A spiking neural network model of an actor-critic learning agent
Neural Computation
Coexistence of Cell Assemblies and STDP
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Regenerative systems: challenges and opportunities for modeling, simulation, and visualization
Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools
BCM and membrane potential: alternative ways to timing dependent plasticity
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Domain-specific modeling as a pragmatic approach to neuronal model descriptions
BI'10 Proceedings of the 2010 international conference on Brain informatics
Simple constraints for zero-lag synchronous oscillations under STDP
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
A Hebbian-based reinforcement learning framework for spike-timing-dependent synapses
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Vectorized algorithms for spiking neural network simulation
Neural Computation
Spiking neurons that keep the rhythm
Journal of Computational Neuroscience
A reafferent and feed-forward model of song syntax generation in the Bengalese finch
Journal of Computational Neuroscience
Learning rule of homeostatic synaptic scaling: Presynaptic dependent or not
Neural Computation
Stochastic perturbation methods for spike-timing-dependent plasticity
Neural Computation
Journal of Computational Neuroscience
Frequency selectivity emerging from spike-timing-dependent plasticity
Neural Computation
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Spike-timing-dependent construction
Neural Computation
Hi-index | 0.00 |
Synaptic plasticity is considered to be the biological substrate of learning and memory. In this document we review phenomenological models of short-term and long-term synaptic plasticity, in particular spike-timing dependent plasticity (STDP). The aim of the document is to provide a framework for classifying and evaluating different models of plasticity. We focus on phenomenological synaptic models that are compatible with integrate-and-fire type neuron models where each neuron is described by a small number of variables. This implies that synaptic update rules for short-term or long-term plasticity can only depend on spike timing and, potentially, on membrane potential, as well as on the value of the synaptic weight, or on low-pass filtered (temporally averaged) versions of the above variables. We examine the ability of the models to account for experimental data and to fulfill expectations derived from theoretical considerations. We further discuss their relations to teacher-based rules (supervised learning) and reward-based rules (reinforcement learning). All models discussed in this paper are suitable for large-scale network simulations.