- Poster presentation
- Open Access
Modeling the interplay between structural plasticity and spike-timing-dependent plasticity
© George et al. 2015
- Published: 18 December 2015
- Neural Network
- Synaptic Weight
- Neural Network Architecture
- Structural Plasticity
- Dedicated Hardware
Structural Plasticity describes a form of long-term plasticity, in which the pruning and the creation of synapses lead to the formation of memories in the topology of a network of neurons. In contrast, classical learning rules such as spike-timing dependent plasticity (STDP) focus on changing the efficacy of synapses, for example by looking at the correlation of pre-and post-synaptic activity in spiking neural networks. Typically, prolonged correlated activity leads to a long-term potentiation of the synaptic weight, while anti-correlated activity depresses the weight.
A major advantage of structural plasticity in artificial neural networks is given by the fact that it allows a drastic increase in performance given a finite number of synaptic resources. In addition to offering a promising approach for optimizing performance in software simulated networks, the model we propose optimizes the usage of resources in dedicated hardware neural network implementations that are faced with limited resources for emulating or simulating synaptic connections. This is particularly relevant for electronic implementations of spiking neural networks, ranging from GPU-based systems to mixed signal analog-digital neuromorphic VLSI devices.
This work is supported by European Union Seventh Framework Program (FP7/2007-2013) under grant agreement no.612058 (RAMP) and the SNF grant 200021-143337.
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