In conclusion, this study has revealed the developmental profile and NMDA receptor subunit requirement of a timing-dependent form of synaptic depression at layer 4-to-layer 2/3 synapses.
Consequently, we observed a timing-dependent action of NGF on cell differentiation, and discontinuous NGF stimulation similarly induced differentiation.
In contrast, a timing-dependent form of potentiation at layer 4-to-layer 2/3 synapses was seen in the second postnatal week, persisted into adulthood, and was sensitive to GluN2A subunit-preferring antagonists.
Phase advancing or slowing of one element had less of an effect on tremor amplitude (and, possibly less of an effect on spike timing dependent plasticity, if this underlies cumulative effects).
A recent study suggests that functional connections, at least to a certain extend, describe anatomical connectivity (the set of physical or structural (synaptic) connections linking neuronal units at a given time) because they follow the rules of spike timing dependent plasticity [18].
Combined with the discovery of spike timing dependent plasticity, these findings have sparked growing interest in the capabilities of neurons to encode and decode spike timing based neural representations.
In this paper, a VHDL-AMS based TSTDP model is presented which exhibits the behavioral model of triplet-based spike timing dependent plasticity.
[33] found that homeostasis needs to have a faster rate of change for spike-timing dependent plasticity to achieve stability.
Then, the application of RRAM in neuromorphic computing are addressed, presenting hybrid synapses capable of spike-timing dependent plasticity (STDP).
Spike-based learning models for temporally structured input need to take this timing information into account (e.g. Gerstner et al. 1993) which leads to models of spike-timing dependent plasticity (STDP) (Gerstner et al. 1996; Kempter et al. 1999; Roberts 1999; Abbott and Nelson 2000) that can be seen as a spike-based generalization of Hebbian learning.
The model consists of Izhikevich neurons and conductance-based synapses for realistic approximation of neuronal dynamics, a spike-timing-dependent plasticity (STDP) synaptic learning rule with additional synaptic dynamics for memory encoding, and an accumulator model for memory retrieval and categorization.
