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Neuronal plasticity and its applications to advanced network computations

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One of the main active research areas in neuroscience over the past several decades is synaptic plasticity. This phenomenon, with its various timescales, is assumed to play a major role in learning and memory. Besides the time dependent features of the synapse conducting signals between neurons, the neuron ...

One of the main active research areas in neuroscience over the past several decades is synaptic plasticity. This phenomenon, with its various timescales, is assumed to play a major role in learning and memory. Besides the time dependent features of the synapse conducting signals between neurons, the neuron itself also shows time dependent features as a function of its inputs. This can be defined as neuronal plasticity. One of the most striking features of neuronal plasticity is the neuronal response latency to ongoing stimulations, measuring the time-lag from the beginning of a stimulation to its corresponding evoked spike. This phenomenon on a single neuron level is well investigated, however its possible implications on a neuronal circuit and network level is unclear and is at the center of this research topic. On a network level, synaptic plasticity and neuronal plasticity are expected to have different effects. Synaptic plasticity affects the transmission of a signal through a link, a synapse, connecting two nodes, neurons, in the network. In contrast, neuronal plasticity affects the internal dynamics of a node, neuron, in the network. Consequently, synaptic plasticity affects all transmission of information passing through a link, whereas neuronal plasticity affects all information routes passing through a node. These two phenomena have different impacts on the information flow in a network, especially when a node functions as a hub with many connections. Hence, synaptic plasticity and neuronal plasticity may have different computational implications on normal and abnormal brain functionality. Suggested topics include, but are not limited to:
1. Neuronal plasticity in the form of neuronal response latency, and its underlying mechanisms.
2. The interplay between synaptic and neuronal plasticity on the circuit level.
3. Neuronal circuits and motifs engineered to perform advanced computational tasks.
4. The role of neuronal plasticity in encoding and decoding rate and temporal codes.
5. The role of neuronal plasticity in achieving different types of synchronized modes, e.g. the mechanisms underlying epochs of synchrony.


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