Abstract Objective. All motor commands flow through motoneurons, which entrain control of their innervated muscle fibers, forming a unit (MU). Owing to the high fidelity action potentials within MUs, discharge profiles detail organization ionotropic excitatory/inhibitory as well metabotropic neuromodulatory motoneurons. Neuromodulatory inputs (e.g. norepinephrine, serotonin) enhance motoneuron excitability and facilitate persistent inward currents (PICs). PICs introduce quantifiable...

In this study, we develop new reverse engineering (RE) techniques to identify the organization of synaptic inputs generating firing patterns populations neurons. We tested these in silico allow rigorous evaluation their effectiveness, using remarkably extensive parameter searches enabled by massively-parallel computation on supercomputers. chose spinal motoneurons as our target neural system, since process all motor commands and have well-established input-output properties. One set...

Design and optimization of statistical models for use in methods estimating radiofrequency ablation (RFA) lesion depths soft real-time performance.Using tissue multi-frequency complex electrical impedance data collected from a low-cost embedded system, deep neural network (NN) tree-based ensembles (TEs) were trained the RFA depth via regression.Addition frequency sweep data, previous RF power state boosted accuracy models. The root mean square errors 2 mm NN 0.5 TEs 0.4 0.04 presented this...

Background: Radiofrequency ablation (RFA), a method of inducing thermal (cell death), is often used to destroy tumours or potentially cancerous tissue. Current techniques for RFA estimation (electrical impedance tomography, Nakagami ultrasound, etc.) require long compute times (≥ 2 s) and measurement devices other than the device. This study aims determine if neural network (NN) can estimate lesion depth control bipolar using complex electrical – since tissue conductivity varies as function...

Background: Radiofrequency ablation is a minimally-invasive treatment method that aims to destroy undesired tissue by exposing it alternating current in the 100 kHz–800 kHz frequency range and heating until destroyed via coagulative necrosis. Ablation gaining momentum especially cancer research, where malignant tumor. While ablating tumor with an electrode or catheter easy task, real-time monitoring process must order maintain reliability of treatment. Common methods for this task have...

One of the most common types models that helps us to understand neuron behavior is based on Hodgkin–Huxley ion channel formulation (HH model). A major challenge with inferring parameters in HH non-uniqueness: many different sets parameter values produce similar outputs for same input stimulus. Such phenomena result an objective function exhibits multiple modes (i.e., local minima). This non-uniqueness optimality poses challenges estimation algorithmic optimization techniques. additionally...

Radiofrequency ablation (RFA) is a popular modality for tumor treatment. However, inexpensive real-time monitoring of RFA within multiple tissue types still an ongoing research topic. The objective this study to utilize multi-frequency electrical impedance data depth estimation through fusion schemes that include non-linear machine learning (ML) models. Multi-frequency complex measurements are used provide input the schemes. Our results show significantly decrease both spread residuals and...

In this study, we develop new reverse engineering (RE) techniques to identify the organization of synaptic inputs generating firing patterns populations neurons. We tested these in silico allow rigorous evaluation their effectiveness, using remarkably extensive parameter searches enabled by massively-parallel computation on supercomputers. chose spinal motoneurons as our target neural system, since process all motor commands and have well-established input-output properties. One set...

In this study, we develop new reverse engineering (RE) techniques to identify the organization of synaptic inputs generating firing patterns populations neurons. We tested these in silico allow rigorous evaluation their effectiveness, using remarkably extensive parameter searches enabled by massively-parallel computation on supercomputers. chose spinal motoneurons as our target neural system, since process all motor commands and have well established input-output properties. One set...

Abstract In this study, we develop new reverse engineering (RE) techniques to identify the organization of synaptic inputs generating firing patterns populations neurons. We tested these in silico allow rigorous evaluation their effectiveness, using remarkably extensive parameter searches enabled by massively-parallel computation on supercomputers. chose spinal motoneurons as our target neural system, since process all motor commands and have well established input-output properties. One set...

Abstract Experimental data-based parameter search for Hodgkin–Huxley-style (HH) neuron models is a major challenge neuroscientists and neuroengineers. Current strategies are often computationally expensive, slow to converge, have difficulty handling nonlinearities or multimodalities in the objective function, require good initial guesses. Most important, many existing approaches lack quantification of uncertainties estimates even though such immense biological significance. We propose novel...

Following spinal cord injury (SCI), sensory and motor functions are severely disrupted yet the circuitry below site continues to maintain active functional neuronal properties (Edgerton, 2004). The is endogenously capable of several forms adaptive plasticity, including re-training with exercise, instrumental, Pavlovian learning. paw withdrawal learning (PaWL) paradigm represents a simple instrumental model (Jindrich 2019). Briefly, in mice whose cords completely transected (ST) at...

In this study, we develop new reverse engineering (RE) techniques to identify the organization of synaptic inputs generating firing patterns populations neurons. We tested these in silico allow rigorous evaluation their effectiveness, using remarkably extensive parameter searches enabled by massively-parallel computation on supercomputers. chose spinal motoneurons as our target neural system, since process all motor commands and have well established input-output properties. One set...