4,977 research outputs found
Biological Principles in Self-Organization of Young Brain - Viewed from Kohonen Model
Variants of the Kohonen model are proposed to study biological principles of
self-organization in a model of young brain. We suggest a function to measure
aquired knowledge and use it to auto-adapt the topology of neuronal
connectivity, yielding substantial organizational improvement relative to the
standard model. In the early phase of organization with most intense learning,
we observe that neural connectivity is of Small World type, which is very
efficient to organize neurons in response to stimuli. In analogy to human brain
where pruning of neural connectivity (and neuron cell death) occurs in early
life, this feature is present also in our model, which is found to stabilize
neuronal response to stimuli
Short-Term Memory Through Persistent Activity: Evolution of Self-Stopping and Self-Sustaining Activity in Spiking Neural Networks
Memories in the brain are separated in two categories: short-term and
long-term memories. Long-term memories remain for a lifetime, while short-term
ones exist from a few milliseconds to a few minutes. Within short-term memory
studies, there is debate about what neural structure could implement it.
Indeed, mechanisms responsible for long-term memories appear inadequate for the
task. Instead, it has been proposed that short-term memories could be sustained
by the persistent activity of a group of neurons. In this work, we explore what
topology could sustain short-term memories, not by designing a model from
specific hypotheses, but through Darwinian evolution in order to obtain new
insights into its implementation. We evolved 10 networks capable of retaining
information for a fixed duration between 2 and 11s. Our main finding has been
that the evolution naturally created two functional modules in the network: one
which sustains the information containing primarily excitatory neurons, while
the other, which is responsible for forgetting, was composed mainly of
inhibitory neurons. This demonstrates how the balance between inhibition and
excitation plays an important role in cognition.Comment: 28 page
Generation of Explicit Knowledge from Empirical Data through Pruning of Trainable Neural Networks
This paper presents a generalized technology of extraction of explicit
knowledge from data. The main ideas are 1) maximal reduction of network
complexity (not only removal of neurons or synapses, but removal all the
unnecessary elements and signals and reduction of the complexity of elements),
2) using of adjustable and flexible pruning process (the pruning sequence
shouldn't be predetermined - the user should have a possibility to prune
network on his own way in order to achieve a desired network structure for the
purpose of extraction of rules of desired type and form), and 3) extraction of
rules not in predetermined but any desired form. Some considerations and notes
about network architecture and training process and applicability of currently
developed pruning techniques and rule extraction algorithms are discussed. This
technology, being developed by us for more than 10 years, allowed us to create
dozens of knowledge-based expert systems. In this paper we present a
generalized three-step technology of extraction of explicit knowledge from
empirical data.Comment: 9 pages, The talk was given at the IJCNN '99 (Washington DC, July
1999
The brainstem reticular formation is a small-world, not scale-free, network
Recently, it has been demonstrated that several complex systems may have simple graph-theoretic characterizations as so-called ‘small-world’ and ‘scale-free’ networks. These networks have also been applied to the gross neural connectivity between primate cortical areas and the nervous system of Caenorhabditis elegans. Here, we extend this work to a specific neural circuit of the vertebrate brain—the medial reticular formation (RF) of the brainstem—and, in doing so, we have made three key contributions. First, this work constitutes the first model (and quantitative review) of this important brain structure for over three decades. Second, we have developed the first graph-theoretic analysis of vertebrate brain connectivity at the neural network level. Third, we propose simple metrics to quantitatively assess the extent to which the networks studied are small-world or scale-free. We conclude that the medial RF is configured to create small-world (implying coherent rapid-processing capabilities), but not scale-free, type networks under assumptions which are amenable to quantitative measurement
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