Computational neuroscience meets optogenetics: Unlocking the brain’s secrets

Working at the interface between engineering and neuroscience, Professor Simon Schultz and Dr Konstantin Nikolic at Imperial College London are developing tools to help us understand the intricate workings of the brain. Combining a revolutionary technology – optogenetics – with computational neuroscience, the team are developing powerful tools to study the real-time physiology of individual neurons and their function. Their work is unlocking the complex secrets of the circuitry of the brain and has significance for debilitating neural disorders such as Alzheimer’s disease

Guest editorial: Special issue on selected papers from IEEE BioCAS 2018

The papers in this special section were presented at the 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS 2018) that was held in in Cleveland, OH, from October 17–19, 2018

Neuromorphic robotic platform with visual input, processor and actuator, based on spiking neural networks

This paper describes the design and modus of operation of a neuromorphic robotic platform based on SpiNNaker, and its implementation on the goalkeeper task. The robotic system utilises an address event representation (AER) type of camera (dynamic vision sensor (DVS)) to capture features of a moving ball, and a servo motor to position the goalkeeper to intercept the incoming ball. At the backbone of the system is a microcontroller (Arduino Due) which facilitates communication and control between different robot parts. A spiking neuronal network (SNN), which is running on SpiNNaker, predicts the location of arrival of the moving ball and decides where to place the goalkeeper. In our setup, the maximum data transmission speed of the closed-loop system is approximately 3000 packets per second for both uplink and downlink, and the robot can intercept balls whose speed is up to 1 m/s starting from the distance of about 0.8 m. The interception accuracy is up to 85%, the response latency is 6.5 ms and the maximum power consumption is 7.15W. This is better than previous implementations based on PC. Here, a simplified version of an SNN has been developed for the ‘interception of a moving object’ task, for the purpose of demonstrating the platform, however a generalised SNN for this problem is a nontrivial problem. A demo video of the robot goalie is available on YouTube

On the Emergent "Quantum" Theory in Complex Adaptive Systems

We explore the concept of emergent quantum-like theory in complex adaptive systems, and examine in particular the concrete example of such an emergent (or "mock") quantum theory in the Lotka-Volterra system. In general, we investigate the possibility of implementing the mathematical formalism of quantum mechanics on classical systems, and what would be the conditions for using such an approach. We start from a standard description of a classical system via Hamilton-Jacobi (HJ) equation and reduce it to an effective Schr\"odinger-type equation, with a (mock) Planck constant \mockbar, which is system-dependent. The condition for this is that the so-called quantum potential VQ, which is state-dependent, is cancelled out by some additional term in the HJ equation. We consider this additional term to provide for the coupling of the classical system under consideration to the "environment." We assume that a classical system could cancel out the VQ term (at least approximately) by fine tuning to the environment. This might provide a mechanism for establishing a stable, stationary states in (complex) adaptive systems, such as biological systems. In this context we emphasize the state dependent nature of the mock quantum dynamics and we also introduce the new concept of the mock quantum, state dependent, statistical field theory. We also discuss some universal features of the quantum-to-classical as well as the mock-quantum-to-classical transition found in the turbulent phase of the hydrodynamic formulation of our proposal. In this way we reframe the concept of decoherence into the concept of "quantum turbulence," i.e. that the transition between quantum and classical could be defined in analogy to the transition from laminar to turbulent flow in hydrodynamics.Comment: 20 pages, 2 figure

Quadratic Mutual Information estimation of mouse dLGN receptive fields reveals asymmetry between ON and OFF visual pathways

The longstanding theory of “parallel processing” predicts that, except for a sign reversal, ON and OFF cells are driven by a similar pre-synaptic circuit and have similar visual field coverage, direction/orientation selectivity, visual acuity and other functional properties. However, recent experimental data challenges this view. Here we present an information theory based receptive field (RF) estimation method - quadratic mutual information (QMI) - applied to multi-electrode array electrophysiological recordings from the mouse dorsal lateral geniculate nucleus (dLGN). This estimation method provides more accurate RF estimates than the commonly used Spike-Triggered Average (STA) method, particularly in the presence of spatially correlated inputs. This improved efficiency allowed a larger number of RFs (285 vs 189 cells) to be extracted from a previously published dataset. Fitting a spatial-temporal Difference-of-Gaussians (ST-DoG) model to the RFs revealed that while the structural RF properties of ON and OFF cells are largely symmetric, there were some asymmetries apparent in the functional properties of ON and OFF visual processing streams - with OFF cells preferring higher spatial and temporal frequencies on average, and showing a greater degree of orientation selectivity

Unsupervised Classification of Human Activity with Hidden Semi-Markov Models

The modern sedentary lifestyle is negatively influencing human health, and the current guidelines recommend at least 150 min of moderate activity per week. However, the challenge is how to measure human activity in a practical way. While accelerometers are the most common tools to measure activity, current activity classification methods require calibration studies or labelled datasets—requirements that slow the research progress. Therefore, there is a pressing need to classify and quantify human activity efficiently. In this work, we propose an unsupervised approach to classify activities from accelerometer data using hidden semi-Markov models. We tune and infer the model parameters on accelerometer data from the UK Biobank and select the optimal model based on features used and informativeness of the prior. The best model achieves an average correlation of 0.4 between the inferred activities and the reference ones, with the overall physical activity obtaining a correlation of 0.8. Additionally, to prove the clinical significance of the method, we validate it by performing a linear regression between the inferred activities and anthropometric measures such as BMI and waist circumference. We show that for a sedentary behaviour and total physical activity, the proposed method achieves comparable regression coefficients to the reference labelled dataset. Moreover, the proposed method achieves a good agreement with a labelled dataset for daily time spent in a sedentary behaviour and total physical activity. The unsupervised nature of the method allows for a data-driven classification that does not require calibration studies or labelled datasets and can thus facilitate both clinical research as well as lifestyle recommendations

The spatial pattern of light determines the kinetics and modulates backpropagation of optogenetic action potentials

Optogenetics offers an unprecedented ability to spatially target neuronal stimulations. This study investigated via simulation, for the first time, how the spatial pattern of excitation affects the response of channelrhodopsin-2 (ChR2) expressing neurons. First we described a methodology for modeling ChR2 in the NEURON simulation platform. Then, we compared four most commonly considered illumination strategies (somatic, dendritic, axonal and whole cell) in a paradigmatic model of a cortical layer V pyramidal cell. We show that the spatial pattern of illumination has an important impact on the efficiency of stimulation and the kinetics of the spiking output. Whole cell illumination synchronizes the depolarization of the dendritic tree and the soma and evokes spiking characteristics with a distinct pattern including an increased bursting rate and enhanced back propagation of action potentials (bAPs). This type of illumination is the most efficient as a given irradiance threshold was achievable with only 6 % of ChR2 density needed in the case of somatic illumination. Targeting only the axon initial segment requires a high ChR2 density to achieve a given threshold irradiance and a prolonged illumination does not yield sustained spiking. We also show that patterned illumination can be used to modulate the bAPs and hence spatially modulate the direction and amplitude of spike time dependent plasticity protocols. We further found the irradiance threshold to increase in proportion to the demyelination level of an axon, suggesting that measurements of the irradiance threshold (for example relative to the soma) could be used to remotely probe a loss of neural myelin sheath, which is a hallmark of several neurodegenerative diseases

Pattern Recognition Spiking Neural Network for Classification of Chinese Characters

The Spiking Neural Networks (SNNs) are biologically more realistic than other types of Artificial Neural Networks (ANNs), but they have been much less utilised in applications. When comparing the two types of NNs, the SNNs are considered to be of lower latency, more hardware-friendly and energy-efficient, and suitable for running on portable devices with weak computing performance. In this paper we aim to use an SNN for the task of classifying Chinese character images, and test its performance. The network utilises inhibitory synapses for the purpose of using unsupervised learning. The learning algorithm is a derivative of the traditional Spike-timing-dependent Plasticity (STDP) learning rule. The input images are first pre-processed by traditional methods (OpenCV).Different hyperparameters configurations are tested reaching an optimal configuration and a classification accuracy rate of 93%

On the Emergent "Quantum" Theory in Complex Adaptive Systems

We explore the concept of emergent quantum-like theory in complex adaptive systems, and examine in particular the concrete example of such an emergent (or "mock") quantum theory in the Lotka-Volterra system. In general, we investigate the possibility of implementing the mathematical formalism of quantum mechanics on classical systems, and what would be the conditions for using such an approach. We start from a standard description of a classical system via Hamilton-Jacobi (HJ) equation and reduce it to an effective Schrödinger-type equation, with a (mock) Planck constant \mockbar, which is system-dependent. The condition for this is that the so-called quantum potential VQ, which is state-dependent, is cancelled out by some additional term in the HJ equation. We consider this additional term to provide for the coupling of the classical system under consideration to the "environment." We assume that a classical system could cancel out the VQ term (at least approximately) by fine tuning to the environment. This might provide a mechanism for establishing a stable, stationary states in (complex) adaptive systems, such as biological systems. In this context we emphasize the state dependent nature of the mock quantum dynamics and we also introduce the new concept of the mock quantum, state dependent, statistical field theory. We also discuss some universal features of the quantum-to-classical as well as the mock-quantum-to-classical transition found in the turbulent phase of the hydrodynamic formulation of our proposal. In this way we reframe the concept of decoherence into the concept of "quantum turbulence," i.e. that the transition between quantum and classical could be defined in analogy to the transition from laminar to turbulent flow in hydrodynamics

Signal identification of DNA amplification curves in custom-PCR platforms

Custom-made, point-of-care PCR platforms are a necessary tool for rapid, point-of-care diagnostics in situations such as the current Covid-19 pandemic. However, a common issue faced by them is noisy fluorescence signals, which consist of a drifting baseline or noisy sigmoidal curve. This makes automated detection difficult and requires human verification. In this paper, we have tried to use nonlinear fitting for automated classification of PCR waveforms to identify whether amplification has taken place or not. We have presented several novel signal reconstruction techniques based on nonlinear fitting which will enable better pre-processing and automated differentiation of a valid or invalid PCR amplification curve. We have also tried to perform this classification at lower PCR cycles to reduce decision times in diagnostic tests