scholarly journals Computing of temporal information in spiking neural networks with ReRAM synapses

2019 ◽  
Vol 213 ◽  
pp. 453-469 ◽  
Author(s):  
W. Wang ◽  
G. Pedretti ◽  
V. Milo ◽  
R. Carboni ◽  
A. Calderoni ◽  
...  
Keyword(s):  
Neural Networks ◽  
Spike Timing ◽  
Temporal Information ◽  
Spiking Neural Networks ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Neural Spikes

This work addresses the methodology and implementation of a neuromorphic SNN system to compute the temporal information among neural spikes using ReRAM synapses capable of spike-timing dependent plasticity (STDP).

Soft-Reward Based Reinforcement Learning by Spiking Neural Networks

2011 ◽  
Vol 219-220 ◽  
pp. 770-773
Author(s):  
Wei Ya Shi
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Synaptic Weight ◽  
Spike Timing ◽  
Benchmark Dataset ◽  
Spiking Neural Networks ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Winner Take All ◽  
Take All

In this paper, we propose algorithm based reinforcement learning for spiking neural networks. The algorithm simulates biological adaptability and uses the soft-reward from environment to modulate the synaptic weight, which combines spike-timing-dependent plasticity (STDP), winner-take-all mechanism. The algorithm is tested to classify a number of standard benchmark dataset. The obtained results show the effectiveness of the proposed algorithm.

scholarly journals Pattern Classification by Spiking Neural Networks Combining Self-Organized and Reward-Related Spike-Timing-Dependent Plasticity

2019 ◽  
Vol 9 (4) ◽  
pp. 283-291 ◽  
Author(s):  
Sou Nobukawa ◽  
Haruhiko Nishimura ◽  
Teruya Yamanishi
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Supervised Learning ◽  
Pattern Classification ◽  
Spike Timing ◽  
Spiking Neural Networks ◽  
Learning Problems ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Self Organized

Abstract Many recent studies have applied to spike neural networks with spike-timing-dependent plasticity (STDP) to machine learning problems. The learning abilities of dopamine-modulated STDP (DA-STDP) for reward-related synaptic plasticity have also been gathering attention. Following these studies, we hypothesize that a network structure combining self-organized STDP and reward-related DA-STDP can solve the machine learning problem of pattern classification. Therefore, we studied the ability of a network in which recurrent spiking neural networks are combined with STDP for non-supervised learning, with an output layer joined by DA-STDP for supervised learning, to perform pattern classification. We confirmed that this network could perform pattern classification using the STDP effect for emphasizing features of the input spike pattern and DA-STDP supervised learning. Therefore, our proposed spiking neural network may prove to be a useful approach for machine learning problems.

scholarly journals Neurodegeneration exposes firing rate dependent effects on oscillation dynamics in computational neural networks

2019 ◽  
Author(s):  
D. Gabrieli ◽  
Samantha N. Schumm ◽  
B. Parvesse ◽  
D.F. Meaney
Keyword(s):  
Neural Networks ◽  
Firing Rate ◽  
Spike Timing ◽  
Network Activity ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Inhibitory Circuitry ◽  
Excitatory Neurons ◽  
Oscillation Dynamics ◽  
The Impact

AbstractTraumatic brain injury (TBI) can lead to neurodegeneration in the injured circuitry, either through primary structural damage to the neuron or secondary effects that disrupt key cellular processes. Moreover, traumatic injuries can preferentially impact subpopulations of neurons, but the functional network effects of these targeted degeneration profiles remain unclear. Although isolating the consequences of complex injury dynamics and long-term recovery of the circuit can be difficult to control experimentally, computational networks can be a powerful tool to analyze the consequences of injury. Here, we use the Izhikevich spiking neuron model to create networks representative of cortical tissue. After an initial settling period with spike-timing-dependent plasticity (STDP), networks developed rhythmic oscillations similar to those seenin vivo. As neurons were sequentially removed from the network, population activity rate and oscillation dynamics were significantly reduced. In a successive period of network restructuring with STDP, network activity levels were returned to baseline for some injury levels and oscillation dynamics significantly improved. We next explored the role that specific neurons have in the creation and termination of oscillation dynamics. We determined that oscillations initiate from activation of low firing rate neurons with limited structural inputs. To terminate oscillations, high activity excitatory neurons with strong input connectivity activate downstream inhibitory circuitry. Finally, we confirm the excitatory neuron population role through targeted neurodegeneration. These results suggest targeted neurodegeneration can play a key role in the oscillation dynamics after injury.Author SummaryIn this study, we study the impact of neuronal degeneration – a process that commonly occurs after traumatic injury and neurodegenerative disease – on the neuronal dynamics in a cortical network. We create computational models of neural networks and include spike timing plasticity to alter the synaptic strength among connections as networks remodel after simulated injury. We find that spike-timing dependent plasticity helps recover the neural dynamics of an injured microcircuit, but it frequently cannot recover the original oscillation dynamics in an uninjured network. In addition, we find that selectively injuring excitatory neurons with the highest firing rate reduced the neuronal oscillations in a circuit much more than either random deletion or the removing neurons with the lowest firing rate. In all, these data suggest (a) plasticity reduces the consequences of neurodegeneration and (b) losing the most active neurons in the network has the most adverse effect on neural oscillations.

Spike-Timing Dependent Plasticity for Evolved Robots

2002 ◽  
Vol 10 (3-4) ◽  
pp. 243-263 ◽  
Author(s):  
Ezequiel Di Paolo
Keyword(s):  
Neural Networks ◽  
Synaptic Plasticity ◽  
Spike Trains ◽  
Spike Timing ◽  
Behavioral Strategies ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Neural Noise ◽  
Functional Value ◽  
Millisecond Range

Plastic spiking neural networks are synthesized for phototactic robots using evolutionary techniques. Synaptic plasticity asymmetrically depends on the precise relative timing between presynaptic and postsynaptic spikes at the millisecond range and on longer-term activity-dependent regulatory scaling. Comparative studies have been carried out for different kinds of plastic neural networks with low and high levels of neural noise. In all cases, the evolved controllers are highly robust against internal synaptic decay and other perturbations. The importance of the precise timing of spikes is demonstrated by randomizing the spike trains. In the low neural noise scenario, weight values undergo rhythmic changes at the mesoscale due to bursting, but during periods of high activity they are finely regulated at the microscale by synchronous or entrained firing. Spike train randomization results in loss of performance in this case. In contrast, in the high neural noise scenario, robots are robust to loss of information in the timing of the spike trains, demonstrating the counterintuitive results that plasticity, which is dependent on precise spike timing, can work even in its absence, provided the behavioral strategies make use of robust longer-term invariants of sensorimotor interaction. A comparison with a rate-based model of synaptic plasticity shows that under similarly noisy conditions, asymmetric spike-timing dependent plasticity achieves better performance by means of efficient reduction in weight variance over time. Performance also presents negative sensitivity to reduced levels of noise, showing that random firing has a functional value.

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