A neuromorphic network for generic multivariate data classification

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-to-trial 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 real-world computing problems.The 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 10⁴ compared with biological real time (12, 13). It has been developed as a reconfigurable multineuron computing substrate supporting a wide range of network topologies (14).In addition to providing faster tools for neurosimulation, high-throughput spiking network computation in hardware offers the possibility of using spiking networks to solve real-world computational problems. The massive parallelism is a potential advantage over conventional computing when processing large amounts of data in parallel. However, conventional algorithms are often difficult to implement using spiking networks for which many neuromorphic hardware substrates are designed. Novel algorithms have to be designed that embrace the inherent parallelism of a brain-like computing architecture.A common problem in data analysis is classification of multivariate data. Many problems in artificial intelligence relate to classification in some way or the other, such as object recognition or decision making. It is the basis for data mining and, as such, has widespread applications in industry. We interact with classification systems in many aspects of daily life, for example in the form of Web shop recommendations, driver assistance systems, or when sending a letter with a handwritten address that is deciphered automatically in the post office.In this work, we present a neuromorphic network for supervised classification of multivariate data. We implemented the spiking network part on a neuromorphic hardware system. Using a range of datasets, we demonstrate how the classifier network supports nonlinear separation through encoding by virtual receptors, whereas lateral inhibition transforms the input data into a sparser encoding that is better suited for learning.