Nodal degree centrality in the default mode-like network of the TgF344-AD Alzheimer's disease rat model as a measure of early network alterations.

This study investigates brain network alterations in the default mode-like network (DMLN) at early stages of disease progression in a rat model of Alzheimer's disease (AD) with application in the development of early diagnostic biomarkers of AD in translational studies. Thirteen male TgF344-AD (TG) rats, and eleven male wild-types (WT) littermates underwent longitudinal resting-state fMRI at the age of 4 and 6 months (pre and early-plaque stages of AD). Alterations in connectivity within DMLN were characterized by calculating the nodal degree (ND), a graph theoretical measure of centrality. The ND values of the left CA2 subregion of the hippocampus was found to be significantly lower in the 4-month-old TG cohort compared to the age-matched WT littermates. Moreover, a lower ND value (hypo-connectivity) was observed in the right prelimbic cortex (prL) and basal forebrain in the 6-month-old TG cohort, compared to the same age WT cohort. Indeed, the ND pattern in the DMLN in both TG and WT cohorts showed significant differences across the two time points that represent pre-plaque and early plaque stages of disease progression. Our findings indicate that lower nodal degree (hypo-connectivity) in the left CA2 in the pre-plaque stage of AD and hypo-connectivity between the basal forebrain and the DMLN regions in the early-plaque stage demonstrated differences in comparison to healthy controls. These results suggest that a graph-theoretical measure such as the nodal degree, can characterize brain networks and improve our insights into the mechanisms underlying Alzheimer's disease.

Anodal HD-tDCS on the dominant anterior temporal lobe and dorsolateral prefrontal cortex: clinical results in patients with mild cognitive impairment.

OBJECTIVES: Mild cognitive impairment (MCI) is a neurocognitive disorder in which the cognitive and mental abilities of humans are declined. Transcranial direct-current stimulation (tDCS) is an emerging noninvasive brain stimulation technique aimed at neuromodulation. In this study, we investigate whether high-definition anodal tDCS stimulation (anodal HD-tDCS) in MCI patients in two different brain regions will be effective in improving cognitive function. METHODS: This study was done as a randomized, double-blind clinical trial. Sixty MCI patients (clinically diagnosed by expert neurologists) were randomly divided into three groups. Two groups received 2-mA anodal HD-tDCS for 20 min for 2 weeks (5 consecutive days in each week, 10 days in total). In the first group (twenty patients), the left dorsolateral prefrontal cortex (left DLPFC) was targeted. In the second group (twenty patients), the target zone was the dominant anterior temporal lobe (DATL). The third group (twenty patients) formed the Sham group. The Montreal Cognitive Assessment (MoCA) and Quality of Life in Alzheimer's Disease (QoLAD) were considered as the outcome measures. RESULTS: MCI patients obtained the highest MoCA mean scores in both left DLPFC and DATL groups versus the study baseline 2 weeks after the intervention. In addition, the MoCA mean scores of MCI patients were greater in both intervention groups compared to the Sham group up to 3 months post-stimulation (p-value ≤ 0.05). However, as we moved away from the first stimulation day, a decreasing trend in the MoCA mean scores was observed. Moreover, in the left DLPFC and DATL groups, higher QoLAD mean scores were observed 3-month post-stimulation, highlighting the effectiveness of anodal HD-tDCS in improving the quality of life in MCI patients. CONCLUSION: In this research, it was shown that applying anodal HD-tDCS at left DLPFC and DATL brain regains for two successive weeks improves cognitive function in MCI patients (by obtaining higher values of MoCA scores) up to 3 months after the intervention compared to the Sham group. This illustrates the positive effects of HD-tDCS, as a non-pharmacological intervention, for improving cognitive function and quality of life in MCI patients. SIGNIFICANCE: Two weeks after anodal HD-tDCS of the DLPFC and DATL brain regions, the MCI patients achieved the highest MoCA mean scores compared to the Sham group across all measurement intervals.

Astrocyte's self-repairing characteristics improve working memory in spiking neuronal networks.

Astrocytes play a significant role in the working memory (WM) mechanism, yet their contribution to spiking neuron-astrocyte networks (SNAN) is underexplored. This study proposes a non-probabilistic SNAN incorporating a self-repairing (SR) mechanism through endocannabinoid pathways to facilitate WM function. Four experiments were conducted with different damaging patterns, replicating close-to-realistic synaptic impairments. Simulation results suggest that the SR process enhances WM performance by improving the consistency of neuronal firing. Moreover, the intercellular astrocytic [Ca]2+ transmission via gap junctions improves WM and SR processes. With increasing damage, WM and SR activities initially fail for non-matched samples and then for smaller and minimally overlapping matched samples. Simulation results also indicate that the inclusion of the SR mechanism in both random and continuous forms of damage improves the resilience of the WM by approximately 20%. This study highlights the importance of astrocytes in synaptically impaired networks.

Early classification of Alzheimer's disease phenotype based on hippocampal electrophysiology in the TgF344-AD rat model.

The hippocampus plays a vital role in navigation, learning, and memory, and is affected in Alzheimer's disease (AD). This study investigated the classification of AD-transgenic rats versus wild-type littermates using electrophysiological activity recorded from the hippocampus at an early, presymptomatic stage of the disease (6 months old) in the TgF344-AD rat model. The recorded signals were filtered into low frequency (LFP) and high frequency (spiking activity) signals, and machine learning classifiers were employed to identify the rat genotype (TG vs. WT). By analyzing specific frequency bands in the low frequency signals and calculating distance metrics between spike trains in the high frequency signals, accurate classification was achieved. Gamma band power emerged as a valuable signal for classification, and combining information from both low and high frequency signals improved the accuracy further. These findings provide valuable insights into the early stage effects of AD on different regions of the hippocampus.

EEG-based functional connectivity for tactile roughness discrimination.

Tactile sensation and perception involve cooperation between different parts of the brain. Roughness discrimination is an important phase of texture recognition. In this study, we investigated how different roughness levels would influence the brain network characteristics. We recorded EEG signals from nine right-handed healthy subjects who underwent touching three surfaces with different levels of roughness. The experiment was separately repeated in 108 trials for each hand for both static and dynamic touch. For estimation of the functional connectivity between brain regions, the phase lag index method was employed. Frequency-specific connectivity patterns were observed in the ipsilateral and contralateral hemispheres to the hand of interest, for delta, theta, alpha, and beta frequency bands under the study. A number of connections were identified to be in charge of discrimination between surfaces in both alpha and beta frequency bands for the left hand in static touch and for the right hand in dynamic touch. In addition, common connections were determined in both hands for all three roughness in alpha band for static touch and in theta band for dynamic touch. The common connections were identified for the smooth surface in beta band for static touch and in delta and alpha bands for dynamic touch. As observed for static touch in alpha band and for dynamic touch in theta band, the number of common connections between the two hands was decreased by increasing the surface roughness. The results of this research would extend the current knowledge about tactile information processing in the brain. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-022-09876-1.

The impact of selective and non-selective medial septum stimulation on hippocampal neuronal oscillations: A study based on modeling and experiments.

Alzheimer's disease (AD) is a neurodegenerative disorder with a rising socioeconomic impact on societies. The hippocampus (HPC), which plays an important role in AD, is affected in the early stages. The medial septum (MS) in the forebrain provides major cholinergic input to the HPC and has been shown to play a significant role in generating oscillations in hippocampal neurons. Cholinergic neurons in the basal forebrain are particularly vulnerable to neurodegeneration in AD. To better understand the role of MS neurons including the cholinergic, glutamatergic, and GABAergic subpopulations in generating the well-known brain rhythms in HPC including delta, theta, slow gamma, and fast gamma oscillations, we designed a detailed computational model of the septohippocampal pathway. We validated the results of our model, using electrophysiological recordings in HPC with and without stimulation of the cholinergic neurons in MS using designer receptors exclusively activated by designer drugs (DREADDs) in healthy male ChAT-cre rats. Then, we eliminated 75% of the MS cholinergic neurons in the model to simulate degeneration in AD. A series of selective and non-selective stimulations of the remaining MS neurons were performed to understand the dynamics of oscillation regulation in the HPC during the degenerated state. In this way, appropriate stimulation strategies able to normalize the aberrant oscillations are proposed. We found that selectively stimulating the remaining healthy cholinergic neurons was sufficient for network recovery and compare this to stimulating other subpopulations and a non-selective stimulation of all MS neurons. Our data provide valuable information for the development of new therapeutic strategies in AD and a tool to test and predict the outcome of potential theranostic manipulations.

Texture recognition based on multi-sensory integration of proprioceptive and tactile signals.

The sense of touch plays a fundamental role in enabling us to interact with our surrounding environment. Indeed, the presence of tactile feedback in prostheses greatly assists amputees in doing daily tasks. In this line, the present study proposes an integration of artificial tactile and proprioception receptors for texture discrimination under varying scanning speeds. Here, we fabricated a soft biomimetic fingertip including an 8 × 8 array tactile sensor and a piezoelectric sensor to mimic Merkel, Meissner, and Pacinian mechanoreceptors in glabrous skin, respectively. A hydro-elastomer sensor was fabricated as an artificial proprioception sensor (muscle spindles) to assess the instantaneous speed of the biomimetic fingertip. In this study, we investigated the concept of the complex receptive field of RA-I and SA-I afferents for naturalistic textures. Next, to evaluate the synergy between the mechanoreceptors and muscle spindle afferents, ten naturalistic textures were manipulated by a soft biomimetic fingertip at six different speeds. The sensors' outputs were converted into neuromorphic spike trains to mimic the firing pattern of biological mechanoreceptors. These spike responses are then analyzed using machine learning classifiers and neural coding paradigms to explore the multi-sensory integration in real experiments. This synergy between muscle spindle and mechanoreceptors in the proposed neuromorphic system represents a generalized texture discrimination scheme and interestingly irrespective of the scanning speed.

Therapeutic efficacy of seizure onset zone-targeting high-definition cathodal tDCS in patients with drug-resistant focal epilepsy.

OBJECTIVE: Epilepsy is a common neurological disease with recurrent seizures. Transcranial direct-current stimulation (tDCS) is a safe, non-invasive method used for neuromodulation. The aim of this study was to explore the efficacy of cathodal high-definition tDCS (HD-tDCS) as a noninvasive method for the treatment of patients with drug-resistant focal epilepsy (DRFE). METHODS: This study was conducted as a randomized parallel and double-blind clinical trial. Twenty patients with DRFE were randomly selected through the convenience sampling method and then were divided into two groups. Ten patients received 2 mA cathodal HD-tDCS targeting the seizure-onset zone for 30 min for 2 weeks (5 consecutive days in each week and 10 days in total). Ten patients were randomized to the sham group. Seizure frequency and Interictal Epileptiform Discharges (IEDs) frequency were the primary outcome measures of this study. Quality of Life in Epilepsy Inventory (QoLIE-89) and Montreal Cognitive Assessment (MoCA) scores were used as secondary outcome measures. RESULTS: Seizure frequency decreased significantly among the patients in the treatment group 2 weeks (P-value ≤ 0.05), 1 month (P-value ≤ 0.05), and 2 months (P-value ≤ 0.05) after the stimulation in comparison to the sham group. Patients in the treatment group showed a decrease in the frequency of IED 2 weeks (P-value ≤ 0.05), 1 month (P-value ≤ 0.05), and 2 months (P-value ≤ 0.05) after stimulation. This cathodal stimulation protocol also improved the overall QoLIE-89 score after the stimulation compared to the sham group. CONCLUSIONS: The present study showed that cathodal HD-tDCS had a positive effect on seizure frequency and IED in patients with DRFE. More evidence is required to define the optimal stimulation parameters of HD-tDCS for the treatment of epilepsy. SIGNIFICANCE: Cathodal HD-tDCS targeting the seizure-onset zone is a promising treatment modality for patients with drug-resistant focal epilepsy.

A Neuromorphic CMOS Circuit With Self-Repairing Capability.

Neurophysiological observations confirm that the brain not only is able to detect the impaired synapses (in brain damage) but also it is relatively capable of repairing faulty synapses. It has been shown that retrograde signaling by astrocytes leads to the modulation of synaptic transmission and thus bidirectional collaboration of astrocyte with nearby neurons is an important aspect of self-repairing mechanism. Specifically, the retrograde signaling via astrocyte can increase the transmission probability of the healthy synapses linked to the neuron. Motivated by these findings, in the present research, a CMOS neuromorphic circuit with self-repairing capabilities is proposed based on astrocyte signaling. In this way, the computational model of self-repairing process is hired as a basis for designing a novel analog integrated circuit in the 180-nm CMOS technology. It is illustrated that the proposed analog circuit is able to successfully recompense the damaged synapses by appropriately modifying the voltage signals of the remaining healthy synapses in the wide range of frequency. The proposed circuit occupies 7500- [Formula: see text] silicon area and its power consumption is about [Formula: see text]. This neuromorphic fault-tolerant circuit can be considered as a key candidate for future silicon neuronal systems and implementation of neurorobotic and neuro-inspired circuits.

A Biomimetic Circuit for Electronic Skin With Application in Hand Prosthesis.

One major challenge in upper limb prostheses is providing sensory feedback to amputees. Reproducing the spiking patterns of human primary tactile afferents can be considered as the first step for this challenging problem. In this study, a novel biomimetic circuit for SA-I and RA-I afferents is proposed to functionally replicate the spiking response of the biological tactile afferents to indentation stimuli. The circuit has been designed, laid out, and simulated in TSMC 180nm CMOS technology with a 1.8V supply voltage. A pair of SA-I and RA-I afferent circuits consume [Formula: see text] of power. The occupied silicon area is [Formula: see text] for 32 afferents. To provide the inputs for circuit testing, a patch of skin with a grid of mechanoreceptors is simulated and tested by an edge stimulus presented at different orientations. Experimental data are collected using indentation of 3D-printed edges at different orientations on a tactile sensor mounted on a robotic arm. Inspired by innervation patterns observed in biology, the artificial afferents are connected to several neighboring mechanoreceptors with different weights to form complex receptive fields which cover the entire mechanoreceptor grid. Machine learning algorithms are applied offline to classify the edge orientations based on the pattern of neural responses. Our results show that the complex receptive fields arising from the innervation pattern led to smaller circuit area and lower power consumption, while facilitating data encoding from high-resolution sensors. The proposed biomimetic circuit and tactile encoding example demonstrate potential applications in modern tactile sensing modules for developing novel bio-robotic and prosthetic technologies.

A neuromimetic realization of hippocampal CA1 for theta wave generation.

Recent advances in neural engineering allowed the development of neuroprostheses which facilitate functionality in people with neurological problems. In this research, a real-time neuromorphic system is proposed to artificially reproduce the theta wave and firing patterns of different neuronal populations in the CA1, a sub-region of the hippocampus. The hippocampal theta oscillations (4-12 Hz) are an important electrophysiological rhythm that contributes in various cognitive functions, including navigation, memory, and novelty detection. The proposed CA1 neuromimetic circuit includes 100 linearized Pinsky-Rinzel neurons and 668 excitatory and inhibitory synapses on a field programmable gate array (FPGA). The implemented spiking neural network of the CA1 includes the main neuronal populations for the theta rhythm generation: excitatory pyramidal cells, PV+ basket cells, and Oriens Lacunosum-Moleculare (OLM) cells which are inhibitory interneurons. Moreover, the main inputs to the CA1 region from the entorhinal cortex via the perforant pathway, the CA3 via Schaffer collaterals, and the medial septum via fimbria-fornix are also implemented on the FPGA using a bursting leaky-integrate and fire (LIF) neuron model. The results of hardware realization show that the proposed CA1 neuromimetic circuit successfully reconstructs the theta oscillations and functionally illustrates the phase relations between firing responses of the different neuronal populations. It is also evaluated the impact of medial septum elimination on the firing patterns of the CA1 neuronal population and the theta wave's characteristics. This neuromorphic system can be considered as a potential platform that opens opportunities for neuroprosthetic applications in future works.

Sharpness recognition based on synergy between bio-inspired nociceptors and tactile mechanoreceptors.

Touch and pain sensations are complementary aspects of daily life that convey crucial information about the environment while also providing protection to our body. Technological advancements in prosthesis design and control mechanisms assist amputees to regain lost function but often they have no meaningful tactile feedback or perception. In the present study, we propose a bio-inspired tactile system with a population of 23 digital afferents: 12 RA-I, 6 SA-I, and 5 nociceptors. Indeed, the functional concept of the nociceptor is implemented on the FPGA for the first time. One of the main features of biological tactile afferents is that their distal axon branches in the skin, creating complex receptive fields. Given these physiological observations, the bio-inspired afferents are randomly connected to the several neighboring mechanoreceptors with different weights to form their own receptive field. To test the performance of the proposed neuromorphic chip in sharpness detection, a robotic system with three-degree of freedom equipped with the tactile sensor indents the 3D-printed objects. Spike responses of the biomimetic afferents are then collected for analysis by rate and temporal coding algorithms. In this way, the impact of the innervation mechanism and collaboration of afferents and nociceptors on sharpness recognition are investigated. Our findings suggest that the synergy between sensory afferents and nociceptors conveys more information about tactile stimuli which in turn leads to the robustness of the proposed neuromorphic system against damage to the taxels or afferents. Moreover, it is illustrated that spiking activity of the biomimetic nociceptors is amplified as the sharpness increases which can be considered as a feedback mechanism for prosthesis protection. This neuromorphic approach advances the development of prosthesis to include the sensory feedback and to distinguish innocuous (non-painful) and noxious (painful) stimuli.

A functional spiking neuronal network for tactile sensing pathway to process edge orientation.

To obtain deeper insights into the tactile processing pathway from a population-level point of view, we have modeled three stages of the tactile pathway from the periphery to the cortex in response to indentation and scanned edge stimuli at different orientations. Three stages in the tactile pathway are, (1) the first-order neurons which innervate the cutaneous mechanoreceptors, (2) the cuneate nucleus in the midbrain and (3) the cortical neurons of the somatosensory area. In the proposed network, the first layer mimics the spiking patterns generated by the primary afferents. These afferents have complex skin receptive fields. In the second layer, the role of lateral inhibition on projection neurons in the cuneate nucleus is investigated. The third layer acts as a biomimetic decoder consisting of pyramidal and cortical interneurons that correspond to heterogeneous receptive fields with excitatory and inhibitory sub-regions on the skin. In this way, the activity of pyramidal neurons is tuned to the specific edge orientations. By modifying afferent receptive field size, it is observed that the larger receptive fields convey more information about edge orientation in the first spikes of cortical neurons when edge orientation stimuli move across the patch of skin. In addition, the proposed spiking neural model can detect edge orientation at any location on the simulated mechanoreceptor grid with high accuracy. The results of this research advance our knowledge about tactile information processing and can be employed in prosthetic and bio-robotic applications.

A Neuromorphic Digital Circuit for Neuronal Information Encoding Using Astrocytic Calcium Oscillations.

Neurophysiological observations are clarifying how astrocytes can actively participate in information processing and how they can encode information through frequency and amplitude modulation of intracellular Ca2+ signals. Consequently, hardware realization of astrocytes is important for developing the next generation of bio-inspired computing systems. In this paper, astrocytic calcium oscillations and neuronal firing dynamics are presented by De Pittà and IF (Integrated & Fire) models, respectively. Considering highly nonlinear equations of the astrocyte model, linear approximation and single constant multiplication (SCM) techniques are employed for efficient hardware execution while maintaining the dynamic of the original models. This low-cost hardware architecture for the astrocyte model is able to show the essential features of different types of Ca2+ modulation such as amplitude modulation (AM), frequency modulation (FM), or both modes (AFM). To show good agreement between the results of original models simulated in MATLAB and the proposed digital circuits executed on FPGA, quantitative, and qualitative analyses including phase plane are done. This new neuromorphic circuit of astrocyte is able to successfully demonstrate AM/FM/AFM calcium signaling in its real operation on FPGA and has applications in self-repairing systems. It also can be employed as a subsystem for linking biological cells to artificial neuronal networks using astrocytic calcium oscillations in future research.

A Digital Hardware System for Spiking Network of Tactile Afferents.

In the present research, we explore the possibility of utilizing a hardware-based neuromorphic approach to develop a tactile sensory system at the level of first-order afferents, which are slowly adapting type 1 (SA-I) and fast adapting type 1 (FA-I) afferents. Four spiking models are used to mimic neural signals of both SA-I and FA-I primary afferents. Next, a digital circuit is designed for each spiking model for both afferents to be implemented on the field-programmable gate array (FPGA). The four different digital circuits are then compared from source utilization point of view to find the minimum cost circuit for creating a population of digital afferents. In this way, the firing responses of both SA-I and FA-I afferents are physically measured in hardware. Finally, a population of 243 afferents consisting of 90 SA-I and 153 FA-I digital neuromorphic circuits are implemented on the FPGA. The FPGA also receives nine inputs from the force sensors through an interfacing board. Therefore, the data of multiple inputs are processed by the spiking network of tactile afferents, simultaneously. Benefiting from parallel processing capabilities of FPGA, the proposed architecture offers a low-cost neuromorphic structure for tactile information processing. Applying machine learning algorithms on the artificial spiking patterns collected from FPGA, we successfully classified three different objects based on the firing rate paradigm. Consequently, the proposed neuromorphic system provides the opportunity for development of new tactile processing component for robotic and prosthetic applications.

A Digital Hardware Realization for Spiking Model of Cutaneous Mechanoreceptor.

Inspired by the biology of human tactile perception, a hardware neuromorphic approach is proposed for spiking model of mechanoreceptors to encode the input force. In this way, a digital circuit is designed for a slowly adapting type I (SA-I) and fast adapting type I (FA-I) mechanoreceptors to be implemented on a low-cost digital hardware, such as field-programmable gate array (FPGA). This system computationally replicates the neural firing responses of both afferents. Then, comparative simulations are shown. The spiking models of mechanoreceptors are first simulated in MATLAB and next the digital neuromorphic circuits simulated in VIVADO are also compared to show that obtained results are in good agreement both quantitatively and qualitatively. Finally, we test the performance of the proposed digital mechanoreceptors in hardware using a prepared experimental set up. Hardware synthesis and physical realization on FPGA indicate that the digital mechanoreceptors are able to replicate essential characteristics of different firing patterns including bursting and spiking responses of the SA-I and FA-I mechanoreceptors. In addition to parallel computation, a main advantage of this method is that the mechanoreceptor digital circuits can be implemented in real-time through low-power neuromorphic hardware. This novel engineering framework is generally suitable for use in robotic and hand-prosthetic applications, so progressing the state of the art for tactile sensing.

Digital Implementation of the Two-Compartmental Pinsky-Rinzel Pyramidal Neuron Model.

It is believed that brain-like computing system can be achieved by the fusion of electronics and neuroscience. In this way, the optimized digital hardware implementation of neurons, primary units of nervous system, play a vital role in neuromorphic applications. Moreover, one of the main features of pyramidal neurons in cortical areas is bursting activities that has a critical role in synaptic plasticity. The Pinsky-Rinzel model is a nonlinear two-compartmental model for CA3 pyramidal cell that is widely used in neuroscience. In this paper, a modified Pinsky-Rinzel pyramidal model is proposed by replacing its complex nonlinear equations with piecewise linear approximation. Next, a digital circuit is designed for the simplified model to be able to implement on a low-cost digital hardware, such as field-programmable gate array (FPGA). Both original and proposed models are simulated in MATLAB and next digital circuit simulated in Vivado is compared to show that obtained results are in good agreement. Finally, the results of physical implementation on FPGA are also illustrated. The presented circuit advances preceding designs with regards to the ability to replicate essential characteristics of different firing responses including bursting and spiking in the compartmental model. This new circuit has various applications in neuromorphic engineering, such as developing new neuroinspired chips.

Fast and Efficient Four­class Motor Imagery Electroencephalography Signal Analysis Using Common Spatial Pattern-Ridge Regression Algorithm for the Purpose of Brain-Computer Interface.

Brain-computer interfaces enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. One of the most challenging issues in this regard is the balance between the accuracy of brain signals from patients and the speed of interpreting them into machine language. The main objective of this paper is to analyze different approaches to achieve the balance more quickly and in a better way. To reduce the ocular artifacts, the symmetric prewhitening independent component analysis (ICA) algorithm has been evaluated, which has the lowest runtime and lowest signal-to-interference (SIR) index, without destroying the original signal. After quick elimination of all undesirable signals, two successful feature extractors - the log-band power algorithm and common spatial patterns (CSPs) - are used to extract features. The emphasis is on identifying discriminative properties of the feature sets representing EEG trials recorded during the imagination of the tongue, feet, and left-right-hand movement. Finally, three well-known classifiers are evaluated, where the ridge regression classifier and CSPs as feature extractor have the highest accuracy classification rate about 83.06% with a standard deviation of 1.22%, counterposing the recent studies.

A new bio-inspired stimulator to suppress hyper-synchronized neural firing in a cortical network.

Hyper-synchronous neural oscillations are the character of several neurological diseases such as epilepsy. On the other hand, glial cells and particularly astrocytes can influence neural synchronization. Therefore, based on the recent researches, a new bio-inspired stimulator is proposed which basically is a dynamical model of the astrocyte biophysical model. The performance of the new stimulator is investigated on a large-scale, cortical network. Both excitatory and inhibitory synapses are also considered in the simulated spiking neural network. The simulation results show that the new stimulator has a good performance and is able to reduce recurrent abnormal excitability which in turn avoids the hyper-synchronous neural firing in the spiking neural network. In this way, the proposed stimulator has a demand controlled characteristic and is a good candidate for deep brain stimulation (DBS) technique to successfully suppress the neural hyper-synchronization.

A bio-inspired stimulator to desynchronize epileptic cortical population models: A digital implementation framework.

Pathophysiologic neural synchronization is a hallmark of several neurological disorders such as epilepsy. In addition, based on established neurophysiologic findings, astrocytes dynamically regulate the synaptic transmission and have key roles in stabilizing neural synchronization. Therefore, in the present study, based on the dynamic model of astrocyte, a digital bio-inspired stimulator is proposed to avoid the hyper-synchronous seizure-like activities in a cortical population model. The complete digital circuit of the close loop system that is the bio-inspired stimulator and the cortical population model are implemented in hardware on the ZedBoard development kit. Based on the results of MATLAB simulations, hardware synthesis and FPGA implementation, it is demonstrated that the digital bio-inspired stimulator can effectively prevent the occurrence of spontaneous paroxysmal episodes with a demand-controlled characteristic. In this way, the designed digital stimulator successfully maintains the normal ongoing activity.