A new QRS detector stress test combining temporal jitter and F-score (JF) reveals significant performance differences amongst popular detectors.

QRS detection within an electrocardiogram (ECG) is the basis of virtually any further processing and any error caused by this detection will propagate to further processing stages. However, standard benchmarking procedures of QRS detectors are seriously flawed because they report almost always close to 100% accuracy for any QRS detection algorithm. This is due to the use of large temporal error margins and noise-free ECG databases which grossly overestimate their performance.

Classification of chemically modified red blood cells in microflow using machine learning video analysis.

We classify native and chemically modified red blood cells with an AI based video classifier. Using TensorFlow video analysis enables us to capture not only the morphology of the cell but also the trajectories of motion of individual red blood cells and their dynamics. We chemically modify cells in three different ways to model different pathological conditions and obtain classification accuracies for all three classification tasks of more than 90% between native and modified cells.

AI based image analysis of red blood cells in oscillating microchannels.

The flow dynamics of red blood cells in blood capillaries and in microfluidic channels is complex. Cells can obtain different shapes such as discoid, parachute, slipper-like shapes and various intermediate states depending on flow conditions and their viscoelastic properties. We use artificial intelligence based analysis of red blood cells (RBCs) in an oscillating microchannel to distinguish healthy red blood cells from red blood cells treated with formaldehyde to chemically modify their viscoelastic behavior.

BCI-Walls: A robust methodology to predict if conscious EEG changes can be detected in the presence of artefacts.

Brain computer interfaces (BCI) depend on reliable realtime detection of conscious EEG changes for example to control a video game. However, scalp recordings are contaminated with non-stationary noise, such as facial muscle activity and eye movements. This interferes with the detection process making it potentially unreliable or even impossible.

Real-time noise cancellation with deep learning.

Biological measurements are often contaminated with large amounts of non-stationary noise which require effective noise reduction techniques. We present a new real-time deep learning algorithm which produces adaptively a signal opposing the noise so that destructive interference occurs. As a proof of concept, we demonstrate the algorithm's performance by reducing electromyogram noise in electroencephalograms with the usage of a custom, flexible, 3D-printed, compound electrode.

Differential Hebbian learning with time-continuous signals for active noise reduction.

Spike timing-dependent plasticity, related to differential Hebb-rules, has become a leading paradigm in neuronal learning, because weights can grow or shrink depending on the timing of pre- and post-synaptic signals. Here we use this paradigm to reduce unwanted (acoustic) noise. Our system relies on heterosynaptic differential Hebbian learning and we show that it can efficiently eliminate noise by up to -140 dB in multi-microphone setups under various conditions.

Closed-Loop Deep Learning: Generating Forward Models With Backpropagation.

A reflex is a simple closed-loop control approach that tries to minimize an error but fails to do so because it will always react too late. An adaptive algorithm can use this error to learn a forward model with the help of predictive cues. For example, a driver learns to improve steering by looking ahead to avoid steering in the last minute.

CikA, an Input Pathway Component, Senses the Oxidized Quinone Signal to Generate Phase Delays in the Cyanobacterial Circadian Clock.

The circadian clock is a timekeeping system in most organisms that keeps track of the time of day. The rhythm generated by the circadian oscillator must be constantly synchronized with the environmental day/night cycle to make the timekeeping system truly advantageous. In the cyanobacterial circadian clock, quinone is a biological signaling molecule used for entraining and fine-tuning the oscillator, a process in which the external signals are transduced into biological metabolites that adjust the phase of the circadian oscillation.

On the Interaction of Head and Gaze Control With Acoustic Beam Width of a Simulated Beamformer in a Two-Talker Scenario.

Superdirectional acoustic beamforming technology provides a high signal-to-noise ratio, but potential speech intelligibility benefits to hearing aid users are limited by the way the users move their heads. Steering the beamformer using eye gaze instead of head orientation could mitigate this problem. This study investigated the intelligibility of target speech with a dynamically changing direction when heard through gaze-controlled (GAZE) or head-controlled (HEAD) superdirectional simulated beamformers.

Bipedal robotic walking control derived from analysis of human locomotion.

This paper proposes the design of a bipedal robotic controller where the function between the sensory input and motor output is treated as a black box derived from human data. In order to achieve this, we investigated the causal relationship between ground contact information from the feet and leg muscle activity n human walking and calculated filter functions which transform sensory signals to motor actions. A minimal, nonlinear, and robust control system was created and subsequently analysed by applying it to our bipedal robot RunBot III without any central pattern generators or precise trajectory control.

Real-time estimation of horizontal gaze angle by saccade integration using in-ear electrooculography.

The manuscript proposes and evaluates a real-time algorithm for estimating eye gaze angle based solely on single-channel electrooculography (EOG), which can be obtained directly from the ear canal using conductive ear moulds. In contrast to conventional high-pass filtering, we used an algorithm that calculates absolute eye gaze angle via statistical analysis of detected saccades. The estimated eye positions of the new algorithm were still noisy.

A functional electrical stimulation system for human walking inspired by reflexive control principles.

This study presents an innovative multichannel functional electrical stimulation gait-assist system which employs a well-established purely reflexive control algorithm, previously tested in a series of bipedal walking robots. In these robots, ground contact information was used to activate motors in the legs, generating a gait cycle similar to that of humans. Rather than developing a sophisticated closed-loop functional electrical stimulation control strategy for stepping, we have instead utilised our simple reflexive model where muscle activation is induced through transfer functions which translate sensory signals, predominantly ground contact information, into motor actions.

Reflex control of robotic gait using human walking data.

Control of human walking is not thoroughly understood, which has implications in developing suitable strategies for the retraining of a functional gait following neurological injuries such as spinal cord injury (SCI). Bipedal robots allow us to investigate simple elements of the complex nervous system to quantify their contribution to motor control. RunBot is a bipedal robot which operates through reflexes without using central pattern generators or trajectory planning algorithms.

Formal modeling of robot behavior with learning.

We present formal specification and verification of a robot moving in a complex network, using temporal sequence learning to avoid obstacles. Our aim is to demonstrate the benefit of using a formal approach to analyze such a system as a complementary approach to simulation. We first describe a classical closed-loop simulation of the system and compare this approach to one in which the system is analyzed using formal verification.

Deep brain stimulation of the mediodorsal thalamic nucleus yields increases in the expression of zif-268 but not c-fos in the frontal cortex.

This study explores the regions activated by deep brain stimulation of the mediodorsal thalamic nucleus through examination of immediate early genes as markers of neuronal activation. Stimulation was delivered unilaterally with constant current 100 μs duration pulses at a frequency of 130 Hz delivered at an amplitude of 200 μA for 3h. Brains were removed, sectioned and radio-labelled for the IEGs zif-268 and c-fos.

Proof of concept evaluation of the electroencephalophone as a discriminator between wakefulness and general anaesthesia.

Background: Depth of anaesthesia (DOA) monitors based on the electroencephalogram (EEG) are commonly used in anaesthetic practice. Their technology relies on mathematical analysis of the EEG waveform, generally resulting in a number which corresponds to anaesthetic depth. We have created a novel method of interpreting the EEG, which retains its underlying complexity.

The internal representation of vowel spectra investigated using behavioral response-triggered averaging.

Listeners presented with noise were asked to press a key whenever they heard the vowels [a] or [i:]. The noise had a random spectrum, with levels in 60 frequency bins changing every 0.5 s.

SaBer DBS: a fully programmable, rechargeable, bilateral, charge-balanced preclinical microstimulator for long-term neural stimulation.

To effectively study the mechanisms by which deep brain stimulation (DBS) produces its therapeutic benefit and to evaluate new therapeutic indications, it is vital to administer DBS over an extended period of time in awake, freely behaving animals. To date multiple preclinical stimulators have been designed and described. However, these stimulators have failed to incorporate some of the design criteria necessary to provide a system analogous to those used clinically.

How feedback inhibition shapes spike-timing-dependent plasticity and its implications for recent Schizophrenia models.

It has been shown that plasticity is not a fixed property but, in fact, changes depending on the location of the synapse on the neuron and/or changes of biophysical parameters. Here, we investigate how plasticity is shaped by feedback inhibition in a cortical microcircuit. We use a differential Hebbian learning rule to model spike-timing-dependent plasticity and show analytically that the feedback inhibition shortens the time window for LTD during spike-timing-dependent plasticity but not for LTP.

Behavioral analysis of differential Hebbian learning in closed-loop systems.

Understanding closed loop behavioral systems is a non-trivial problem, especially when they change during learning. Descriptions of closed loop systems in terms of information theory date back to the 1950s, however, there have been only a few attempts which take into account learning, mostly measuring information of inputs. In this study we analyze a specific type of closed loop system by looking at the input as well as the output space.

On the asymptotic equivalence between differential Hebbian and temporal difference learning.

In this theoretical contribution, we provide mathematical proof that two of the most important classes of network learning-correlation-based differential Hebbian learning and reward-based temporal difference learning-are asymptotically equivalent when timing the learning with a modulatory signal. This opens the opportunity to consistently reformulate most of the abstract reinforcement learning framework from a correlation-based perspective more closely related to the biophysics of neurons.

Path-finding in real and simulated rats: assessing the influence of path characteristics on navigation learning.

A large body of experimental evidence suggests that the hippocampal place field system is involved in reward based navigation learning in rodents. Reinforcement learning (RL) mechanisms have been used to model this, associating the state space in an RL-algorithm to the place-field map in a rat. The convergence properties of RL-algorithms are affected by the exploration patterns of the learner.

Mathematical properties of neuronal TD-rules and differential Hebbian learning: a comparison.

A confusingly wide variety of temporally asymmetric learning rules exists related to reinforcement learning and/or to spike-timing dependent plasticity, many of which look exceedingly similar, while displaying strongly different behavior. These rules often find their use in control tasks, for example in robotics and for this rigorous convergence and numerical stability is required. The goal of this article is to review these rules and compare them to provide a better overview over their different properties.

Chained learning architectures in a simple closed-loop behavioural context.

Objective: Living creatures can learn or improve their behaviour by temporally correlating sensor cues where near-senses (e.g., touch, taste) follow after far-senses (vision, smell).