Spike-timing-dependent synaptic plasticity: from single spikes to spike trains

Panchev, Christo

doi:10.1016/s0925-2312(04)00073-6

Cited by 7 publications

(7 citation statements)

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“…Neurological research also shows that the biological neurons store information in the timing of spikes and in the synapses. There have been many studies in the past using spiking neuron models to solve different problems for example spatial and temporal pattern analysis [2], instructing a robot for navigation and grasping tasks [3][4][5], character recognition [6,7], and learning visual features [8]. There are also biologically realistic networks or brain simulators ranking medium to high in biological plausibility such as [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Hebbian learning with winner take all for spiking neural networks

Gupta

Long

2009

2009 International Joint Conference on Neural Networks

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Learning methods for spiking neural networks are not as well developed as the traditional rate based networks, which widely use the back-propagation learning algorithm. We propose and implement an efficient Hebbian learning method with homeostasis for a network of spiking neurons. Similar to STDP, timing between spikes is used for synaptic modification. Homeostasis ensures that the synaptic weights are bounded and the learning is stable. The winner take all mechanism is also implemented to promote competitive learning among output neurons. We have implemented this method in a C++ object oriented code (called CSpike). We have tested the code on four images of Gabor filters and found bell-shaped tuning curves using 36 test set images of Gabor filters in different orientations. These bell-shapes curves are similar to those experimentally observed in the VI and MTN5 area of the mammalian brain.

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Section: Introductionmentioning

confidence: 99%

Hebbian learning with winner take all for spiking neural networks

Gupta

Long

2009

2009 International Joint Conference on Neural Networks

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“…Although the spatiotemporal firing pattern propagating toward the two directions might contribute to encoding auditory information, presumably in a more precise manner, we modeled the propagation only along the isofrequency bands. Panchev and Wermter (2004) proposed a neural network model that can detect temporal sequences in timescale from hundreds of milliseconds to several seconds. The network consisted of integrate-and-fire neurons with active dendrites and dynamic synapses.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Conversion of Auditory Information for Cochleotopic Mapping

Hoshino

2007

Neural Computation

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Auditory communication signals such as monkey calls are complex FM vocal sounds and in general induce action potentials in different timing in the primary auditory cortex. Delay line scheme is one of the effective ways for detecting such neuronal timing. However, the scheme is not straightforwardly applicable if the time intervals of signals are beyond the latency time of delay lines. In fact, monkey calls are often expressed in longer time intervals (hundreds of milliseconds to seconds) and are beyond the latency times observed in the brain (less than several hundreds of milliseconds). Here, we propose a cochleotopic map similar to that in vision known as a retinotopic map. We show that information about monkey calls could be mapped on a cochleotopic cortical network as spatiotemporal firing patterns of neurons, which can then be decomposed into simple (linearly sweeping) FM components and integrated into unified percepts by higher cortical networks. We suggest that the spatiotemporal conversion of auditory information may be essential for developing the cochleotopic map, which could serve as the foundation for later processing, or monkey call identification by higher cortical areas.

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“…When the spike timings of an input vector are fully synchronised with the spike timing of the target vector, each input synapse connected to the spiking neuron will respond with a maximum value of 1, resulting in an output of 1 as shown in Figure b. If a spike is off‐synchronised with respect to its corresponding spike timing in the target vector, the response of the synapse will decrease according to a Gaussian distribution function (shaded area in Figure b), as in a biological auditory nerve response (Greenberg et al ., ); (Panchev & Wermter, ). In other words, the more off‐synchronised spikes the input vector is, the lower output response the spiking neuron produces.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Speaker verification inspired by the physiology of hearing using spiking self‐organising map

2014

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In this paper, a speaker verification algorithm, which is inspired by the physiology of hearing in the human auditory system, is proposed. The algorithm uses a modified self‐organising map composed of spiking neurons. The architecture of the algorithm imitates the biomechanical mechanism of the human auditory system, which converts the speech signal from sound vibration waves into electrical spikes inside the cochlea. The paper also suggests a spike‐based rank order coding input feature vector, which is designed to be representative of the real biological spike trains found within the human auditory nerve. During the training phase, the winner neuron in the proposed Spiking Self‐Organising Map is updated only when its activity exceeds a specified threshold. The algorithm is evaluated using 50 speakers from the Centre for Spoken Language Understanding (CSLU2002) speaker verification database and shows a speaker verification performance of 90.1%. This compares favourably with previous non‐spiking self‐organising map that used Discrete Fourier Transform‐based input feature vector with the same dataset.

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Spike-timing-dependent synaptic plasticity: from single spikes to spike trains

Cited by 7 publications

References 0 publications

Hebbian learning with winner take all for spiking neural networks

Hebbian learning with winner take all for spiking neural networks

Spatiotemporal Conversion of Auditory Information for Cochleotopic Mapping

Speaker verification inspired by the physiology of hearing using spiking self‐organising map

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