Artificial Cognitive Systems: From VLSI Networks of Spiking Neurons to Neuromorphic Cognition

Indiveri, Giacomo; Chicca, Elisabetta; Douglas, Rodney J.

doi:10.1007/s12559-008-9003-6

Cited by 115 publications

(80 citation statements)

References 61 publications

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“…To overcome this limitation, several groups around the world have started to develop hardware realizations of spiking neuron models and neuronal networks (2)(3)(4)(5)(6)(7)(8)(9)(10) for studying the behavior of biological networks (11). The approach of the Spikey hardware system used in the present study is to enable high-throughput network simulations by speeding up computation by a factor of 10 4 compared with biological real time (12,13).…”

mentioning

confidence: 99%

A neuromorphic network for generic multivariate data classification

Schmuker

Pfeil

Nawrot

2014

Proc. Natl. Acad. Sci. U.S.A.

119

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Computational neuroscience has uncovered a number of computational principles used by nervous systems. At the same time, neuromorphic hardware has matured to a state where fast silicon implementations of complex neural networks have become feasible. En route to future technical applications of neuromorphic computing the current challenge lies in the identification and implementation of functional brain algorithms. Taking inspiration from the olfactory system of insects, we constructed a spiking neural network for the classification of multivariate data, a common problem in signal and data analysis. In this model, real-valued multivariate data are converted into spike trains using "virtual receptors" (VRs). Their output is processed by lateral inhibition and drives a winner-take-all circuit that supports supervised learning. VRs are conveniently implemented in software, whereas the lateral inhibition and classification stages run on accelerated neuromorphic hardware. When trained and tested on real-world datasets, we find that the classification performance is on par with a naïve Bayes classifier. An analysis of the network dynamics shows that stable decisions in output neuron populations are reached within less than 100 ms of biological time, matching the time-to-decision reported for the insect nervous system. Through leveraging a population code, the network tolerates the variability of neuronal transfer functions and trial-totrial variation that is inevitably present on the hardware system. Our work provides a proof of principle for the successful implementation of a functional spiking neural network on a configurable neuromorphic hardware system that can readily be applied to realworld computing problems.bioinspired computing | spiking networks | machine learning | multivariate classification T he remarkable sensory and behavioral capabilities of all higher organisms are provided by the network of neurons in their nervous systems. The computing principles of the brain have inspired many powerful algorithms for data processing, most importantly the perceptron and, building on top of that, multilayer artificial neural networks, which are being applied with great success to various data analysis problems (1). Although these networks operate with continuous values, computation in biological neuronal networks relies on the exchange of action potentials, or "spikes."Simulating networks of spiking neurons with software tools is computationally intensive, imposing limits to the duration of simulations and maximum network size. To overcome this limitation, several groups around the world have started to develop hardware realizations of spiking neuron models and neuronal networks (2-10) for studying the behavior of biological networks (11). The approach of the Spikey hardware system used in the present study is to enable high-throughput network simulations by speeding up computation by a factor of 10 4 compared with biological real time (12, 13). It has been developed as a reconfigurable multineuron computing substrat...

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mentioning

confidence: 99%

A neuromorphic network for generic multivariate data classification

Schmuker

Pfeil

Nawrot

2014

Proc. Natl. Acad. Sci. U.S.A.

119

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“…For applications that require on line training we intend to use evolving SNN classifier [11], [12]. Finally, implementation of the developed models on existing SNN hardware [22], [23] will be studied especially for on-line learning and object recognition applications such as intelligent mobile robots [24].…”

Section: Discussionmentioning

confidence: 99%

Evolving Probabilistic Spiking Neural Networks for Spatio-temporal Pattern Recognition: A Preliminary Study on Moving Object Recognition

Kasabov

Dhoble

Nuntalid

et al. 2011

Lecture Notes in Computer Science

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Abstract. This paper proposes a novel architecture for continuous spatio-temporal data modeling and pattern recognition utilizing evolving probabilistic spiking neural network 'reservoirs' (epSNNr). The paper demonstrates on a simple experimental data for moving object recognition that: (1) The epSNNr approach is more accurate and flexible than using standard SNN; (2) The use of probabilistic neuronal models is superior in several aspects when compared with the traditional deterministic SNN models, including a better performance on noisy data.

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“…The paper uses a recently proposed SNN architectureNeuCube [10], [23] that refers also to elements of previous studies [11]- [17].…”

Section: The Neucube Snn-based Architecturementioning

confidence: 99%

Personalised modelling on integrated clinical and EEG Spatio-Temporal Brain Data in the NeuCube Spiking Neural Network system

Doborjeh

Kasabov

2016

2016 International Joint Conference on Neural Networks (IJCNN)

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Abstract-This paper introduces a novel personalised modelling framework and system for analysing Spatio-Temporal Brain Data (STBD) along with person clinical static data. For every individual, based on selected subset of similar to this individual clinical data, a subset of STBD is used for training a personalised Spiking Neural Network (PSNN) model using the recently proposed NeuCube SNN architecture. The proposed method is illustrated on a case study of personalised modelling using clinical and EEG data of two groups of subjects -drug addicts and addicts under medication.The PSNN models help to achieve a better classification accuracy compared to global SNN models or when using traditional AI methods. A PSNN model visualisation enables discovery of new knowledge about individual persons and to distinguish complex STBD across subjects.

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Artificial Cognitive Systems: From VLSI Networks of Spiking Neurons to Neuromorphic Cognition

Cited by 115 publications

References 61 publications

A neuromorphic network for generic multivariate data classification

A neuromorphic network for generic multivariate data classification

Evolving Probabilistic Spiking Neural Networks for Spatio-temporal Pattern Recognition: A Preliminary Study on Moving Object Recognition

Personalised modelling on integrated clinical and EEG Spatio-Temporal Brain Data in the NeuCube Spiking Neural Network system

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