Cargando…
Topological dynamics in spike-timing dependent plastic model neural networks
Spike-timing dependent plasticity (STDP) is a biologically constrained unsupervised form of learning that potentiates or depresses synaptic connections based on the precise timing of pre-synaptic and post-synaptic firings. The effects of on-going STDP on the topology of evolving model neural network...
Autores principales: | Stone, David B., Tesche, Claudia D. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3629334/ https://www.ncbi.nlm.nih.gov/pubmed/23616750 http://dx.doi.org/10.3389/fncir.2013.00070 |
Ejemplares similares
-
Characterization of Generalizability of Spike Timing Dependent Plasticity Trained Spiking Neural Networks
por: Chakraborty, Biswadeep, et al.
Publicado: (2021) -
SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training
por: Liu, Fangxin, et al.
Publicado: (2021) -
A Spiking Neural Network Model of the Medial Superior Olive Using Spike Timing Dependent Plasticity for Sound Localization
por: Glackin, Brendan, et al.
Publicado: (2010) -
SpikePropamine: Differentiable Plasticity in Spiking Neural Networks
por: Schmidgall, Samuel, et al.
Publicado: (2021) -
Triphasic spike-timing-dependent plasticity organizes networks to produce robust sequences of neural activity
por: Waddington, Amelia, et al.
Publicado: (2012)