neuronal-Astrocytic Network (SNAN)
into Intel's Loihi neuromorphic chip. The basis of the Loihi module is an
end-to-end biophysically plausible compartmental model of an astrocyte that
simulates the intracellular activity in response to the synaptic activity in
space and time. To demonstrate the functional role of astrocytes in SNAN, we
describe how an astrocyte may sense and induce activity-dependent neuronal
synchronization, switch on and off spike-time-dependent plasticity (STDP) to
introduce single-shot learning, and monitor the transition between ordered and
chaotic activity at the synaptic space. Our module may serve as an extension
for neuromorphic hardware, by either replicating or exploring the distinct
computational roles that astrocytes have in forming biological intelligence.Comment: 9 pages, 7 figure
Neural Distributed Autoassociative Memories: A Survey
Introduction. Neural network models of autoassociative, distributed memory
allow storage and retrieval of many items (vectors) where the number of stored
items can exceed the vector dimension (the number of neurons in the network).
This opens the possibility of a sublinear time search (in the number of stored
items) for approximate nearest neighbors among vectors of high dimension. The
purpose of this paper is to review models of autoassociative, distributed
memory that can be naturally implemented by neural networks (mainly with local
learning rules and iterative dynamics based on information locally available to
neurons). Scope. The survey is focused mainly on the networks of Hopfield,
Willshaw and Potts, that have connections between pairs of neurons and operate
on sparse binary vectors. We discuss not only autoassociative memory, but also
the generalization properties of these networks. We also consider neural
networks with higher-order connections and networks with a bipartite graph
structure for non-binary data with linear constraints. Conclusions. In
conclusion we discuss the relations to similarity search, advantages and
drawbacks of these techniques, and topics for further research. An interesting
and still not completely resolved question is whether neural autoassociative
memories can search for approximate nearest neighbors faster than other index
structures for similarity search, in particular for the case of very high
dimensional vectors.Comment: 31 page
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The role of HG in the analysis of temporal iteration and interaural correlation
Biologically Inspired Modelling for the Control of Upper Limb Movements: From Concept Studies to Future Applications
Modelling is continuously being deployed to gain knowledge on the mechanisms of motor control. Computational models, simulating the behaviour of complex systems, have often been used in combination with soft computing strategies, thus shifting the rationale of modelling from the description of a behaviour to the understanding of the mechanisms behind it. In this context, computational models are preferred to deterministic schemes because they deal better with complex systems. The literature offers some striking examples of biologically inspired modelling, which perform better than traditional approaches when dealing with both learning and adaptivity mechanisms. Can these theoretical studies be transferred into an application framework? That is, can biologically inspired models be used to implement rehabilitative devices? Some evidences, even if preliminary, are presented here, and support an affirmative answer to the previous question, thus opening new perspectives
The spectro-contextual encoding and retrieval theory of episodic memory.
The spectral fingerprint hypothesis, which posits that different frequencies of oscillations underlie different cognitive operations, provides one account for how interactions between brain regions support perceptual and attentive processes (Siegel etal., 2012). Here, we explore and extend this idea to the domain of human episodic memory encoding and retrieval. Incorporating findings from the synaptic to cognitive levels of organization, we argue that spectrally precise cross-frequency coupling and phase-synchronization promote the formation of hippocampal-neocortical cell assemblies that form the basis for episodic memory. We suggest that both cell assembly firing patterns as well as the global pattern of brain oscillatory activity within hippocampal-neocortical networks represents the contents of a particular memory. Drawing upon the ideas of context reinstatement and multiple trace theory, we argue that memory retrieval is driven by internal and/or external factors which recreate these frequency-specific oscillatory patterns which occur during episodic encoding. These ideas are synthesized into a novel model of episodic memory (the spectro-contextual encoding and retrieval theory, or "SCERT") that provides several testable predictions for future research
On the information in spike timing: neural codes derived from polychronous groups
There is growing evidence regarding the importance of spike timing in neural
information processing, with even a small number of spikes carrying
information, but computational models lag significantly behind those for rate
coding. Experimental evidence on neuronal behavior is consistent with the
dynamical and state dependent behavior provided by recurrent connections. This
motivates the minimalistic abstraction investigated in this paper, aimed at
providing insight into information encoding in spike timing via recurrent
connections. We employ information-theoretic techniques for a simple reservoir
model which encodes input spatiotemporal patterns into a sparse neural code,
translating the polychronous groups introduced by Izhikevich into codewords on
which we can perform standard vector operations. We show that the distance
properties of the code are similar to those for (optimal) random codes. In
particular, the code meets benchmarks associated with both linear
classification and capacity, with the latter scaling exponentially with
reservoir size
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