Visual Speech Recognition with Lightweight Psychologically Motivated Gabor Features.

Extraction of relevant lip features is of continuing interest in the visual speech domain. Using end-to-end feature extraction can produce good results, but at the cost of the results being difficult for humans to comprehend and relate to. We present a new, lightweight feature extraction approach, motivated by human-centric glimpse-based psychological research into facial barcodes, and demonstrate that these simple, easy to extract 3D geometric features (produced using Gabor-based image patches), can successfully be used for speech recognition with LSTM-based machine learning.

Can Lionel Messi's brain slow down time passing?

It seems that seeing others in slow-motion by heroes does not belong only to movies. When Lionel Messi plays football, you can hardly see anything from him that other players cannot do. Then why he is not stoppable really? It seems the answer may be that opponents do not have enough time to do what they want; because in Messi's neural system, time passes slower.

Toward a neuromorphic microphone.

Neuromorphic systems are used in variety of circumstances: as parts of sensory systems, for modeling parts of neural systems and for analog signal processing. In the sensory processing domain, neuromorphic systems can be considered in three parts: pre-transduction processing, transduction itself, and post-transduction processing. Neuromorphic systems include transducers for light, odors, and touch but so far neuromorphic applications in the sound domain have used standard microphones for transduction.

Why sharing matters for electrophysiological data analysis.

We present the case for the sharing of electrophysiological datasets and tools for their analysis. Some of the problems, both sociological and technical, associated with improving the sharing of data and analysis tools are discussed. The work that has been done to try to improve data and code sharing in the electrophysiology area is reviewed.

A Power-Efficient Capacitive Read-Out Circuit With Parasitic-Cancellation for MEMS Cochlea Sensors.

This paper proposes a solution for signal read-out in the MEMS cochlea sensors that have very small sensing capacitance and do not have differential sensing structures. The key challenge in such sensors is the significant signal degradation caused by the parasitic capacitance at the MEMS-CMOS interface. Therefore, a novel capacitive read-out circuit with parasitic-cancellation mechanism is developed; the equivalent input capacitance of the circuit is negative and can be adjusted to cancel the parasitic capacitance.

A Bio-Realistic Analog CMOS Cochlea Filter With High Tunability and Ultra-Steep Roll-Off.

This paper presents the design and experimental results of a cochlea filter in analog very large scale integration (VLSI) which highly resembles physiologically measured response of the mammalian cochlea. The filter consists of three specialized sub-filter stages which respectively provide passive response in low frequencies, actively tunable response in mid-band frequencies and ultra-steep roll-off at transition frequencies from pass-band to stop-band. The sub-filters are implemented in balanced ladder topology using floating active inductors.

A neurally inspired musical instrument classification system based upon the sound onset.

Physiological evidence suggests that sound onset detection in the auditory system may be performed by specialized neurons as early as the cochlear nucleus. Psychoacoustic evidence shows that the sound onset can be important for the recognition of musical sounds. Here the sound onset is used in isolation to form tone descriptors for a musical instrument classification task.

Neuromorphic systems: past, present and future.

Neuromorphic systems are implementations in silicon of elements of neural systems. The idea of electronic implementation is not new, but modern microelectronics has provided opportunities for producing systems for both sensing and neural modelling that can be mass produced straightforwardly. We review the the history of neuromorphic systems, and discuss the range of neuromorphic systems that have been developed.

A new spike detection algorithm for extracellular neural recordings.

Signals from extracellular electrodes in neural systems record voltages resulting from activity in many neurons. Detecting action potentials (spikes) in a small number of specific (target) neurons is difficult because many neurons, both near and more distant, contribute to the signal at the electrode. We consider some nearby neurons as target neurons (providing a signal) and all the other contributions to the signal as noise.

A tool for synthesizing spike trains with realistic interference.

Spike detection and spike sorting techniques are often difficult to assess because of the lack of ground truth data (i.e., spike timings for each neuron).

Precision constrained stochastic resonance in a feedforward neural network.

Stochastic resonance (SR) is a phenomenon in which the response of a nonlinear system to a subthreshold information-bearing signal is optimized by the presence of noise. By considering a nonlinear system (network of leaky integrate-and-fire (LIF) neurons) that captures the functional dynamics of neuronal firing, we demonstrate that sensory neurons could, in principle harness SR to optimize the detection and transmission of weak stimuli. We have previously characterized this effect by use of signal-to-noise ratio (SNR).

Robust sound onset detection using leaky integrate-and-fire neurons with depressing synapses.

A biologically inspired technique for detecting onsets in sound is presented. Outputs from a cochlea-like filter are spike coded, in a way similar to the auditory nerve (AN). These AN-like spikes are presented to a leaky integrate-and-fire neuron through a depressing synapse.

Using Beowulf clusters to speed up neural simulations.

Simulation of large neural systems on PCs requires large amounts of memory, and takes a long time. Parallel computers can speed them up. A new form of parallel computer, the Beowulf cluster, is an affordable version.

A Biologically Supported Error-Correcting Learning Rule.

We show that a form of synaptic plasticity recently discovered in slices of the rat visual cortex (Artola . 1990) can support an error-correcting learning rule. The rule increases weights when both pre- and postsynaptic units are highly active, and decreases them when pre-synaptic activity is high and postsynaptic activation is less than the threshold for weight increment but greater than a lower threshold.