Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms

Advancing the size and complexity of neural network models leads to an ever increasing demand for computational resources for their simulation. Neuromorphic devices offer a number of advantages over conventional computing architectures, such as high emulation speed or low power consumption, but this usually comes at the price of reduced configurability and precision. In this article, we investigate the consequences of several such factors that are common to neuromorphic devices, more specifically limited hardware resources, limited parameter configurability and parameter variations. Our final aim is to provide an array of methods for coping with such inevitable distortion mechanisms. As a platform for testing our proposed strategies, we use an executable system specification (ESS) of the BrainScaleS neuromorphic system, which has been designed as a universal emulation back-end for neuroscientific modeling. We address the most essential limitations of this device in detail and study their effects on three prototypical benchmark network models within a well-defined, systematic workflow. For each network model, we start by defining quantifiable functionality measures by which we then assess the effects of typical hardware-specific distortion mechanisms, both in idealized software simulations and on the ESS. For those effects that cause unacceptable deviations from the original network dynamics, we suggest generic compensation mechanisms and demonstrate their effectiveness. Both the suggested workflow and the investigated compensation mechanisms are largely back-end independent and do not require additional hardware configurability beyond the one required to emulate the benchmark networks in the first place. We hereby provide a generic methodological environment for configurable neuromorphic devices that are targeted at emulating large-scale, functional neural networks

Signal Propagation in Feedforward Neuronal Networks with Unreliable Synapses

In this paper, we systematically investigate both the synfire propagation and firing rate propagation in feedforward neuronal network coupled in an all-to-all fashion. In contrast to most earlier work, where only reliable synaptic connections are considered, we mainly examine the effects of unreliable synapses on both types of neural activity propagation in this work. We first study networks composed of purely excitatory neurons. Our results show that both the successful transmission probability and excitatory synaptic strength largely influence the propagation of these two types of neural activities, and better tuning of these synaptic parameters makes the considered network support stable signal propagation. It is also found that noise has significant but different impacts on these two types of propagation. The additive Gaussian white noise has the tendency to reduce the precision of the synfire activity, whereas noise with appropriate intensity can enhance the performance of firing rate propagation. Further simulations indicate that the propagation dynamics of the considered neuronal network is not simply determined by the average amount of received neurotransmitter for each neuron in a time instant, but also largely influenced by the stochastic effect of neurotransmitter release. Second, we compare our results with those obtained in corresponding feedforward neuronal networks connected with reliable synapses but in a random coupling fashion. We confirm that some differences can be observed in these two different feedforward neuronal network models. Finally, we study the signal propagation in feedforward neuronal networks consisting of both excitatory and inhibitory neurons, and demonstrate that inhibition also plays an important role in signal propagation in the considered networks.Comment: 33pages, 16 figures; Journal of Computational Neuroscience (published

Graded, Dynamically Routable Information Processing with Synfire-Gated Synfire Chains

Coherent neural spiking and local field potentials are believed to be signatures of the binding and transfer of information in the brain. Coherent activity has now been measured experimentally in many regions of mammalian cortex. Synfire chains are one of the main theoretical constructs that have been appealed to to describe coherent spiking phenomena. However, for some time, it has been known that synchronous activity in feedforward networks asymptotically either approaches an attractor with fixed waveform and amplitude, or fails to propagate. This has limited their ability to explain graded neuronal responses. Recently, we have shown that pulse-gated synfire chains are capable of propagating graded information coded in mean population current or firing rate amplitudes. In particular, we showed that it is possible to use one synfire chain to provide gating pulses and a second, pulse-gated synfire chain to propagate graded information. We called these circuits synfire-gated synfire chains (SGSCs). Here, we present SGSCs in which graded information can rapidly cascade through a neural circuit, and show a correspondence between this type of transfer and a mean-field model in which gating pulses overlap in time. We show that SGSCs are robust in the presence of variability in population size, pulse timing and synaptic strength. Finally, we demonstrate the computational capabilities of SGSC-based information coding by implementing a self-contained, spike-based, modular neural circuit that is triggered by, then reads in streaming input, processes the input, then makes a decision based on the processed information and shuts itself down

Six networks on a universal neuromorphic computing substrate

In this study, we present a highly configurable neuromorphic computing substrate and use it for emulating several types of neural networks. At the heart of this system lies a mixed-signal chip, with analog implementations of neurons and synapses and digital transmission of action potentials. Major advantages of this emulation device, which has been explicitly designed as a universal neural network emulator, are its inherent parallelism and high acceleration factor compared to conventional computers. Its configurability allows the realization of almost arbitrary network topologies and the use of widely varied neuronal and synaptic parameters. Fixed-pattern noise inherent to analog circuitry is reduced by calibration routines. An integrated development environment allows neuroscientists to operate the device without any prior knowledge of neuromorphic circuit design. As a showcase for the capabilities of the system, we describe the successful emulation of six different neural networks which cover a broad spectrum of both structure and functionality

Experiments applications guide: Advanced Communications Technology Satellite (ACTS)

This applications guide first surveys the capabilities of the Advanced Communication Technology Satellite (ACTS) system (both the flight and ground segments). This overview is followed by a description of the baseband processor (BBP) and microwave switch matrix (MSM) operating modes. Terminals operating with the baseband processor are referred to as low burst rate (LBR); and those operating with the microwave switch matrix, as high burst rate (HBR). Three very small-aperture terminals (VSATs), LBR-1, LBR-2, and HBR, are described for various ACTS operating modes. Also described is the NASA Lewis link evaluation terminal. A section on ACTS experiment opportunities introduces a wide spectrum of network control, telecommunications, system, and scientific experiments. The performance of the VSATs is discussed in detail. This guide is intended as a catalyst to encourage participation by the telecommunications, business, and science communities in a broad spectrum of experiments

Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression

Investigation of methods for data communication and power delivery through metals

PhD ThesisThe retrieval of data from a sensor, enclosed by a metallic structure, such as a naval vessel, pipeline or nuclear flask is often very challenging. To maintain structural integrity it is not desirable to penetrate the wall of the structure, preventing any hard-wired solution. Furthermore, the conductive nature of the structure prevents the use of radio communications. Applications involving sealed containers also have a requirement for power delivery, as the periodic changing of batteries is not possible. Ultrasound has previously been identified as an attractive approach but there are two key challenges: efficient/reliable ultrasonic transduction and a method of overcoming the inherent multipath distortion resulting from boundary reflections. Previous studies have utilised piezoelectric contact transducers, however, they are impractical due to their reliance on coupling, i.e. the bond between the transducer and the metal surface, which leads to concerns over long term reliability. A non-contact transducer overcomes this key drawback, thus highlighting the electromagnetic acoustic transducer (EMAT) as a favourable alternative. This thesis presents the design and testing of an EMAT with appropriate characteristics for through-metal data communications. A low cost, low power data transmission scheme is presented for overcoming acoustic multipath based on pulse position modulation (PPM). Due to the necessary guard time, the data rate is limited to 50kbps. A second solution is presented employing continuous wave, Quadrature phase shift keying (QPSK) modulation, allowing data rates in excess of 1Mbps to be achieved. Equalisation is required to avoid intersymbol interference (ISI) and a decision feedback equaliser (DFE) is shown to be adept at mitigating this effect. The relatively low efficiency of an EMAT makes it unsuitable for power delivery, consequently, an alternative non-contact approach, utilising inductive coupling, is explored. Power transfer efficiency of ≈ 4% is shown to be achievable through 20mm thick stainless steel.ICS department of BAE Systems Submarine Solutions, EPSR