attractor neural network
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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.
Hippocampal place cells represent different environments with distinct neural activity patterns. Following an abrupt switch between two familiar configurations of visual cues defining two environments, the hippocampal neural activity pattern switches almost immediately to the corresponding representation. Surprisingly, during a transient period following the switch to the new environment, occasional fast transitions between the two activity patterns (flickering) were observed (Jezek, Henriksen, Treves, Moser, & Moser, ). Here we show that an attractor neural network model of place cells with connections endowed with short-term synaptic plasticity can account for this phenomenon. A memory trace of the recent history of network activity is maintained in the state of the synapses, allowing the network to temporarily reactivate the representation of the previous environment in the absence of the corresponding sensory cues. The model predicts that the number of flickering events depends on the amplitude of the ongoing theta rhythm and the distance between the current position of the animal and its position at the time of cue switching. We test these predictions with new analysis of experimental data. These results suggest a potential role of short-term synaptic plasticity in recruiting the activity of different cell assemblies and in shaping hippocampal activity of behaving animals.
- MeSH
- akční potenciály fyziologie MeSH
- časové faktory MeSH
- elektroencefalografie MeSH
- hipokampus cytologie MeSH
- krysa rodu rattus MeSH
- mapování mozku MeSH
- modely neurologické * MeSH
- nervová síť fyziologie MeSH
- neurony fyziologie MeSH
- neuroplasticita fyziologie MeSH
- podněty MeSH
- prostorová paměť fyziologie MeSH
- světelná stimulace MeSH
- theta rytmus EEG fyziologie MeSH
- zvířata MeSH
- Check Tag
- krysa rodu rattus MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
... Self-Organization and Adaptation in Complex Systems, 173 Dynamical Systems and Their Attractors, 175 ... ... Approaches to Studying Families of Mappings of Strings into Strings, 377 Applications to Biological, Neural ... ... Differentiation: The Dynamical Behaviors of Genetic Regulatory Networks, 441 -- Simple Genetic Circuits ... ... Features of Cell Differentiation, 454 The Conceptual Framework: Cell Differentiation in Boolean Networks ... ... Selection for Cell Types, 523 The Framework, 524 Genomic Network Space, 525 Experimental Avenues, 533 ...
1st ed. 709 s. : il.
- Klíčová slova
- Biologie, Evoluce, Fylogeneze,
- MeSH
- biologická evoluce MeSH
- biologie MeSH
- fylogeneze MeSH
- molekulární evoluce MeSH
- původ života MeSH
