Tag Archives: spiking_neuron_model

Persistent memories in transient networks

‘Spatial awareness in mammals is based on an internalized representation of the environment, encoded by large networks of spiking neurons. While such representations can last for a long time, the underlying neuronal network is transient: neuronal cells die every day, … Continue reading

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A Stochastic Approach to STDP (Spike Timing Dependent Plasticity)

‘We present a digital implementation of the Spike Timing Dependent Plasticity (STDP) learning rule. The proposed digital implementation consists of an exponential decay generator array and a STDP adaptor array. On the arrival of a pre- and post-synaptic spike, the … Continue reading

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A Draft Memory Model on Spiking Neural Assemblies

‘A draft memory model (DM) for neural networks with spike propagation delay (SNNwD) is described. Novelty in this approach are that the DM learns immediately, with stimuli presented once, without synaptic weight changes, and without external learning algorithm. Basal on … Continue reading

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Stability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity

‘Abstract Spike-timing dependent plasticity (STDP) is a widespread plasticity mechanism in the nervous system. The simplest description of STDP only takes into account pairs of pre- and postsynaptic spikes, with potentiation of the synapse when a presynaptic spike precedes a … Continue reading

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Training of spiking neural networks based on information theoretic costs

‘Spiking neural network is a type of artificial neural network in which neurons communicate between each other with spikes. Spikes are identical Boolean events characterized by the time of their arrival. A spiking neuron has internal dynamics and responds to … Continue reading

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Optimal Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding

‘Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on … Continue reading

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Magnetic Tunnel Junction Mimics Stochastic Cortical Spiking Neurons

‘Brain-inspired computing architectures attempt to mimic the computations performed in the neurons and the synapses in the human brain in order to achieve its efficiency in learning and cognitive tasks. In this work, we demonstrate the mapping of the probabilistic … Continue reading

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