Simple capacitor-switch model of excitatory and inhibitory neuron with all parts biologically explained allows input fire pattern dependent chaotic oscillations

Real-Time Generation of Hyperbolic Neuronal Spiking Patterns

Neuronal spikes are referred to as the electric activity of neurons (in terms of voltage) in response to various biological events such as the sodium and calcium ionic current channels in the brain. Currently, both biological models as well as mathematical models of neuronal spiking patterns have been introduced in the literature. However, very little attempt has been made to run these models in real-time. With applications ranging from hardware neuromorphic circuit designs, artificial intelligence (AI) architectures, to deep brain stimulation, real-time generation of these models is of particular interest in the brain-inspired computing/architecture and neuro-modulation/stimulation research communities. This paper proposes the development of a framework for generating the hyperbolic based single neuronal spiking patterns in real-time. Simulation results confirm that the generated spikes resemble the existing models of neuronal spiking patterns, with additional real-time run capability as well as the ability to change the parameters on the fly. Clinical relevance-Real-time models of neuronal spiking patterns have significant clinical relevance with respect to applications of neuromorphic/AI chips for medical image processing/computer vision, as well as clinical neuroscience, neuromodulation and neurostimulation such as deep brain stimulation for modulating the abnormal effects of neurological diseases.

A review on magnetic and spintronic neurostimulation: challenges and prospects

In the treatment of neurodegenerative, sensory and cardiovascular diseases, electrical probes and arrays have shown quite a promising success rate. However, despite the outstanding clinical outcomes, their operation is significantly hindered by non-selective control of electric fields. A promising alternative is micromagnetic stimulation (μMS) due to the high permeability of magnetic field through biological tissues. The induced electric field from the time-varying magnetic field generated by magnetic neurostimulators is used to remotely stimulate neighboring neurons. Due to the spatial asymmetry of the induced electric field, high spatial selectivity of neurostimulation has been realized. Herein, some popular choices of magnetic neurostimulators such as microcoils (μcoils) and spintronic nanodevices are reviewed. The neurostimulator features such as power consumption and resolution (aiming at cellular level) are discussed. In addition, the chronic stability and biocompatibility of these implantable neurostimulator are commented in favor of further translation to clinical settings. Furthermore, magnetic nanoparticles (MNPs), as another invaluable neurostimulation material, has emerged in recent years. Thus, in this review we have also included MNPs as a remote neurostimulation solution that overcomes physical limitations of invasive implants. Overall, this review provides peers with the recent development of ultra-low power, cellular-level, spatially selective magnetic neurostimulators of dimensions within micro- to nano-range for treating chronic neurological disorders. At the end of this review, some potential applications of next generation neuro-devices have also been discussed.

Brain information processing capacity modeling

Neurophysiological measurements suggest that human information processing is evinced by neuronal activity. However, the quantitative relationship between the activity of a brain region and its information processing capacity remains unclear. We introduce and validate a mathematical model of the information processing capacity of a brain region in terms of neuronal activity, input storage capacity, and the arrival rate of afferent information. We applied the model to fMRI data obtained from a flanker paradigm in young and old subjects. Our analysis showed that—for a given cognitive task and subject—higher information processing capacity leads to lower neuronal activity and faster responses. Crucially, processing capacity—as estimated from fMRI data—predicted task and age-related differences in reaction times, speaking to the model’s predictive validity. This model offers a framework for modelling of brain dynamics in terms of information processing capacity, and may be exploited for studies of predictive coding and Bayes-optimal decision-making.