On the efficient calculation of van Rossum distances

Houghton, Conor; Kreuz, Thomas

doi:10.3109/0954898x.2012.673048

Cited by 34 publications

(27 citation statements)

References 13 publications

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“…The function average is not a summary in that its most concise representation is given by the times of all the spikes in

false{u_{1}, u_{2}, \dots, u_{n} false}

. Since the algorithmic expense of calculating the distance between two spike trains is of the order of the number of spikes [10], this means, for example, that it is as expensive to calculate the distance between a novel spike train v and the function average as it is to calculate the distance between v and all of the spike trains

false{u_{1}, u_{2}, \dots, u_{n} false}

individually. Calculating the distance between v and the central spike train is thus n times faster.…”

Section: Methodsmentioning

confidence: 99%

A Simple Algorithm for Averaging Spike Trains

Julienne

Houghton

2013

The Journal of Mathematical Neuroscience

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Although spike trains are the principal channel of communication between neurons, a single stimulus will elicit different spike trains from trial to trial. This variability, in both spike timings and spike number can obscure the temporal structure of spike trains and often means that computations need to be run on numerous spike trains in order to extract features common across all the responses to a particular stimulus. This can increase the computational burden and obscure analytical results. As a consequence, it is useful to consider how to calculate a central spike train that summarizes a set of trials. Indeed, averaging responses over trials is routine for other signal types. Here, a simple method for finding a central spike train is described. The spike trains are first mapped to functions, these functions are averaged, and a greedy algorithm is then used to map the average function back to a spike train. The central spike trains are tested for a large data set. Their performance on a classification-based test is considerably better than the performance of the medoid spike trains.

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“…The function average is not a summary in that its most concise representation is given by the times of all the spikes in

false{u_{1}, u_{2}, \dots, u_{n} false}

false{u_{1}, u_{2}, \dots, u_{n} false}

individually. Calculating the distance between v and the central spike train is thus n times faster.…”

Section: Methodsmentioning

confidence: 99%

A Simple Algorithm for Averaging Spike Trains

Julienne

Houghton

2013

The Journal of Mathematical Neuroscience

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“…One example is the multineuron van Rossum distance proposed by Houghton and Sen [11,10]. Kreuz et al extended the ISI (inter-spike interval) distance to include multineuron spike trains [14].…”

Section: Related Workmentioning

confidence: 98%

“…Since the mixture kernel performed best among proposed kernels, we compared it with a conventional method, the multineuron van Rossum distance. To calculate the latter, we used a program implemented by Thomas Kreuz 2 [10]. When we used their original parameter settings, namely rsd tc = 0.005 and cosalpha = 0.5, we got J = 2.995 × 10 −6 .…”

Section: Comparison Of Single-neuron and Multineuron MCImentioning

confidence: 99%

“…Such data can be obtained either through multiple site recordings [1] or spike sorting [19]. The existing work, however, has only dealt with either distances between multiple neuron spike trains [4,11,10,14,1] or kernels on single neuron spike trains [17,18]. The aim of this paper is to propose kernels for spike trains taken from multiple neurons.…”

Section: Introductionmentioning

confidence: 99%

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Spike Train kernels for multiple neuron recordings

Tezuka

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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There is a growing interest in analyzing multineuron spike trains, which are spike timing data obtained from multiple neurons in the brain. Kernel methods have been successful in clustering and classification of single-neuron spike trains. We extend these methods to multineuron spike trains. Among various possible extensions, the mixture kernel was found to be most effective. The optimum parameter obtained from training this kernel was close to a biologically plausible value, suggesting that our approach is effective for seeking an appropriate model for the activity of a set of neurons.

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“…We use the Van Rossum distance [34] with respect to a 1-µs-integration-step-timedriven simulation to compare the accuracy of the results. We also show the speed-up rate obtained with the time-driven method in GPU and the event-driven method in CPU compared to the stand alone time-driven method in CPU.…”

Section: A First Experimental Setupmentioning

confidence: 99%

A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU Co-Processing: A Case Study

Naveros

Luque

Garrido

et al. 2015

IEEE Trans. Neural Netw. Learning Syst.

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Time-driven simulation methods in traditional CPU architectures perform well and precisely when simulating small-scale spiking neural networks. Nevertheless, they still have drawbacks when simulating large-scale systems. Conversely, event-driven simulation methods in CPUs and time-driven simulation methods in graphic processing units (GPUs) can outperform CPU time-driven methods under certain conditions. With this performance improvement in mind, we have developed an event-and-time-driven spiking neural network simulator suitable for a hybrid CPU-GPU platform. Our neural simulator is able to efficiently simulate bio-inspired spiking neural networks consisting of different neural models, which can be distributed heterogeneously in both small layers and large layers or subsystems. For the sake of efficiency, the low-activity parts of the neural network can be simulated in CPU using event-driven methods while the high-activity subsystems can be simulated in either CPU (a few neurons) or GPU (thousands or millions of neurons) using time-driven methods. In this brief, we have undertaken a comparative study of these different simulation methods. For benchmarking the different simulation methods and platforms, we have used a cerebellar-inspired neural-network model consisting of a very dense granular layer and a Purkinje layer with a smaller number of cells (according to biological ratios). Thus, this cerebellar-like network includes a dense diverging neural layer (increasing the dimensionality of its internal representation and sparse coding) and a converging neural layer (integration) similar to many other biologically inspired and also artificial neural networks.

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On the efficient calculation of van Rossum distances

Cited by 34 publications

References 13 publications

A Simple Algorithm for Averaging Spike Trains

A Simple Algorithm for Averaging Spike Trains

Spike Train kernels for multiple neuron recordings

A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU Co-Processing: A Case Study

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