Repeating spatiotemporal spike patterns exist and carry information. Here we investigated how a single spiking neuron can optimally respond to one given pattern (localist coding), or to either one of several patterns (distributed coding, i.e., the neuron's response is ambiguous but the identity of the pattern could be inferred from the response of multiple neurons), but not to random inputs. To do so, we extended a theory developed in a previous paper (Masquelier, 2017), which was limited to localist coding. More specifically, we computed analytically the signal-to-noise ratio (SNR) of a multi-pattern-detector neuron, using a threshold-free leaky integrate-and-fire (LIF) neuron model with non-plastic unitary synapses and homogeneous Poisson inputs. Surprisingly, when increasing the number of patterns, the SNR decreases slowly, and remains acceptable for several tens of independent patterns. In addition, we investigated whether spike-timing-dependent plasticity (STDP) could enable a neuron to reach the theoretical optimal SNR. To this aim, we simulated a LIF equipped with STDP, and repeatedly exposed it to multiple input spike patterns, embedded in equally dense Poisson spike trains. The LIF progressively became selective to every repeating pattern with no supervision, and stopped discharging during the Poisson spike trains. Furthermore, tuning certain STDP parameters, the resulting pattern detectors were optimal. Tens of independent patterns could be learned by a single neuron using a low adaptive threshold, in contrast with previous studies, in which higher thresholds led to localist coding only. Taken together these results suggest that coincidence detection and STDP are powerful mechanisms, fully compatible with distributed coding. Yet we acknowledge that our theory is limited to single neurons, and thus also applies to feed-forward networks, but not to recurrent ones.

Journal Paper - Frontiers in Neuroscience, vol. 12, Article 74, 2018 FRONTIERS RESEARCH FOUNDATION
DOI: 10.3389/fncom.2018.00074    ISSN: 1662-4548    » doi

Recent advances in both software and hardware technologies are enabling the emergence of vision as a key sensorial modality in various application scenarios. Concerning hardware, all of the components along the signal chain play a significant role when it comes to implementing smart vision-enabled systems. At the front end, new circuit structures for sensing, processing, and signal conditioning are adding functionalities in CMOS imagers beyond the mere generation of 2-D intensity maps. Moreover, the development of vertical integration technologies is facilitating monolithic realizations of visual sensors where the incorporation of computational capabilities has no impact at all on image quality. Typically, the outcome of the front-end device in a smart camera will be a preprocessed flow of information ready for further efficient analysis. At this point, specific ICs known as vision processing units can be inserted to accelerate the processing flow according to the targeted application. On the other hand, reconfigurability is a valuable asset in the ever-changing field of vision. FPGAs leverage cutting-edge digital technologies to offer flexible hardware for exploration of different memory arrangements, data flows, and processing parallelization. It is precisely parallelization for which GPUs constitute an interesting alternative in smart cameras when massive pixel-level operation is required. This is the case of state-of-the-art vision algorithms based on convolutional neural networks. At higher level, DSPs and multicore CPUs make software development notably easier at the cost of losing hardware specificity. Overall, this special issue aims at covering some of the latest research works in the vast ecosystem of hardware for artificial vision.

Journal Paper - International Journal of Circuit Theory and Applications, first online, 2018 JOHN WILEY & SONS
DOI: 10.1002/cta.2551    ISSN: 0098-9886    » doi

A new method to obtain a continuous and fast measurement of light intensity is presented. It is targeted for Integrate and Fire pixels that pulse with a frequency proportional to illumination. The procedure is intended to speed up the pixel readout of low illuminated pixels. It does not require synchronisation of different digital signals, being compatible with continuous pixel operation. The fundamentals of this method are described. Experimental results validating the theory are provided.

Journal Paper - Electronics Letters, vol. 54, no. 8, pp 492-494, 2018 IET
DOI: 10.1049/el.2017.3834    ISSN: 0013-5194    » doi

This paper presents a systematic optimization methodology to achieve an accurate estimation of series inductance of inductors implemented in standard CMOS technologies. Proposed method is based on an optimization procedure which aims to obtain adjustment factors associated to main physical inductor characteristics, allowing to estimate more accurate series inductance values that can be used in design stage. Experimental measurements of diverse square inductor geometries are shown and compared with previous approaches in order to demonstrate and validate presented approach.

Journal Paper - Journal of Electrical Engineering, vol. 69, no. 3, pp 250-254, 2018 HANS PUBLISHERS
ISSN: 1335-3632    

A smart sensor system for cell culture real-time supervision is proposed, allowing for a significant reduction in human effort applied to this type of assay. The approach converts the cell culture under test into a suitable "biological" oscillator. The system enables the remote acquisition and management of the "biological" oscillation signals through a secure web interface. The indirectly observed biological properties are cell growth and cell number, which are straightforwardly related to the measured bio-oscillation signal parameters, i.e., frequency and amplitude. The sensor extracts the information without complex circuitry for acquisition and measurement, taking advantage of the microcontroller features. A discrete prototype for sensing and remote monitoring is presented along with the experimental results obtained from the performed measurements, achieving the expected performance and outcomes.

Journal Paper - Sensors, vol. 18, no. 8, art. 2550, 2018 MDPI
DOI: 10.3390/s18082550    ISSN: 1424-8220    » doi