Introduction to the theory of neural computation
Introduction to the theory of neural computation
Learning in neural networks with material synapses
Neural Computation
Shape quantization and recognition with randomized trees
Neural Computation
A pulse-coded communications infrastructure for neuromorphic systems
Pulsed neural networks
Communicating neuronal ensembles between neuromorphic chips
Neuromorphic systems engineering
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spike-driven synaptic dynamics generating working memory states
Neural Computation
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?
Neural Computation
Minimal Models of Adapted Neuronal Response to In Vivo–lLike Input Currents
Neural Computation
Attractor Networks for Shape Recognition
Neural Computation
Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning
Neural Computation
Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning
Neural Computation
Modeling Selective Attention Using a Neuromorphic Analog VLSI Device
Neural Computation
Spike-Driven Synaptic Plasticity: Theory, Simulation, VLSI Implementation
Neural Computation
A neuromorphic VLSI device for implementing 2D selective attention systems
IEEE Transactions on Neural Networks
SpikeStream: a fast and flexible simulator of spiking neural networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
SWAT: a spiking neural network training algorithm for classification problems
IEEE Transactions on Neural Networks
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
The rise and fall of memory in a model of synaptic integration
Neural Computation
Evaluating SPAN incremental learning for handwritten digit recognition
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
A spike-timing-based integrated model for pattern recognition
Neural Computation
Nanoscale electronic synapses using phase change devices
ACM Journal on Emerging Technologies in Computing Systems (JETC) - Special issue on memory technologies
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We present a model of spike-driven synaptic plasticity inspired by experimental observations and motivated by the desire to build an electronic hardware device that can learn to classify complex stimuli in a semisupervised fashion. During training, patterns of activity are sequentially imposed on the input neurons, and an additional instructor signal drives the output neurons toward the desired activity. The network is made of integrate-and-fire neurons with constant leak and a floor. The synapses are bistable, and they are modified by the arrival of presynaptic spikes. The sign of the change is determined by both the depolarization and the state of a variable that integrates the postsynaptic action potentials. Following the training phase, the instructor signal is removed, and the output neurons are driven purely by the activity of the input neurons weighted by the plastic synapses. In the absence of stimulation, the synapses preserve their internal state indefinitely. Memories are also very robust to the disruptive action of spontaneous activity. A network of 2000 input neurons is shown to be able to classify correctly a large number (thousands) of highly overlapping patterns (300 classes of preprocessed Latex characters, 30 patterns per class, and a subset of the NIST characters data set) and to generalize with performances that are better than or comparable to those of artificial neural networks. Finally we show that the synaptic dynamics is compatible with many of the experimental observations on the induction of long-term modifications (spike-timing-dependent plasticity and its dependence on both the postsynaptic depolarization and the frequency of pre-and postsynaptic neurons).