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There have been a number of corner detection methods proposed for event cameras in the last years, since event-driven computer vision has become more accessible. Current state-of-the-art have either unsatisfactory accuracy or real-time performance when considered for practical use, for example when a camera is randomly moved in an unconstrained environment. In this paper, we present yet another method to perform corner detection, dubbed look-up event-Harris (luvHarris), that employs the Harris algorithm for high accuracy but manages an improved event throughput. Our method has two major contributions, 1. a novel “threshold ordinal event-surface” that removes certain tuning parameters and is well suited for Harris operations, and 2. an implementation of the Harris algorithm such that the computational load per event is minimised and computational heavy convolutions are performed only ‘as-fast-as-possible’, i.e., only as computational resources are available. The result is a practical, real-time, and robust corner detector that runs more than $2.6\times$ 2. 6 × the speed of current state-of-the-art; a necessity when using a high-resolution event-camera in real-time. We explain the considerations taken for the approach, compare the algorithm to current state-of-the-art in terms of computational performance and detection accuracy, and discuss the validity of the proposed approach for event cameras. [ABSTRACT FROM AUTHOR]

Applications requiring detection of small visual contrast require high sensitivity. Event cameras can provide higher dynamic range (DR) and reduce data rate and latency, but most existing event cameras have limited sensitivity. This paper presents the results of a 180-nm Towerjazz CIS process vision sensor called SDAVIS192. It outputs temporal contrast dynamic vision sensor (DVS) events and conventional active pixel sensor frames. The SDAVIS192 improves on previous DAVIS sensors with higher sensitivity for temporal contrast. The temporal contrast thresholds can be set down to 1% for negative changes in logarithmic intensity (OFF events) and down to 3.5% for positive changes (ON events). The achievement is possible through the adoption of an in-pixel preamplification stage. This preamplifier reduces the effective intrascene DR of the sensor (70 dB for OFF and 50 dB for ON), but an automated operating region control allows up to at least 110-dB DR for OFF events. A second contribution of this paper is the development of characterization methodology for measuring DVS event detection thresholds by incorporating a measure of signal-to-noise ratio (SNR). At average SNR of 30 dB, the DVS temporal contrast threshold fixed pattern noise is measured to be 0.3%–0.8% temporal contrast. Results comparing monochrome and RGBW color filter array DVS events are presented. The higher sensitivity of SDAVIS192 make this sensor potentially useful for calcium imaging, as shown in a recording from cultured neurons expressing calcium sensitive green fluorescent protein GCaMP6f. [ABSTRACT FROM PUBLISHER]

The asynchronous time-based neuromorphic image sensor ATIS is an array of autonomously operating pixels able to encode luminance information with an exceptionally high dynamic range (>143 dB). This paper introduces an event-based methodology to display data from this type of event-based imagers, taking into account the large dynamic range and high temporal accuracy that go beyond available mainstream display technologies. We introduce an event-based tone mapping methodology for asynchronously acquired time encoded gray-level data. A global and a local tone mapping operator are proposed. Both are designed to operate on a stream of incoming events rather than on time frame windows. Experimental results on real outdoor scenes are presented to evaluate the performance of the tone mapping operators in terms of quality, temporal stability, adaptation capability, and computational time. [ABSTRACT FROM AUTHOR]

A very-large-scale integration field-programmable mixed-signal array specialized for neural signal processing and neural modeling has been designed. This has been fabricated as a core on a chip prototype intended for use in an implantable closed-loop prosthetic system aimed at rehabilitation of the learning of a discrete motor response. The chosen experimental context is cerebellar classical conditioning of the eye-blink response. The programmable system is based on the intimate mixing of switched capacitor analog techniques with low speed digital computation; power saving innovations within this framework are presented. The utility of the system is demonstrated by the implementation of a motor classical conditioning model applied to eye-blink conditioning in real time with associated neural signal processing. Paired conditioned and unconditioned stimuli were repeatedly presented to an anesthetized rat and recordings were taken simultaneously from two precerebellar nuclei. These paired stimuli were detected in real time from this multichannel data. This resulted in the acquisition of a trigger for a well-timed conditioned eye-blink response, and repetition of unpaired trials constructed from the same data led to the extinction of the conditioned response trigger, compatible with natural cerebellar learning in awake animals. [ABSTRACT FROM PUBLISHER]

Analogue and mixed-signal VLSI implementations of Spike-Timing-Dependent Plasticity (STDP) are reviewed. A circuit is presented with a compact implementation of STDP suitable for parallel integration in large synaptic arrays. In contrast to previously published circuits, it uses the limitations of the silicon substrate to achieve various forms and degrees of weight dependence of STDP. It also uses reverse-biased transistors to reduce leakage from a capacitance representing weight. Chip results are presented showing: various ways in which the learning rule may be shaped; how synaptic weights may retain some indication of their learned values over periods of minutes; and how distributions of weights for synapses convergent on single neurons may shift between more or less extreme bimodality according to the strength of correlational cues in their inputs. [ABSTRACT FROM PUBLISHER]

A distributed and locally reprogrammable address--event receiver has been designed, in which incoming address--events are monitored simultaneously by all synapses, allowing for arbitrarily large axonal fan-out without reducing channel capacity. Synapses can change the address of their presynaptic neuron, allowing the distributed implementation of a biologically realistic learning rule, with both synapse formation and elimination (synaptic rewiring). Probabilistic synapse formation leads to topographic map development, made possible by a cross-chip current-mode calculation of Euclidean distance. As well as synaptic plasticity in rewiring, synapses change weights using a competitive Hebbian learning rule (spike-timing-dependent plasticity). The weight plasticity allows receptive fields to be modified based on spatio-temporal correlations in the inputs, and the rewiring plasticity allows these modifications to become embedded in the network topology. [ABSTRACT FROM AUTHOR]

Neuroprostheses could potentially recover functions lost due to neural damage. Typical neuroprostheses connect an intact brain with the external environment, thus replacing damaged sensory or motor pathways. Recently, closed-loop neuroprostheses, bidirectionally interfaced with the brain, have begun to emerge, offering an opportunity to substitute malfunctioning brain structures. In this proof-of-concept study, we demonstrate a neuro-inspired model-based approach to neuroprostheses. A VLSI chip was designed to implement essential cerebellar synaptic plasticity rules, and was interfaced with cerebellar input and output nuclei in real time, thus reproducing cerebellum-dependent learning in anesthetized rats. Such a model-based approach does not require prior system identification, allowing for de novo experience-based learning in the brain-chip hybrid, with potential clinical advantages and limitations when compared to existing parametric 'black box' models. [ABSTRACT FROM AUTHOR]