Bioelectronic medicine is driving the need for neuromorphic microcircuits that integrate raw nervous stimuli and respond identically to biological neurons. However, designing such circuits remains a challenge. Here we estimate the parameters of highly nonlinear conductance models and derive the ab initio equations of intracellular currents and membrane voltages embodied in analog solid-state electronics. By configuring individual ion channels of solid-state neurons with parameters estimated from large-scale assimilation of electrophysiological recordings, we successfully transfer the complete dynamics of hippocampal and respiratory neurons in silico. The solid-state neurons are found to respond nearly identically to biological neurons under stimulation by a wide range of current injection protocols. The optimization of nonlinear models demonstrates a powerful method for programming analog electronic circuits. This approach offers a route for repairing diseased biocircuits and emulating their function with biomedical implants that can adapt to biofeedback.

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The study presents a thalamocortical network model which oscillates within the alpha frequency band (8–13 Hz) as recorded in the wakeful relaxed state with closed eyes to study the neural causes of abnormal oscillatory activity in Alzheimer's disease (AD). Incorporated within the model are various types of cortical excitatory and inhibitory neurons, recurrently connected to thalamic and reticular thalamic regions with the ratios and distances derived from the mammalian thalamocortical system. The model is utilized to study the impacts of four types of connectivity loss on the model's spectral dynamics. The study focuses on investigating degeneration of corticocortical, thalamocortical, corticothalamic, and corticoreticular couplings, with an emphasis on the influence of each modeled case on the spectral output of the model. Synaptic compensation has been included in each model to examine the interplay between synaptic deletion and compensation mechanisms, and the oscillatory activity of the network. The results of power spectra and event related desynchronization/synchronization (ERD/S) analyses show that the dynamics of the thalamic and cortical oscillations are significantly influenced by corticocortical synaptic loss. Interestingly, the patterns of changes in thalamic spectral activity are correlated with those in the cortical model. Similarly, the thalamic oscillatory activity is diminished after partial corticothalamic denervation. The results suggest that thalamic atrophy is a secondary pathology to cortical shrinkage in Alzheimer's disease. In addition, this study finds that the inhibition from neurons in the thalamic reticular nucleus (RTN) to thalamic relay (TCR) neurons plays a key role in regulating thalamic oscillations; disinhibition disrupts thalamic oscillatory activity even though TCR neurons are more depolarized after being released from RTN inhibition. This study provides information that can be explored experimentally to further our understanding on the neurodegeneration associated with AD pathology.

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Abstract-Tuberculosis (TB) remains one of the most devastating infectious diseases and its treatment efficiency is majorly influenced by the stage at which infection with the TB bacterium is diagnosed. The available methods for TB diagnosis are either time consuming, costly or not efficient. This study employs a signal generation mechanism for biosensing, known as Plasmonic ELISA, and computational intelligence to facilitate automatic diagnosis of TB. Plasmonic ELISA enables the detection of a few molecules of analyte by the incorporation of smart nanomaterials for better sensitivity of the developed detection system. The computational system uses k-means clustering and thresholding for image segmentation. This paper presents the results of the classification performance of the Plasmonic ELISA imaging data by using various types of classifiers. The five-fold cross-validation results show high accuracy rate (>97%) in classifying TB images using the entire data set. Future work will focus on developing an intelligent mobile-enabled expert system to diagnose TB in real-time. The intelligent system will be clinically validated and tested in collaboration with healthcare providers in Malaysia.

Confirming that synaptic loss is directly related to cognitive deficit in Alzheimer's disease (AD) has been the focus of many studies. Compensation mechanisms counteract synaptic loss and prevent the catastrophic amnesia induced by synaptic loss via maintaining the activity levels of neural circuits. Here we investigate the interplay between various synaptic degeneration and compensation mechanisms, and abnormal cortical oscillations based on a large-scale network model consisting of 100,000 neurons exhibiting several cortical firing patterns, 8.5 million synapses, short-term plasticity, axonal delays and receptor kinetics. The structure of the model is inspired by the anatomy of the cerebral cortex. The results of the modelling study suggest that cortical oscillations respond differently to compensation mechanisms. Local compensation preserves the baseline activity of theta (5-7 Hz) and alpha (8-12 Hz) oscillations whereas delta (1-4 Hz) and beta (13-30 Hz) oscillations are maintained via global compensation. Applying compensation mechanisms independently shows greater effects than combining both compensation mechanisms in one model and applying them in parallel. Consequently, it can be speculated that enhancing local compensation might recover the neural processes and cognitive functions that are associated with theta and alpha oscillations whereas inducing global compensation might contribute to the repair of neural (cognitive) processes which are associated with delta and beta band activity. Compensation mechanisms may vary across cortical regions and the activation of inappropriate compensation mechanism in a particular region may fail to recover network dynamics and/or induce secondary pathological changes in the network.

This paper describes an investigation into the pathophysiological causes of abnormal cortical oscillations in Alzheimer's disease (AD) using two heterogeneous neuronal network models. The effect of excitatory circuit disruption on the beta band power (13-30 Hz) using a conductance-based network model of 200 neurons is assessed. Then, the neural correlates of abnormal cortical oscillations in different frequency bands based on a larger network model of 1000 neurons consisting of different types of cortical neurons are also analyzed. EEG studies in AD patients have shown that beta band power (13-30 Hz) decreased in the early stages of the disease with a parallel increase in theta band power (4-7 Hz). This abnormal change progresses with the later stages of the disease but with decreased power spectra in other fast frequency bands plus an increase in delta band power (1-3 Hz). Our results show that, despite the heterogeneity of the network models, the beta band power is significantly affected by excitatory neural and synaptic loss. Second, the results of modeling a functional impairment in the excitatory circuit shows that beta band power exhibits the most decrease compared with other bands. Previous biological experiments on different types of cultural excitatory neurons show that cortical neuronal death is mediated by dysfunctional ionic behavior that might specifically contribute to the pathogenesis of β-amyloid-peptide-induced neuronal death in AD. Our study also shows that beta band power was the first affected component when the modeled excitatory circuit begins to lose neurons and synapses.

The colourimetric analysis has been used in diversified fields for years. This paper provides a unique overview of colourimetric tests from the perspective of computer vision by describing different aspects of a colourimetric test in the context of image processing, followed by an investigation into the development of a colorimetric assay type detection system using advanced machine learning algorithms. To the best of our knowledge, this is the first attempt to define colourimetric assay types from the eyes of a machine and perform any colorimetric test using deep learning. This investigation utilizes the state-of-the-art pre-trained models of Convolutional Neural Network (CNN) to perform the assay type detection of an enzyme-linked immunosorbent assay (ELISA) and lateral flow assay (LFA). The ELISA dataset contains images of both positive and negative samples, prepared for the plasmonic ELISA based TB-antigen specific antibody detection. The LFA dataset contains images of the universal pH indicator paper of eight pH levels. It is noted that the pre-trained models offered 100% accurate visual recognition for the assay type detection. Such detection can assist novice users to initiate a colorimetric test using his/her personal digital devices. The assay type detection can also aid in calibrating an image-based colorimetric classification.