Diffusion-Based Model for Synaptic Molecular Communication Channel

N4Sim: 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.

Saturating Receiver and Receptor Competition in Synaptic DMC: Deterministic and Statistical Signal Models

Synaptic communication is based on a biological Molecular Communication (MC) system which may serve as a blueprint for the design of synthetic MC systems. However, the physical modeling of synaptic MC is complicated by the possible saturation of the molecular receiver caused by the competition of neurotransmitters (NTs) for postsynaptic receptors. Receiver saturation renders the system behavior nonlinear in the number of released NTs and is commonly neglected in existing analytical models. Furthermore, due to the ligands' competition for receptors (and vice versa), the individual binding events at the molecular receiver are in general not statistically independent and the commonly used binomial model for the statistics of the received signal does not apply. Hence, in this work, we propose a novel deterministic model for receptor saturation in terms of a state-space description based on an eigenfunction expansion of Fick's diffusion equation. The presented solution is numerically stable and computationally efficient. Employing the proposed deterministic model, we show that saturation at the molecular receiver effectively reduces the peak-value of the expected received signal and accelerates the clearance of NTs as compared to the case when receptor occupancy is neglected. We further derive a statistical model for the received signal in terms of the hypergeometric distribution which accounts for the competition of NTs for receptors and the competition of receptors for NTs. The proposed statistical model reveals how the signal statistics are shaped by the number of released NTs, the number of receptors, and the binding kinetics of the receptors, respectively, in the presence of competition. In particular, we show that the impact of these parameters on the signal variance is qualitatively different depending on the relative numbers of NTs and receptors. Finally, the accuracy of the proposed deterministic and statistical models is verified by particle-based computer simulations.

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.

Boolean AND and OR logic for cell signalling gateways: a communication perspective

Cell signalling plays a vital role in development, sustaining, differentiation, and reproduction of cells. Pathways involved in signalling networks are quite interwoven and complex. Complexity encountered in understanding these pathways is often reduced with the help of Boolean circuit representation. In this study, the authors provide communication aspect of the signalling pathways that have two input Boolean logic AND/OR implemented at the rear effector protein. Communication is assumed to be taking place in extracellular and intracellular environment. The two environments are connected using a receptor protein acting as relay between a molecular source and effector protein. Each relay detects molecules from outside environment and stimulates the production of signals in the intracellular space. These signals/molecules further activate the effector protein which acts as a Boolean switch driven by AND/OR logic. Assuming Poisson reception at the relay as well as at the receiver, the authors provide probability of error of the AND and OR Boolean logic communication systems. Furthermore, reliability and some capacity bounds are deduced for the given Boolean communication system.

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.

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.

Wireless Optogenetic Nanonetworks for Brain Stimulation: Device Model and Charging Protocols

In recent years, numerous research efforts have been dedicated toward developing efficient implantable devices for brain stimulation. However, there are limitations and challenges with the current technologies. They include neuron population stimulation instead of single neuron level, the size, the biocompatibility, and the device lifetime reliability in the patient's brain. We have recently proposed the concept of wireless optogenetic nanonetworking devices (WiOptND) that could address the problem of long term deployment, and at the same time target single neuron stimulation utilizing ultrasonic as a mode for energy harvesting. In addition, a number of charging protocols are also proposed, in order to minimize the quantity of energy required for charging, while ensuring minimum number of neural spike misfirings. These protocols include the simple charge and fire, which requires the full knowledge of the raster plots of neuron firing patterns, and the predictive sliding detection window, and its variant Markov-chain based time-delay patterns, which minimizes the need for full knowledge of neural spiking patterns as well as number of ultrasound charging frequencies. Simulation results exhibit a drop for the stimulation ratio of ~ 25% and more stable trend in its efficiency ratio (standard deviation of ~0.5%) for the Markov-chain based time-delay patterns protocol compared with the baseline change and fire. The results show the feasibility of utilizing WiOptND for long-term implants in the brain, and a new direction toward precise stimulation of neurons in the cortical microcolumn of the brain cortex.