A spiking neural network model of the medial superior olive using spike timing dependent plasticity for sound localization

Glackin,

doi:10.3389/fncom.2010.00018

Cited by 29 publications

(27 citation statements)

References 77 publications

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“…Delays of spike propagation are an important characteristic of real biological neural systems, and they have a significant effect on the information processing ability of the nervous system [18], [41], [42]. In extended delay learning (EDL) based ReSuMe [43], for SNs, and in DL-ReSuMe [41], a delay learning-based remote supervised method for SNs, investigated the viability of adjusting the neuron synaptic weights and delays for training a single SN to map a given spatiotemporal input pattern into a desired output spike train.…”

Section: Background and Related Workmentioning

confidence: 99%

A Supervised Learning Algorithm for Learning Precise Timing of Multiple Spikes in Multilayer Spiking Neural Networks

Taherkhani

Belatreche

et al. 2018

IEEE Trans. Neural Netw. Learning Syst.

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There is a biological evidence to prove information is coded through precise timing of spikes in the brain. However, training a population of spiking neurons in a multilayer network to fire at multiple precise times remains a challenging task. Delay learning and the effect of a delay on weight learning in a spiking neural network (SNN) have not been investigated thoroughly. This paper proposes a novel biologically plausible supervised learning algorithm for learning precisely timed multiple spikes in a multilayer SNNs. Based on the spike-timing-dependent plasticity learning rule, the proposed learning method trains an SNN through the synergy between weight and delay learning. The weights of the hidden and output neurons are adjusted in parallel. The proposed learning method captures the contribution of synaptic delays to the learning of synaptic weights. Interaction between different layers of the network is realized through biofeedback signals sent by the output neurons. The trained SNN is used for the classification of spatiotemporal input patterns. The proposed learning method also trains the spiking network not to fire spikes at undesired times which contribute to misclassification. Experimental evaluation on benchmark data sets from the UCI machine learning repository shows that the proposed method has comparable results with classical rate-based methods such as deep belief network and the autoencoder models. Moreover, the proposed method can achieve higher classification accuracies than single layer and a similar multilayer SNN.

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Section: Background and Related Workmentioning

confidence: 99%

A Supervised Learning Algorithm for Learning Precise Timing of Multiple Spikes in Multilayer Spiking Neural Networks

Taherkhani

Belatreche

et al. 2018

IEEE Trans. Neural Netw. Learning Syst.

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“…The effect of the time delay on the processing ability of the nervous system has been studied in [32]- [34]. There is biological evidence that the synaptic delay is not always invariant, but it can be modulated [35], [36].…”

Section: Background and Related Workmentioning

confidence: 99%

DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons

Taherkhani

Belatreche

et al. 2015

IEEE Trans. Neural Netw. Learning Syst.

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Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.

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“…A standard procedure for experimenting with SNN models typically begins with an extensive, initial parameter-space exploration using small-or medium-sized networks. Having fine-tuned all model parameters, real experiments can then commence by simulating either small-to medium-sized networks (10s to 100s of cells) for exploring real-time, closedloop control such as Brain-Computer Interfaces [10] (TYPE 1), or large-scale networks (>1000s of cells) for mounting behavioral experiments [27] (TYPE 2). To tackle both experiment types, in this section we evaluate a range of 96-to 1,056-cell networks as well as a range of 960-to 7,680-cell networks.…”

Section: Performance Evaluationmentioning

confidence: 99%

Performance analysis of accelerated biophysically-meaningful neuron simulations

Smaragdos¹,

Chatzikostantis²,

Nomikou³

et al. 2016

2016 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)

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In-vivo and in-vitro experiments are routinely used in neuroscience to unravel brain functionality. Although they are a powerful experimentation tool, they are also time-consuming and, often, restrictive. Computational neuroscience attempts to solve this by using biologically-plausible and biophysicallymeaningful neuron models, most prominent among which are the conductance-based models. Their computational complexity calls for accelerator-based computing to mount large-scale or realtime neuroscientific experiments. In this paper, we analyze and draw conclusions on the class of conductance models by using a representative modeling application of the inferior olive (InfOli), an important part of the olivocerebellar brain circuit. We conduct an extensive profiling session to identify the computational and data-transfer requirements of the application under various realistic use cases. The application is, then, ported onto two acceleration nodes, an Intel Xeon Phi and a Maxeler Vectis Data Flow Engine (DFE). We evaluate the performance scalability and resource requirements of the InfOli application on the two target platforms. The analysis of InfOli, which is a real-life neuroscientific application, can serve as a useful guide for porting a wide range of similar workloads on platforms like the Xeon Phi or the Maxeler DFEs. As accelerators are increasingly populating High-Performance Computing (HPC) infrastructure, the current paper provides useful insight on how to optimally use such nodes to run complex and relevant neuron modeling workloads.

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A spiking neural network model of the medial superior olive using spike timing dependent plasticity for sound localization

Cited by 29 publications

References 77 publications

A Supervised Learning Algorithm for Learning Precise Timing of Multiple Spikes in Multilayer Spiking Neural Networks

A Supervised Learning Algorithm for Learning Precise Timing of Multiple Spikes in Multilayer Spiking Neural Networks

DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons

Performance analysis of accelerated biophysically-meaningful neuron simulations

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