While there are adequate tools available to simulate Spiking Neural Networks (e.g. Brian2, snnTorch), as well as the tools for simulating robots and their environments, there remains a need for integrated tools that enable researchers to jointly simulate realistic brain models, robots, and sensory-rich environments. This work introduces a comprehensive neuromorphic robotic system, which combines neuromorphic computing with neuromorphic (and conventional) sensory and motor devices. We emulate the neuromorphic computing on a conventional low-power CPU, specifically a Virtual Machine on a Raspberry Pi 5, integrating Python and specialised packages for real-time Spiking Neural Networks (SNN) simulations. We achieve: (i) a cost-effective alternative to dedicated neuromorphic hardware, (ii) built-in GPIO and USB ports for seamless sensor and motor interfacing. We have built a demonstrator system: a robotic goalkeeper, using a DVS camera, a digital servo motor, and a touch sensor for a reward signal. The SNN uses a combination of unsupervised and supervised (reinforcement) learning. The system off-line and on-line learning was demonstrated, and some performance metrics reported.

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%.

In this paper we present the design of a new point-of-care device for protein quantification. The proposed design is based on a novel aptamer-mediated methodology and real time polymerase chain reaction (RT-PCR), a robust and ultrasensitive method for DNA amplification, which we employ for very sensitive quantification of proteins. In addition, we have also developed an algorithm for the processing of raw fluorescence data from the portable RT-PCR device. The algorithm leads to better linearity than a proprietary software from a commercially available RT-PCR machine. The modular nature of the system allows for easy assembly and adjustment towards a variety of biomarkers for applications in disease diagnosis and personalised medicine.

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.

Neural activity results in chemical changes in the extracellular environment such as variation in pH or potassium/sodium ion concentration. Higher signal to noise ratio make neurochemical signals an interesting biomarker for closed-loop neuromodulation systems. For such applications, it is important to reliably classify pH signatures to control stimulation timing and possibly dosage. For example, the activity of the subdiaphragmatic vagus nerve (sVN) branch can be monitored by measuring extracellular neural pH. More importantly, gut hormone cholecystokinin (CCK)-specific activity on the sVN can be used for controllably activating sVN, in order to mimic the gut-brain neural response to food intake. In this paper, we present a convolutional neural network (CNN) based classification system to identify CCK-specific neurochemical changes on the sVN, from non-linear background activity. Here we present a novel feature engineering approach which enables, after training, a high accuracy classification of neurochemical signals using CNN.

Closed loop neuromodulation, where the stimulation is controlled autonomously based on physiological events, has been more effective than open loop techniques. In the few existing closed loop implementations which have a feedback, indirect non-neurophysiological biomarkers have been typically used (e.g. heart rate, stomach distension). Although these biomarkers enable automatic initiation of neural stimulation, they do not enable intelligent control of stimulation dosage. In this paper, we present a novel closed loop neuromodulation System-on-Chip (SoC) based on a dual signal mode that is detecting both electrical and chemical signatures of neural activity. We use vagus nerve stimulation (VNS) as a design case here. Vagal chemical (pH) signal is detected and used for initiating VNS and vagal compound nerve action potential (CNAP) signals are used to determine the stimulation dosage and pattern. Although we used the paradigm of appetite control and neurometabolic therapies for developing the algorithms for neurostimulation control, the SoC described here can be utilised for other types of closed loop neuromodulation implants.

A necessary requirement for chemometric platforms is pre-processing of the acquired chemical signals to remove baseline drift in the signal. The drift could originate from sensor characteristics or from background chemical activity in the surrounding environment. A recent emerging field is neurochemical monitoring to detect and quantify neural activity. In this paper, a resource efficient pre-processing system is presented to remove drift from the acquired neurochemical signal. The drift removal technique is based on baseline manipulation without requiring window based processing. The target application, for demonstration purposes, is the recording of vagal pH signals to enable closed-loop Vagus Nerve Stimulation (VNS). The final design is multiplier-free and results in an Application Specific Integrated Circuit (ASIC) that is 640 μm by 625 μm in area.

An integrated circuit for real-time, simultaneous recording of electrical and chemical(pH) signatures of neural activity is presented. It consists of six recording channels, three for electrical and three for chemical signals. Each channel has a low-noise amplifier which utilizes a new and optimized technique of flicker noise suppression. Our approach is based on a modified switched bias current technique. Both electrical and chemical amplifiers use the same circuit topology and have a common footprint, but the type of amplifier is reconfigured by controlling the bias current, which controls the bandwidth of the amplifier. The chip was fabricated using an AMS 0.35μm and four metal double poly CMOS technology. Measured noise reduction depends on the switching frequency. Bench tests have shown that our technique achieves up to 60% intrinsic 1/f noise suppression by determining an optimal switching frequency, without additional circuit complexity or an increase in transistor and chip area. The amplifier was designed to exhibit a 60 dB closed loop gain for electrical neural signals, and a 20 dB closed loop gain neurochemical signals. Each channel within the design occupied an area of 3.66mm2. This circuit design will be used as an integrated circuit for a real-time bimodal recording of neural activity for closed-loop neuromodulation therapies.

We demonstrate a novel custom designed multichannel amplifier for simultaneous electrical and chemical neural signal recording. The demonstration platform includes the chip, a dedicated printed circuit board and a Graphical User Interface (GUI). The amplifiers are low-noise, low-power, based on switching bias technique for reducing the flicker noise. This allows for much improved sensitivity for both chemical and low-frequency electrical signals. The demonstration is based on externally generated signals derived from our experiments on vagus nerve and uses a Data Acquisition (DAQ) instrumentation, which also outputs a broad spectrum signal to aid the visitor's experience. The GUI allows interaction with the amplifiers and adjustment of parameters such as switching frequency, demonstrating the implemented technique which is a physics driven fundamental approach to noise suppression.

OBJECTIVE: Vagus Nerve Stimulation (VNS) has shown great promise as a potential therapy for a number of conditions, such as epilepsy, depression and for Neurometabolic Therapies, especially for treating obesity. The objective of this study was to characterize the left ventral subdiaphragmatic gastric trunk of vagus nerve (SubDiaGVN) and to analyze the influence of intravenous injection of gut hormone cholecystokinin octapeptide (CCK-8) on compound nerve action potential (CNAP) observed on the same branch, with the aim of understanding the impact of hormones on VNS and incorporating the methods and results into closed loop implant design. METHODS: The cervical region of the left vagus nerve (CerVN) of male Wistar rats was stimulated with electric current and the elicited CNAPs were recorded on the SubDiaGVN under four different conditions: Control (no injection), Saline, CCK1 (100[Formula: see text]pmol/kg) and CCK2 (1000[Formula: see text]pmol/kg) injections. RESULTS: We identified the presence of A[Formula: see text], B, C1, C2, C3 and C4 fibers with their respective velocity ranges. Intravenous administration of CCK in vivo results in selective, statistically significant reduction of CNAP components originating from A and B fibers, but with no discernible effect on the C fibers in [Formula: see text] animals. The affected CNAP components exhibit statistically significant ([Formula: see text] and [Formula: see text]) higher normalized stimulation thresholds. CONCLUSION: This approach of characterizing the vagus nerve can be used in closed loop systems to determine when to initiate VNS and also to tune the stimulation dose, which is patient-specific and changes over time.

The mechanisms by which the gain of the neuronal input-output function may be modulated have been the subject of much investigation. However, little is known of the role of dendrites in neuronal gain control. New optogenetic experimental paradigms based on spatial profiles or patterns of light stimulation offer the prospect of elucidating many aspects of single cell function, including the role of dendrites in gain control. We thus developed a model to investigate how competing excitatory and inhibitory input within the dendritic arbor alters neuronal gain, incorporating kinetic models of opsins into our modeling to ensure it is experimentally testable. To investigate how different topologies of the neuronal dendritic tree affect the neuron's input-output characteristics we generate branching geometries which replicate morphological features of most common neurons, but keep the number of branches and overall area of dendrites approximately constant. We found a relationship between a neuron's gain modulability and its dendritic morphology, with neurons with bipolar dendrites with a moderate degree of branching being most receptive to control of the gain of their input-output relationship. The theory was then tested and confirmed on two examples of realistic neurons: 1) layer V pyramidal cells-confirming their role in neural circuits as a regulator of the gain in the circuit in addition to acting as the primary excitatory neurons, and 2) stellate cells. In addition to providing testable predictions and a novel application of dual-opsins, our model suggests that innervation of all dendritic subdomains is required for full gain modulation, revealing the importance of dendritic targeting in the generation of neuronal gain control and the functions that it subserves. Finally, our study also demonstrates that neurophysiological investigations which use direct current injection into the soma and bypass the dendrites may miss some important neuronal functions, such as gain modulation.

Conventional synchronous imaging sensor provides frame-based video with a relatively high degree of temporal redundancy. On the other hand, activity-driven, event-based imaging sensor provides low resolution, monochromatic video feeds with low latency. This paper aims to integrate the output from both camera systems to leverage on the strengths of both imaging sensors. We describe and demonstrate various video processing applications achieved using the combined camera system. The applications include a novel video-compression scheme, foveated imaging on the moving objects, object tracking and velocity estimation. All demonstrations are achieved through the integration of data outputs from the Dynamic Vision Sensor (DVS128) and conventional frame-based QVGA (320×240) PS3-Eye webcam, in the jAER software.

This paper identifies and addresses key design considerations and trade-offs in the implementation of a CMOS high-resolution microheater array for Lab-on-Chip (LOC) applications. Specifically, this is investigated in the context of facilitating the in vitro thermal stimulation of single neurons. The paper analyses the electro-thermal response (by means of COMSOL simulations) and reliability issues (such as melting and electromigration) of different microheater designs. The analysis shows that a small-area heater is more efficient in terms of power, but it has more reliability problems essentially due to electromigration effects. For the proposed heater designs, the expected lifetime is a few days (in continuous operation) in the worst scenario, which is still generally acceptable for LOC applications.

This demonstration shows how an inexpensive high frame-rate USB camera is used to emulate existing and proposed activity-driven event-based vision sensors. A PS3-Eye camera which runs at a maximum of 125 frames/second with colour QVGA (320×240) resolution is used to emulate several event-based vision sensors, including a Dynamic Vision Sensor (DVS), a colour-change sensitive DVS (cDVS), and a hybrid vision sensor with DVS+cDVS pixels. The emulator is integrated into the jAER software project for event-based real-time vision and is used to study use cases for future vision sensor designs.

Recently we have completed a system level modelling of the G-protein coupled cascade in Drosophila photoreceptors that converts single photons into transient electrical responses. Many interesting properties were revealed including the underlying mechanisms by which the system generates high quantum efficiency, single photon responses, huge signal amplification and fast recovery, as well as light adaptation to 11 orders of magnitude of light intensities. Now we would like to use this enzymatic cascade model as a design blueprint for a cascade of analogue amplifiers. These circuits can then be used in very sensitive sensory systems such as imaging chips or uncooled infrared detectors and cameras. This paper represents the first phase in this quest, which is establishing the link between the phototransduction model and an engineering design, before we move on to the detailed circuit design realization (phase two).