Sparse Coding Using the Locally Competitive Algorithm on the TrueNorth Neurosynaptic System.

The Locally Competitive Algorithm (LCA) is a biologically plausible computational architecture for sparse coding, where a signal is represented as a linear combination of elements from an over-complete dictionary. In this paper we map the LCA algorithm on the brain-inspired, IBM TrueNorth Neurosynaptic System. We discuss data structures and representation as well as the architecture of functional processing units that perform non-linear threshold, vector-matrix multiplication. We also present the design of the micro-architectural units that facilitate the implementation of dynamical based iterative algorithms. Experimental results with the LCA algorithm using the limited precision, fixed-point arithmetic on TrueNorth compare favorably with results using floating-point computations on a general purpose computer. The scaling of the LCA algorithm within the constraints of the TrueNorth is also discussed.

Miniature atomic scalar magnetometer for space based on the rubidium isotope 87Rb.

A miniature atomic scalar magnetometer based on the rubidium isotope 87Rb was developed for operation in space. The instrument design implements both Mx and Mz mode operation and leverages a novel microelectromechanical system (MEMS) fabricated vapor cell and a custom silicon-on-sapphire (SOS) complementary metal-oxide-semiconductor (CMOS) integrated circuit. The vapor cell has a volume of only 1 mm3 so that it can be efficiently heated to its operating temperature by a specially designed, low-magnetic-field-generating resistive heater implemented in multiple metal layers of the transparent sapphire substrate of the SOS-CMOS chips. The SOS-CMOS chip also hosts the Helmholtz coil and associated circuitry to stimulate the magnetically sensitive atomic resonance and temperature sensors. The prototype instrument has a total mass of fewer than 500 g and uses less than 1 W of power, while maintaining a sensitivity of 15 pT/√Hz at 1 Hz, comparable to present state-of-the-art absolute magnetometers.

A CMOS Neural Interface for a Multichannel Vestibular Prosthesis.

We present a high-voltage CMOS neural-interface chip for a multichannel vestibular prosthesis (MVP) that measures head motion and modulates vestibular nerve activity to restore vision- and posture-stabilizing reflexes. This application specific integrated circuit neural interface (ASIC-NI) chip was designed to work with a commercially available microcontroller, which controls the ASIC-NI via a fast parallel interface to deliver biphasic stimulation pulses with 9-bit programmable current amplitude via 16 stimulation channels. The chip was fabricated in the ONSemi C5 0.5 micron, high-voltage CMOS process and can accommodate compliance voltages up to 12 V, stimulating vestibular nerve branches using biphasic current pulses up to 1.45±0.06 mA with durations as short as 10 µs/phase. The ASIC-NI includes a dedicated digital-to-analog converter for each channel, enabling it to perform complex multipolar stimulation. The ASIC-NI replaces discrete components that cover nearly half of the 2nd generation MVP (MVP2) printed circuit board, reducing the MVP system size by 48% and power consumption by 17%. Physiological tests of the ASIC-based MVP system (MVP2A) in a rhesus monkey produced reflexive eye movement responses to prosthetic stimulation similar to those observed when using the MVP2. Sinusoidal modulation of stimulus pulse rate from 68-130 pulses per second at frequencies from 0.1 to 5 Hz elicited appropriately-directed slow phase eye velocities ranging in amplitude from 1.9-16.7 °/s for the MVP2 and 2.0-14.2 °/s for the MVP2A. The eye velocities evoked by MVP2 and MVP2A showed no significant difference ( t-test, p=0.34), suggesting that the MVP2A achieves performance at least as good as the larger MVP2.

Do audio-visual motion cues promote segregation of auditory streams?

An audio-visual experiment using moving sound sources was designed to investigate whether the analysis of auditory scenes is modulated by synchronous presentation of visual information. Listeners were presented with an alternating sequence of two pure tones delivered by two separate sound sources. In different conditions, the two sound sources were either stationary or moving on random trajectories around the listener. Both the sounds and the movement trajectories were derived from recordings in which two humans were moving with loudspeakers attached to their heads. Visualized movement trajectories modeled by a computer animation were presented together with the sounds. In the main experiment, behavioral reports on sound organization were collected from young healthy volunteers. The proportion and stability of the different sound organizations were compared between the conditions in which the visualized trajectories matched the movement of the sound sources and when the two were independent of each other. The results corroborate earlier findings that separation of sound sources in space promotes segregation. However, no additional effect of auditory movement per se on the perceptual organization of sounds was obtained. Surprisingly, the presentation of movement-congruent visual cues did not strengthen the effects of spatial separation on segregating auditory streams. Our findings are consistent with the view that bistability in the auditory modality can occur independently from other modalities.

Visualizing and quantifying charge distributions correlated to threshold voltage shifts in lateral organic transistors.

Lateral organic field-effect transistors (OFETs), consisting of a polystyrene (PS) polymer gate material and a pentacene organic semiconductor (OSC), were electrically polarized from bias stress during operation or in a separate charging step, and investigated with scanning Kelvin probe microscopy (SKPM) and current-voltage determinations. The charge storage inside the polymer was indicated, without any alteration of the OFET, as a surface voltage with SKPM, and correlated to a threshold voltage (VT) shift in the transistor operation. The SKPM method allows the gate material/OSC interface of the OFET to be visualized and the surface voltage variation between the two gate material interfaces to be mapped. The charge distribution for three samples was derived from the surface voltage maps using Poisson's equation. Charge densities calculated this way agreed with those derived from the VT shifts and the lateral gate-OSC capacitance. We also compared the behavior of two other polymers with PS: PS accepted the most static charge in its entire volume, poly(2-trifluoromethylstyrene) (F-PS) had the most stability to bias stress, and poly(methyl methacrylate) (PMMA) showed the most leakage current and least consistent response to static charging of the three polymers. This work provides a clear demonstration that surface voltage on a working OFET gate material can be related to the quantity of static charge responsible for bias stress and nonvolatility in OFETs.

Multimodal integration of micro-Doppler sonar and auditory signals for behavior classification with convolutional networks.

The ability to recognize the behavior of individuals is of great interest in the general field of safety (e.g. building security, crowd control, transport analysis, independent living for the elderly). Here we report a new real-time acoustic system for human action and behavior recognition that integrates passive audio and active micro-Doppler sonar signatures over multiple time scales. The system architecture is based on a six-layer convolutional neural network, trained and evaluated using a dataset of 10 subjects performing seven different behaviors. Probabilistic combination of system output through time for each modality separately yields 94% (passive audio) and 91% (micro-Doppler sonar) correct behavior classification; probabilistic multimodal integration increases classification performance to 98%. This study supports the efficacy of micro-Doppler sonar systems in characterizing human actions, which can then be efficiently classified using ConvNets. It also demonstrates that the integration of multiple sources of acoustic information can significantly improve the system's performance.

Design of silicon brains in the nano-CMOS era: spiking neurons, learning synapses and neural architecture optimization.

We present a design framework for neuromorphic architectures in the nano-CMOS era. Our approach to the design of spiking neurons and STDP learning circuits relies on parallel computational structures where neurons are abstracted as digital arithmetic logic units and communication processors. Using this approach, we have developed arrays of silicon neurons that scale to millions of neurons in a single state-of-the-art Field Programmable Gate Array (FPGA). We demonstrate the validity of the design methodology through the implementation of cortical development in a circuit of spiking neurons, STDP synapses, and neural architecture optimization.

Distortion of neural signals by spike coding.

Analog neural signals must be converted into spike trains for transmission over electrically leaky axons. This spike encoding and subsequent decoding leads to distortion. We quantify this distortion by deriving approximate expressions for the mean square error between the inputs and outputs of a spiking link. We use integrate-and-fire and Poisson encoders to convert naturalistic stimuli into spike trains and spike count and inter-spike interval decoders to generate reconstructions of the stimulus. The distortion expressions enable us to compare these spike coding schemes over a large parameter space. We verify that the integrate-and-fire encoder is more effective than the Poisson encoder. The disparity between the two encoders diminishes as the stimulus coefficient of variation (CV) increases, at which point, the variability attributed to the stimulus overwhelms the variability attributed to Poisson statistics. When the stimulus CV is small, the interspike interval decoder is superior, as the distortion resulting from spike count decoding is dominated by a term that is attributed to the discrete nature of the spike count. In this regime, additive noise has a greater impact on the interspike interval decoder than the spike count decoder. When the stimulus CV is large, the average signal excursion is much larger than the quantization step size, and spike count decoding is superior.

Acoustic micro-Doppler radar for human gait imaging.

A portable acoustic micro-Doppler radar system for the acquisition of human gait signatures in indoor and outdoor environments is reported. Signals from an accelerometer attached to the leg support the identification of the components in the measured micro-Doppler signature. The acoustic micro-Doppler system described in this paper is simpler and offers advantages over the widely used electromagnetic wave micro-Doppler radars.

Hybrid silicon/silicone (polydimethylsiloxane) microsystem for cell culture.

We discuss the design, fabrication and testing of a hybrid microsystem for stand-alone cell culture and incubation. The micro-incubator is engineered through the integration of a silicon CMOS die for the heater and temperature sensor, with multilayer silicone PDMS (polydimethylsiloxane) structures namely, fluidic channels and a 4 mm diameter, 30 microL, culture well. A 25 micron thick PDMS membrane covers the top of the culture well, acting as barrier to contaminants while allowing the cells to exchange gases with the ambient environment. The packaging for the microsystem includes a flexible polyimide electronic ribbon cable and four fluidic ports that provide external interfaces to electrical energy, closed loop sensing and electronic control as well as solid and liquid supplies. The complete structure has a size of (2.5x2.5x0.6 cm3). We have employed the device to successfully culture BHK-21 cells autonomously over a sixty hour period in ambient environment.