Information and Communication Theoretical Understanding and Treatment of Spinal Cord Injuries: State-of-The-Art and Research Challenges.

Among the various key networks in the human body, the nervous system occupies central importance. The debilitating effects of spinal cord injuries (SCI) impact a significant number of people throughout the world, and to date, there is no satisfactory method to treat them. In this paper, we review the major treatment techniques for SCI that include promising solutions based on information and communication technology (ICT) and identify the key characteristics of such systems. We then introduce two novel ICT-based treatment approaches for SCI. The first proposal is based on neural interface systems (NIS) with enhanced feedback, where the external machines are interfaced with the brain and the spinal cord such that the brain signals are directly routed to the limbs for movement. The second proposal relates to the design of self-organizing artificial neurons (ANs) that can be used to replace the injured or dead biological neurons. Apart from SCI treatment, the proposed methods may also be utilized as enabling technologies for neural interface applications by acting as bio-cyber interfaces between the nervous system and machines. Furthermore, under the framework of Internet of Bio-Nano Things (IoBNT), experience gained from SCI treatment techniques can be transferred to nano communication research.

N4 Sim: The First Nervous NaNoNetwork Simulator With Synaptic Molecular Communications.

The unconventional nature of molecular communication necessitates contributions from a host of scientific fields making the simulator design for such systems to be quite challenging. The nervous system is one of the largest and most important nanonetworks of the body. Several molecular and nano communication simulators exist in literature along with a few neural network simulators, however, most existing simulators are not specific for the nervous system since they ignore the synaptic diffusion because of the computational complexity required to model it. Additionally, information and communication theoretical (ICT) analysis of the system is not directly supported by existing neural network simulators. In this work, we present and describe Neural NaNoNetwork Simulator, N4Sim , which can resolve these issues in existing simulators. We describe key components of the simulator and methods to solve the synaptic communication in a fast and efficient manner. Our model for the synaptic communication channel is comparable in accuracy to those achieved by Monte Carlo simulations while using a fraction of time and processing resources. The presented simulator opens a large set of design options for applications in nervous system.

Fabrication and microfluidic analysis of graphene-based molecular communication receiver for Internet of Nano Things (IoNT).

Bio-inspired molecular communications (MC), where molecules are used to transfer information, is the most promising technique to realise the Internet of Nano Things (IoNT), thanks to its inherent biocompatibility, energy-efficiency, and reliability in physiologically-relevant environments. Despite a substantial body of theoretical work concerning MC, the lack of practical micro/nanoscale MC devices and MC testbeds has led researchers to make overly simplifying assumptions about the implications of the channel conditions and the physical architectures of the practical transceivers in developing theoretical models and devising communication methods for MC. On the other hand, MC imposes unique challenges resulting from the highly complex, nonlinear, time-varying channel properties that cannot be always tackled by conventional information and communication tools and technologies (ICT). As a result, the reliability of the existing MC methods, which are mostly adopted from electromagnetic communications and not validated with practical testbeds, is highly questionable. As the first step to remove this discrepancy, in this study, we report on the fabrication of a nanoscale MC receiver based on graphene field-effect transistor biosensors. We perform its ICT characterisation in a custom-designed microfluidic MC system with the information encoded into the concentration of single-stranded DNA molecules. This experimental platform is the first practical implementation of a micro/nanoscale MC system with nanoscale MC receivers, and can serve as a testbed for developing realistic MC methods and IoNT applications.

Molecular Communication Theoretical Modeling and Analysis of SARS-CoV2 Transmission in Human Respiratory System.

Severe Acute Respiratory Syndrome-CoronaVirus 2 (SARS-CoV2) caused the ongoing pandemic. This pandemic devastated the world by killing more than a million people, as of October 2020. It is imperative to understand the transmission dynamics of SARS-CoV2 so that novel and interdisciplinary prevention, diagnostic, and therapeutic techniques could be developed. In this work, we model and analyze the transmission of SARS-CoV2 through the human respiratory tract from a molecular communication perspective. We consider that virus diffusion occurs in the mucus layer so that the shape of the tract does not have a significant effect on the transmission. Hence, this model reduces the inherent complexity of the human respiratory system. We further provide the impulse response of SARS-CoV2-ACE2 receptor binding event to determine the proportion of the virus population reaching different regions of the respiratory tract. Our findings confirm the results in the experimental literature on higher mucus flow rate causing virus migration to the lower respiratory tract. These results are especially important to understand the effect of SARS-CoV2 on the different human populations at different ages who have different mucus flow rates and ACE2 receptor concentrations in the different regions of the respiratory tract.

Rate of Information Flow Across Layered Neuro-Spike Network in the Spinal Cord.

Spinal Cord Injury (SCI) is a severe condition that can result in loss of motor and sensory functions by disrupting communication among neurons, i.e., neuro-spike communication. Future information and communication technology (ICT) based treatment techniques for SCI are expected to rely on nano networks, deployed inside the body. In this respect, modeling neuro-spike communication channels in the spinal cord and revealing the relationship between channel metrics and SCI are required to realize these treatment techniques and diagnosis tools such as replacement neural implants, high-performance diagnosis tools, which are based on ICT metrics instead of large medical data. Therefore, in this study, we focus on a spinal cord network, namely the descending spinal cord pathway, which is responsible for the transmission of brain motor signals to the spinal cord. We aim to analyze the rate of motor information flow to the corresponding muscle. To this end, we model the spinal cord motor network as a layered network consisting of a cascade of two independent neuro-spike channels, which are brain-spinal cord network and spinal cord interneuron-spinal cord motoneuron network. We derive upper and lower bounds for the total rate across the brain-spinal cord network and interneuron-spinal cord network. Our evaluations demonstrate that the total rate in the case of upper motor neuron syndrome (UMNS), which manifests itself with muscle weakness, approaches zero, where the brain-spinal cord network becomes a bottleneck. In lower motor neuron syndrome (LMNS), which results in muscle atrophy, the total rate again approaches zero with the loss of spinal cord motoneurons (MN).

Impact of Long Term Plasticity on Information Transmission Over Neuronal Networks.

The realization of bio-compatible nanomachines would pave the way for developing novel diagnosis and treatment techniques for the dysfunctions of intra-body nanonetworks and revolutionize the traditional healthcare methodologies making them less invasive and more efficient. The network of these nanomachines is aimed to be used for treating neuronal diseases such as developing an implant that bridges over the injured spinal cord to regain its normal functionality. Thus, nanoscale communication paradigms are needed to be investigated to facilitate communication between nanomachines. Communication among neurons is one of the most promising nanoscale communication paradigm, which necessitates the thorough communication theoretical analysis of information transmission among neurons. The information flow in neuro-spike communication channel is regulated by the ability of neurons to change synaptic strengths over time, i.e. synaptic plasticity. Thus, the performance evaluation of the nervous nanonetwork is incomplete without considering the influence of synaptic plasticity. In this paper, we focus on information transmission among hippocampal pyramidal neurons and provide a comprehensive channel model for MISO neuro-spike communication, which includes axonal transmission, vesicle release process, synaptic communication and spike generation. In this channel, the spike timing dependent plasticity (STDP) model is used to cover both synaptic depressiofan and potentiation depending on the temporal correlation between spikes generated by input and output neurons. Since synaptic strength changes depending on different physiological factors such as spiking rate of presynaptic neurons, number of correlated presynaptic neurons and the correlation factor among them, we simulate this model with correlated inputs and analyze the evolution of synaptic weights over time. Moreover, we calculate average mutual information between input and output of the channel and find the impact of plasticity and correlation among inputs on the information transmission. The simulation results reveal the impact of different physiological factors related to either presynaptic or postsynaptic neurons on the performance of MISO neuro-spike communication. Moreover, they provide guidelines for selecting the system parameters in a bio-inspired neuronal network according to the requirements of different applications.

Sum Rate of MISO Neuro-Spike Communication Channel With Constant Spiking Threshold.

Communication among neurons, known as neuro-spike communication, is the most promising technique for realization of a bio-inspired nanoscale communication paradigm to achieve biocompatible nanonetworks. In neuro-spike communication, the information, encoded into spike trains, is communicated to various brain regions through neuronal network. An output neuron needs to receive signal from multiple input neurons to generate a spike. Hence, in this paper, we aim to quantify the information transmitted through the multiple-input single-output (MISO) neuro-spike communication channel by considering models for axonal propagation, synaptic transmission, and spike generation. Moreover, the spike generation and propagation in each neuron requires opening and closing of numerous ionic channels on the cell membrane, which consumes considerable amount of ATP molecules called metabolic energy. Thus, we evaluate how applying a constraint on available metabolic energy affects the maximum achievable mutual information of this system. To this aim, we derive a closed form equation for the sum rate of the MISO neuro-spike communication channel and analyze it under the metabolic cost constraints. Finally, we discuss the impacts of changes in number of pre-synaptic neurons on the achievable rate and quantify the tradeoff between maximum achievable sum rate and the consumed metabolic energy.

Impacts of Spike Shape Variations on Synaptic Communication.

Understanding the communication theoretical capabilities of information transmission among neurons, known as neuro-spike communication, is a significant step in developing bio-inspired solutions for nanonetworking. In this paper, we focus on a part of this communication known as synaptic transmission for pyramidal neurons in the Cornu Ammonis area of the hippocampus location in the brain and propose a communication-based model for it that includes effects of spike shape variation on neural calcium signaling and the vesicle release process downstream of it. For this aim, we find impacts of spike shape variation on opening of voltage-dependent calcium channels, which control the release of vesicles from the pre-synaptic neuron by changing the influx of calcium ions. Moreover, we derive the structure of the optimum receiver based on the Neyman-Pearson detection method to find the effects of spike shape variations on the functionality of neuro-spike communication. Numerical results depict that changes in both spike width and amplitude affect the error detection probability. Moreover, these two factors do not control the performance of the system independently. Hence, a proper model for neuro-spike communication should contain effects of spike shape variations during axonal transmission on both synaptic propagation and spike generation mechanisms to enable us to accurately explain the performance of this communication paradigm.

D-DSC: Decoding Delay-based Distributed Source Coding for Internet of Sensing Things.

Spatial correlation between densely deployed sensor nodes in a wireless sensor network (WSN) can be exploited to reduce the power consumption through a proper source coding mechanism such as distributed source coding (DSC). In this paper, we propose the Decoding Delay-based Distributed Source Coding (D-DSC) to improve the energy efficiency of the classical DSC by employing the decoding delay concept which enables the use of the maximum correlated portion of sensor samples during the event estimation. In D-DSC, network is partitioned into clusters, where the clusterheads communicate their uncompressed samples carrying the side information, and the cluster members send their compressed samples. Sink performs joint decoding of the compressed and uncompressed samples and then reconstructs the event signal using the decoded sensor readings. Based on the observed degree of the correlation among sensor samples, the sink dynamically updates and broadcasts the varying compression rates back to the sensor nodes. Simulation results for the performance evaluation reveal that D-DSC can achieve reliable and energy-efficient event communication and estimation for practical signal detection/estimation applications having massive number of sensors towards the realization of Internet of Sensing Things (IoST).

Maximum Likelihood Detection With Ligand Receptors for Diffusion-Based Molecular Communications in Internet of Bio-Nano Things.

Molecular Communication (MC) is a bio-inspired communication technique that uses molecules as a method of information transfer among nanoscale devices. MC receiver is an essential component having profound impact on the communication system performance. However, the interaction of the receiver with information bearing molecules has been usually oversimplified in modeling the reception process and developing signal detection techniques. In this paper, we focus on the signal detection problem of MC receivers employing receptor molecules to infer the transmitted messages encoded into the concentration of molecules, i.e., ligands. Exploiting the observable characteristics of ligand-receptor binding reaction, we first introduce a Maximum Likelihood (ML) detection method based on instantaneous receptor occupation ratio, as aligned with the current MC literature. Then, we propose a novel ML detection technique, which exploits the amount of time the receptors stay unbound in an observation time window. A comprehensive analysis is carried out to compare the performance of the detectors in terms of bit error probability. In evaluating the detection performance, emphasis is given to the receptor saturation problem resulting from the accumulation of messenger molecules at the receiver as a consequence of intersymbol interference. The results reveal that detection based on receptor unbound time is quite reliable even in saturation, whereas the reliability of detection based on receptor occupation ratio substantially decreases as the receiver gets saturated. Finally, we also discuss the potential methods of implementing the detectors.

Modeling convection-diffusion-reaction systems for microfluidic molecular communications with surface-based receivers in Internet of Bio-Nano Things.

We consider a microfluidic molecular communication (MC) system, where the concentration-encoded molecular messages are transported via fluid flow-induced convection and diffusion, and detected by a surface-based MC receiver with ligand receptors placed at the bottom of the microfluidic channel. The overall system is a convection-diffusion-reaction system that can only be solved by numerical methods, e.g., finite element analysis (FEA). However, analytical models are key for the information and communication technology (ICT), as they enable an optimisation framework to develop advanced communication techniques, such as optimum detection methods and reliable transmission schemes. In this direction, we develop an analytical model to approximate the expected time course of bound receptor concentration, i.e., the received signal used to decode the transmitted messages. The model obviates the need for computationally expensive numerical methods by capturing the nonlinearities caused by laminar flow resulting in parabolic velocity profile, and finite number of ligand receptors leading to receiver saturation. The model also captures the effects of reactive surface depletion layer resulting from the mass transport limitations and moving reaction boundary originated from the passage of finite-duration molecular concentration pulse over the receiver surface. Based on the proposed model, we derive closed form analytical expressions that approximate the received pulse width, pulse delay and pulse amplitude, which can be used to optimize the system from an ICT perspective. We evaluate the accuracy of the proposed model by comparing model-based analytical results to the numerical results obtained by solving the exact system model with COMSOL Multiphysics.

Controlled Information Transfer Through An In Vivo Nervous System.

The nervous system holds a central position among the major in-body networks. It comprises of cells known as neurons that are responsible to carry messages between different parts of the body and make decisions based on those messages. In this work, further to the extensive theoretical studies, we demonstrate the first controlled information transfer through an in vivo nervous system by modulating digital data from macro-scale devices onto the nervous system of common earthworms and conducting successful transmissions. The results and analysis of our experiments provide a method to model networks of neurons, calculate the channel propagation delay, create their simulation models, indicate optimum parameters such as frequency, amplitude and modulation schemes for such networks, and identify average nerve spikes per input pulse as the nervous information coding scheme. Future studies on neuron characterization and artificial neurons may benefit from the results of our work.

Importance of vesicle release stochasticity in neuro-spike communication.

Aim of this paper is proposing a stochastic model for vesicle release process, a part of neuro-spike communication. Hence, we study biological events occurring in this process and use microphysiological simulations to observe functionality of these events. Since the most important source of variability in vesicle release probability is opening of voltage dependent calcium channels (VDCCs) followed by influx of calcium ions through these channels, we propose a stochastic model for this event, while using a deterministic model for other variability sources. To capture the stochasticity of calcium influx to pre-synaptic neuron in our model, we study its statistics and find that it can be modeled by a distribution defined based on Normal and Logistic distributions.

Speech features for telemonitoring of Parkinson's disease symptoms.

The aim of this paper is tracking Parkinson's disease (PD) progression based on its symptoms on vocal system using Unified Parkinsons Disease Rating Scale (UPDRS). We utilize a standard speech signal feature set, which contains 6373 static features as functionals of low-level descriptor (LLD) contours, and select the most informative ones using the maximal relevance and minimal redundancy based on correlations (mRMRC) criteria. Then, we evaluate performance of Gaussian mixture regression (GMR) and support vector regression (SVR) on estimating the third subscale of UPDRS, i.e., UPDRS: motor subscale (UPDRS-III). Among the most informative features, a list of features are selected after redundancy reduction. The selected features depict that LLDs providing information about spectrum flatness, spectral distribution of energy, and hoarseness of voice are the most important ones for estimating UPDRS-III. Moreover, the most informative statistical functions are related to range, maximum, minimum and standard deviation of LLDs, which is an evidence of the muscle weakness due to the PD. Furthermore, GMR outperforms SVR on compact feature sets while the performance of SVR improves by increasing number of features.

An Information Theoretical Analysis of Human Insulin-Glucose System Toward the Internet of Bio-Nano Things.

Molecular communication is an important tool to understand biological communications with many promising applications in Internet of Bio-Nano Things (IoBNT). The insulin-glucose system is of key significance among the major intra-body nanonetworks, since it fulfills metabolic requirements of the body. The study of biological networks from information and communication theoretical (ICT) perspective is necessary for their introduction in the IoBNT framework. Therefore, the objective of this paper is to provide and analyze for the first time in the literature, a simple molecular communication model of the human insulin-glucose system from ICT perspective. The data rate, channel capacity, and the group propagation delay are analyzed for a two-cell network between a pancreatic beta cell and a muscle cell that are connected through a capillary. The results point out a correlation between an increase in insulin resistance and a decrease in the data rate and channel capacity, an increase in the insulin transmission rate, and an increase in the propagation delay. We also propose applications for the introduction of the system in the IoBNT framework. Multi-cell insulin glucose system models may be based on this simple model to help in the investigation, diagnosis, and treatment of insulin resistance by means of novel IoBNT applications.

A Fast Algorithm for Analysis of Molecular Communication in Artificial Synapse.

In this paper, we analyze molecular communications (MCs) in a proposed artificial synapse (AS), whose main difference from biological synapses (BSs) is that it is closed, i.e., transmitter molecules cannot diffuse out from AS. Such a setup has both advantages and disadvantages. Besides higher structural stability, being closed, AS never runs out of transmitters. Thus, MC in AS is disconnected from outer environment, which is very desirable for possible intra-body applications. On the other hand, clearance of transmitters from AS has to be achieved by transporter molecules on the presynaptic membrane of AS. Except from these differences, rest of AS content is taken to be similar to that of a glutamatergic BS. Furthermore, in place of commonly used Monte Carlo-based random walk experiments, we derive a deterministic algorithm that attacks for expected values of desired parameters such as evolution of receptor states. To assess validity of our algorithm, we compare its results with average results of an ensemble of Monte Carlo experiments, which shows near exact match. Moreover, our approach requires significantly less amount of computation compared with Monte Carlo approach, making it useful for parameter space exploration necessary for optimization in design of possible MC devices, including but not limited to AS. Results of our algorithm are presented in case of single quantal release only, and they support that MC in closed AS with elevated uptake has similar properties to that in BS. In particular, similar to glutamatergic BSs, the quantal size and the density of receptors are found to be main sources of synaptic plasticity. On the other hand, the proposed model of AS is found to have slower decaying transients of receptor states than BSs, especially desensitized ones, which is due to prolonged clearance of transmitters from AS.

Diffusion-Based Model for Synaptic Molecular Communication Channel.

Computational methods have been extensively used to understand the underlying dynamics of molecular communication methods employed by nature. One very effective and popular approach is to utilize a Monte Carlo simulation. Although it is very reliable, this method can have a very high computational cost, which in some cases renders the simulation impractical. Therefore, in this paper, for the special case of an excitatory synaptic molecular communication channel, we present a novel mathematical model for the diffusion and binding of neurotransmitters that takes into account the effects of synaptic geometry in 3-D space and re-absorption of neurotransmitters by the transmitting neuron. Based on this model we develop a fast deterministic algorithm, which calculates expected value of the output of this channel, namely, the amplitude of excitatory postsynaptic potential (EPSP), for given synaptic parameters. We validate our algorithm by a Monte Carlo simulation, which shows total agreement between the results of the two methods. Finally, we utilize our model to quantify the effects of variation in synaptic parameters, such as position of release site, receptor density, size of postsynaptic density, diffusion coefficient, uptake probability, and number of neurotransmitters in a vesicle, on maximum number of bound receptors that directly affect the peak amplitude of EPSP.

A Communication Theoretical Modeling of Axonal Propagation in Hippocampal Pyramidal Neurons.

Understanding the fundamentals of communication among neurons, known as neuro-spike communication, leads to reach bio-inspired nanoscale communication paradigms. In this paper, we focus on a part of neuro-spike communication, known as axonal transmission, and propose a realistic model for it. The shape of the spike during axonal transmission varies according to previously applied stimulations to the neuron, and these variations affect the amount of information communicated between neurons. Hence, to reach an accurate model for neuro-spike communication, the memory of axon and its effect on the axonal transmission should be considered, which are not studied in the existing literature. In this paper, we extract the important factors on the memory of axon and define memory states based on these factors. We also describe the transition among these states and the properties of axonal transmission in each of them. Finally, we demonstrate that the proposed model can follow changes in the axonal functionality properly by simulating the proposed model and reporting the root mean square error between simulation results and experimental data.

Theoretical Limits on Multiuser Molecular Communication in Internet of Nano-Bio Things.

In nano-bio networks, multiple transmitter-receiver pairs will operate in the same medium. Both inter-symbol interference and multi-user interference can cause saturation at the receiver side, and this effect may cause an outage. Thus, we propose a tractable framework to calculate the theoretical operating points for fully absorbing receiver.

On the capacity of diffusion-based molecular communications with SiNW FET-based receiver.

Molecular communication (MC) is a bio-inspired communication method based on the exchange of molecules for information transfer among nanoscale devices. Although MC has been extensively studied from various aspects, limitations imposed by the physical design of transceiving units have been largely neglected in the literature. Recently, we have proposed a nanobioelectronic MC receiver architecture based on the nanoscale field effect transistor-based biosensor (bioFET) technology, providing noninvasive and sensitive molecular detection at nanoscale while producing electrical signals at the output. In this paper, we derive analytical closed-form expressions for the capacity and capacity-achieving input distribution for a memoryless MC channel with a silicon nanowire (SiNW) FET-based MC receiver. The resulting expressions could be used to optimize the information flow in MC systems equipped with nanobioelectronic receivers.