Neural computation
Dotaz
Zobrazit nápovědu
sv.
- MeSH
- neuronové sítě MeSH
- Publikační typ
- periodika MeSH
- Konspekt
- Automatizační a řídicí technika
- NLK Obory
- neurovědy
- technika
sv.
- MeSH
- lékařská informatika MeSH
- neuronové sítě MeSH
- Publikační typ
- periodika MeSH
- Konspekt
- Lékařské vědy. Lékařství
- NLK Obory
- lékařská informatika
elektronický časopis
- MeSH
- neuronové sítě MeSH
- Konspekt
- Patologie. Klinická medicína
- NLK Obory
- neurovědy
- neurologie
- NLK Publikační typ
- elektronické časopisy
In neural computation, the essential information is generally encoded into the neurons via their spiking configurations, activation values or (attractor) dynamics. The synapses and their associated plasticity mechanisms are, by contrast, mainly used to process this information and implement the crucial learning features. Here, we propose a novel Turing complete paradigm of neural computation where the essential information is encoded into discrete synaptic states, and the updating of this information achieved via synaptic plasticity mechanisms. More specifically, we prove that any 2-counter machine-and hence any Turing machine-can be simulated by a rational-weighted recurrent neural network employing spike-timing-dependent plasticity (STDP) rules. The computational states and counter values of the machine are encoded into discrete synaptic strengths. The transitions between those synaptic weights are then achieved via STDP. These considerations show that a Turing complete synaptic-based paradigm of neural computation is theoretically possible and potentially exploitable. They support the idea that synapses are not only crucially involved in information processing and learning features, but also in the encoding of essential information. This approach represents a paradigm shift in the field of neural computation.
Synfire rings are neural circuits capable of conveying synchronous, temporally precise and self-sustained activities in a robust manner. We propose a cell assembly based paradigm for abstract neural computation centered on the concept of synfire rings. More precisely, we empirically show that Hodgkin-Huxley neural networks modularly composed of synfire rings are automata complete. We provide an algorithmic construction which, starting from any given finite state automaton, builds a corresponding Hodgkin-Huxley neural network modularly composed of synfire rings and capable of simulating it. We illustrate the correctness of the construction on two specific examples. We further analyze the stability and robustness of the construction as a function of changes in the ring topologies as well as with respect to cell death and synaptic failure mechanisms, respectively. These results establish the possibility of achieving abstract computation with bio-inspired neural networks. They might constitute a theoretical ground for the realization of biological neural computers.
- MeSH
- akční potenciály fyziologie MeSH
- lidé MeSH
- modely neurologické * MeSH
- neuronové sítě * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
1 elektronický disk (disketa) : barev. ; 11 cm
1st ed. 482 s.
- MeSH
- neuronové sítě MeSH
- Konspekt
- Knihovnictví. Informatika
- NLK Obory
- knihovnictví, informační věda a muzeologie
elektronický časopis
- Konspekt
- Fyziologie člověka a srovnávací fyziologie
- NLK Obory
- neurovědy
- technika
- NLK Publikační typ
- elektronické časopisy
^^^sv. ; 24 cm
- MeSH
- umělá inteligence MeSH
- Publikační typ
- periodika MeSH
- Konspekt
- Umělá inteligence
- NLK Obory
- lékařská informatika
