dorsal/arxiv
View SchemaAutonomous Dynamics in Neural networks: The dHAN Concept and Associative Thought Processes
| Authors | Claudius Gros |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0703002 |
| URL | https://arxiv.org/abs/q-bio/0703002 |
| DOI | 10.1063/1.2709594 |
| Journal | Ninth Granada Lectures, AIP Conference Proceedings, Vol. 887, 129 (2007) |
Abstract
The neural activity of the human brain is dominated by self-sustained activities. External sensory stimuli influence this autonomous activity but they do not drive the brain directly. Most standard artificial neural network models are however input driven and do not show spontaneous activities. It constitutes a challenge to develop organizational principles for controlled, self-sustained activity in artificial neural networks. Here we propose and examine the dHAN concept for autonomous associative thought processes in dense and homogeneous associative networks. An associative thought-process is characterized, within this approach, by a time-series of transient attractors. Each transient state corresponds to a stored information, a memory. The subsequent transient states are characterized by large associative overlaps, which are identical to acquired patterns. Memory states, the acquired patterns, have such a dual functionality. In this approach the self-sustained neural activity has a central functional role. The network acquires a discrimination capability, as external stimuli need to compete with the autonomous activity. Noise in the input is readily filtered-out. Hebbian learning of external patterns occurs coinstantaneous with the ongoing associative thought process. The autonomous dynamics needs a long-term working-point optimization which acquires within the dHAN concept a dual functionality: It stabilizes the time development of the associative thought process and limits runaway synaptic growth, which generically occurs otherwise in neural networks with self-induced activities and Hebbian-type learning rules.
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"abstract": "The neural activity of the human brain is dominated by self-sustained\nactivities. External sensory stimuli influence this autonomous activity but\nthey do not drive the brain directly. Most standard artificial neural network\nmodels are however input driven and do not show spontaneous activities.\n It constitutes a challenge to develop organizational principles for\ncontrolled, self-sustained activity in artificial neural networks. Here we\npropose and examine the dHAN concept for autonomous associative thought\nprocesses in dense and homogeneous associative networks. An associative\nthought-process is characterized, within this approach, by a time-series of\ntransient attractors. Each transient state corresponds to a stored information,\na memory. The subsequent transient states are characterized by large\nassociative overlaps, which are identical to acquired patterns. Memory states,\nthe acquired patterns, have such a dual functionality.\n In this approach the self-sustained neural activity has a central functional\nrole. The network acquires a discrimination capability, as external stimuli\nneed to compete with the autonomous activity. Noise in the input is readily\nfiltered-out.\n Hebbian learning of external patterns occurs coinstantaneous with the ongoing\nassociative thought process. The autonomous dynamics needs a long-term\nworking-point optimization which acquires within the dHAN concept a dual\nfunctionality: It stabilizes the time development of the associative thought\nprocess and limits runaway synaptic growth, which generically occurs otherwise\nin neural networks with self-induced activities and Hebbian-type learning\nrules.",
"arxiv_id": "q-bio/0703002",
"authors": [
"Claudius Gros"
],
"categories": [
"q-bio.NC",
"cond-mat.dis-nn"
],
"doi": "10.1063/1.2709594",
"journal_ref": "Ninth Granada Lectures, AIP Conference Proceedings, Vol. 887, 129\n (2007)",
"title": "Autonomous Dynamics in Neural networks: The dHAN Concept and Associative Thought Processes",
"url": "https://arxiv.org/abs/q-bio/0703002"
},
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