Self-organizing neural projections

Kohonen, Teuvo

doi:10.1016/j.neunet.2006.05.001

Cited by 107 publications

(65 citation statements)

References 24 publications

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“…A modification of the signal-transfer law has also an automatic standardization effect on the synaptic strengths ordering, called "shunting" (Grossberg, 1976, Kohonen, 2006.…”

Section: Discussionmentioning

confidence: 99%

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Algorithmic Self-Instructing Consciousness

Skopec¹

2016

Neuroquantology

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Let we are outgoing from the thesis: if consciousness corresponds to the capacity to integrate information, then the system should be able to generate consciousness to the extent having a large repertoire of available states (information). Natural selection is an algorithm for generating adaptation and the question is, whether it may be utilized for cognition. Natural selection is capable to improve itself as a heuristic search algorithm. In neuronal information self-transfer is possible formation of a one-to-one topographic map between two neuronal layers, and reconstruction of the intra-layer topology of the parent in the offspring layer. The problem of neuronal transfer exists, from anatomical (activity-dependent) mechanisms, to self-instructing (activity-independent) algorithms. We establish a link between network topology and information integration showing how biologically inspired auto-adaptation improves the consciousness self-instructing.

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“…A modification of the signal-transfer law has also an automatic standardization effect on the synaptic strengths ordering, called "shunting" (Grossberg, 1976, Kohonen, 2006.…”

Section: Discussionmentioning

confidence: 99%

“…In a similar sense as the neighborhood function was defined in the earlier biological SOM algorithms, a laterally spreading plasticity-control agent (possibly chemical) is assumed (Kohonen, 2006).…”

Section: Information Integration In the Brainmentioning

confidence: 99%

Algorithmic Self-Instructing Consciousness

Skopec¹

2016

Neuroquantology

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“…The ANN architecture to be employed in this study is known as a Self-Organizing Feature Map (SOFM) network (Kohonen 2006). The CloudSat radar images show distinguishable features of different cloud types.…”

Section: Image Classification Using Self Organizing Mapsmentioning

confidence: 99%

Cloud Classification and its Application in Reducing False Rain

Nasrollahi

2014

Springer Theses

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“…2). This step models the influence of lateral connections across the cortical sheet (Kohonen, 2006;Sullivan and de Sa, 2006) and gives the final neural activation. To accomplish this, the neuron with the maximum feedforward excitation is found.…”

Section: V1mentioning

confidence: 99%

Sleeping Our Way to Weight Normalization and Stable Learning

Sullivan

2008

Neural Computation

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The functions of sleep have been an enduring mystery. Recently, Tononi and Cirelli hypothesized that one of the functions of slow-wave sleep is to scale down synapses in the cortex that have strengthened during awake learning (Tononi and Cirelli, 2003).We create a computational model to test the functionality of this idea and examine some of its implications. We show that synaptic scaling during slow-wave sleep is capable of keeping Hebbian learning in check and that it enables stable development.We also show theoretically how it implements classical weight normalization, which has been in common use in neural models for decades. Finally, a significant computational limitation of this form of synaptic scaling is revealed through computer simulations.

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Self-organizing neural projections

Cited by 107 publications

References 24 publications

Algorithmic Self-Instructing Consciousness

Algorithmic Self-Instructing Consciousness

Cloud Classification and its Application in Reducing False Rain

Sleeping Our Way to Weight Normalization and Stable Learning

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