Reliable Computation in Noisy Backgrounds Using Real-Time Neuromorphic Hardware

Wang H-P, Chicca E, Indiveri G, Sejnowski TJ. Reliable Computation in Noisy Backgrounds Using Real-Time Neuromorphic Hardware. Presented at the Biomedical Circuits and Systems Conference (BIOCAS), Montreal, Que.Spike-time based coding of neural information, in contrast to rate coding, requires that neurons reliably and precisely fire spikes in response to repeated identical inputs, despite a high degree of noise from stochastic synaptic firing and extraneous background inputs. We investigated the degree of reliability and precision achievable in various noisy background conditions using real-time neuromorphic VLSI hardware which models integrate-and-fire spiking neurons and dynamic synapses. To do so, we varied two properties of the inputs to a single neuron, synaptic weight and synchrony magnitude (number of synchronously firing pre-synaptic neurons). Thanks to the realtime response properties of the VLSI system we could carry out extensive exploration of the parameter space, and measure the neurons firing rate and reliability in real-time. Reliability of output spiking was primarily influenced by the amount of synchronicity of synaptic input, rather than the synaptic weight of those synapses. These results highlight possible regimes in which real-time neuromorphic systems might be better able to reliably compute with spikes despite noisy input

A guide to time-resolved and parameter-free measures of spike train synchrony

Measures of spike train synchrony have proven a valuable tool in both experimental and computational neuroscience. Particularly useful are time-resolved methods such as the ISI- and the SPIKE-distance, which have already been applied in various bivariate and multivariate contexts. Recently, SPIKE-Synchronization was proposed as another time-resolved synchronization measure. It is based on Event-Synchronization and has a very intuitive interpretation. Here, we present a detailed analysis of the mathematical properties of these three synchronization measures. For example, we were able to obtain analytic expressions for the expectation values of the ISI-distance and SPIKE-Synchronization for Poisson spike trains. For the SPIKE-distance we present an empirical formula deduced from numerical evaluations. These expectation values are crucial for interpreting the synchronization of spike trains measured in experiments or numerical simulations, as they represent the point of reference for fully randomized spike trains.Comment: 8 pages, 4 figure

Exploiting plume structure to decode gas source distance using metal-oxide gas sensors

Estimating the distance of a gas source is important in many applications of chemical sensing, like e.g. environmental monitoring, or chemically-guided robot navigation. If an estimation of the gas concentration at the source is available, source proximity can be estimated from the time-averaged gas concentration at the sensing site. However, in turbulent environments, where fast concentration fluctuations dominate, comparably long measurements are required to obtain a reliable estimate. A lesser known feature that can be exploited for distance estimation in a turbulent environment lies in the relationship between source proximity and the temporal variance of the local gas concentration – the farther the source, the more intermittent are gas encounters. However, exploiting this feature requires measurement of changes in gas concentration on a comparably fast time scale, that have up to now only been achieved using photo-ionisation detectors. Here, we demonstrate that by appropriate signal processing, off-theshelf metal-oxide sensors are capable of extracting rapidly fluctuating features of gas plumes that strongly correlate with source distance. We show that with a straightforward analysis method it is possible to decode events of large, consistent changes in the measured signal, so-called ‘bouts’. The frequency of these bouts predicts the distance of a gas source in wind-tunnel experiments with good accuracy. In addition, we found that the variance of bout counts indicates cross-wind offset to the centreline of the gas plume. Our results offer an alternative approach to estimating gas source proximity that is largely independent of gas concentration, using off-the-shelf metal-oxide sensors. The analysis method we employ demands very few computational resources and is suitable for low-power microcontrollers

Measuring spike train synchrony

Estimating the degree of synchrony or reliability between two or more spike trains is a frequent task in both experimental and computational neuroscience. In recent years, many different methods have been proposed that typically compare the timing of spikes on a certain time scale to be fixed beforehand. Here, we propose the ISI-distance, a simple complementary approach that extracts information from the interspike intervals by evaluating the ratio of the instantaneous frequencies. The method is parameter free, time scale independent and easy to visualize as illustrated by an application to real neuronal spike trains obtained in vitro from rat slices. In a comparison with existing approaches on spike trains extracted from a simulated Hindemarsh-Rose network, the ISI-distance performs as well as the best time-scale-optimized measure based on spike timing.Comment: 11 pages, 13 figures; v2: minor modifications; v3: minor modifications, added link to webpage that includes the Matlab Source Code for the method (http://inls.ucsd.edu/~kreuz/Source-Code/Spike-Sync.html

Statistical-Mechanical Measure of Stochastic Spiking Coherence in A Population of Inhibitory Subthreshold Neurons

By varying the noise intensity, we study stochastic spiking coherence (i.e., collective coherence between noise-induced neural spikings) in an inhibitory population of subthreshold neurons (which cannot fire spontaneously without noise). This stochastic spiking coherence may be well visualized in the raster plot of neural spikes. For a coherent case, partially-occupied "stripes" (composed of spikes and indicating collective coherence) are formed in the raster plot. This partial occupation occurs due to "stochastic spike skipping" which is well shown in the multi-peaked interspike interval histogram. The main purpose of our work is to quantitatively measure the degree of stochastic spiking coherence seen in the raster plot. We introduce a new spike-based coherence measure $M_s$ by considering the occupation pattern and the pacing pattern of spikes in the stripes. In particular, the pacing degree between spikes is determined in a statistical-mechanical way by quantifying the average contribution of (microscopic) individual spikes to the (macroscopic) ensemble-averaged global potential. This "statistical-mechanical" measure $M_s$ is in contrast to the conventional measures such as the "thermodynamic" order parameter (which concerns the time-averaged fluctuations of the macroscopic global potential), the "microscopic" correlation-based measure (based on the cross-correlation between the microscopic individual potentials), and the measures of precise spike timing (based on the peri-stimulus time histogram). In terms of $M_s$, we quantitatively characterize the stochastic spiking coherence, and find that $M_s$ reflects the degree of collective spiking coherence seen in the raster plot very well. Hence, the "statistical-mechanical" spike-based measure $M_s$ may be used usefully to quantify the degree of stochastic spiking coherence in a statistical-mechanical way.Comment: 16 pages, 5 figures, to appear in the J. Comput. Neurosc

Heterogeneity in Neuronal Calcium Spike Trains based on Empirical Distance

Statistical similarities between neuronal spike trains could reveal significant information on complex underlying processing. In general, the similarity between synchronous spike trains is somewhat easy to identify. However, the similar patterns also potentially appear in an asynchronous manner. However, existing methods for their identification tend to converge slowly, and cannot be applied to short sequences. In response, we propose Hellinger distance measure based on empirical probabilities, which we show to be as accurate as existing techniques, yet faster to converge for synthetic as well as experimental spike trains. Further, we cluster pairs of neuronal spike trains based on statistical similarities and found two non-overlapping classes, which could indicate functional similarities in neurons. Significantly, our technique detected functional heterogeneity in pairs of neuronal responses with the same performance as existing techniques, while exhibiting faster convergence. We expect the proposed method to facilitate large-scale studies of functional clustering, especially involving short sequences, which would in turn identify signatures of various diseases in terms of clustering patterns

Feedforward computational model for pattern recognition with spiking neurons

Humans and primates are remarkably good at pattern recognition and outperform the best machine vision systems with respect to almost any measure. Building a computational model that emulates the architecture and information processing in biological neural systems has always been an attractive target. To build a computational model that closely follows the information processing and architecture of the visual cortex, in this paper, we have improved the latency-phase encoding to express the external stimuli in a more abstract manner. Moreover, inspired by recent findings in the biological neural system, including architecture, encoding, and learning theories, we have proposed a feedforward computational model of spiking neurons that emulates object recognition of the visual cortex for pattern recognition. Simulation results showed that the proposed computational model can perform pattern recognition task well. In addition, the success of this computational model suggests a plausible proof for feedforward architecture of pattern recognition in the visual cortex