Hardware implementations of this method therefore enable low-power long-term wireless transmission of multiple site extracellular recordings, with application to wireless BMIs or closed-loop stimulation designs.
In spike sorting systems, front-end electronics is a crucial pre-processing step that not only has a direct impact on detection and sorting accuracy, but also on power and silicon area. In this work, a behavioural front-end model is proposed to assess the impact of the design parameters (including signal-to-noise ratio, filter type/order, bandwidth, converter resolution/rate) on subsequent spike processing. Initial validation of the model is provided by applying a test stimulus to a hardware platform and comparing the measured circuit response to the expected from the behavioural model. Our model is then used to demonstrate the effect of the Analogue Front-End (AFE) on subsequent spike processing by testing established spike detection and sorting methods on a selection of systems reported in the literature. It is revealed that although these designs have a wide variation in design parameters (and thus also circuit complexity), the ultimate impact on spike processing performance is relatively low (10-15%). This can be used to inform the design of future systems to have an efficient AFE whilst also maintaining good processing performance.
Bioelectronic Medicines that modulate the activity patterns on peripheral nerves have promise as a new way of treating diverse medical conditions from epilepsy to rheumatism. Progress in the field builds upon time consuming and expensive experiments in living organisms. To reduce experimentation load and allow for a faster, more detailed analysis of peripheral nerve stimulation and recording, computational models incorporating experimental insights will be of great help. We present a peripheral nerve simulator that combines biophysical axon models and numerically solved and idealised extracellular space models in one environment. We modelled the extracellular space as a three-dimensional resistive continuum governed by the electro-quasistatic approximation of the Maxwell equations. Potential distributions were precomputed in finite element models for different media (homogeneous, nerve in saline, nerve in cuff) and imported into our simulator. Axons, on the other hand, were modelled more abstractly as one-dimensional chains of compartments. Unmyelinated fibres were based on the Hodgkin-Huxley model; for myelinated fibres, we adapted the model proposed by McIntyre et al. in 2002 to smaller diameters. To obtain realistic axon shapes, an iterative algorithm positioned fibres along the nerve with a variable tortuosity fit to imaged trajectories. We validated our model with data from the stimulated rat vagus nerve. Simulation results predicted that tortuosity alters recorded signal shapes and increases stimulation thresholds. The model we developed can easily be adapted to different nerves, and may be of use for Bioelectronic Medicine research in the future.
This is the first study to show pH changes in peripheral nerves in vivo. In addition, the demonstration that iridium oxide microelectrodes are sufficiently pH sensitive as to measure changes in pH associated with physiological stimuli means they have the potential to be integrated into closed-loop neurostimulating devices.
This work describes the preparation of an array of individually addressable pH sensitive microneedles which demonstrated suitable for measuring pH distribution during heart ischemia and reperfusion cycles.
This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation.
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