Neuro-spike Communication Channel Research Articles

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 analysis of multiple-access neuro-spike communication channel with dynamic spiking threshold

The information from outside world is encoded into spikes by the sensory neurons. These spikes are further propagated to different brain regions through various neural pathways. In the cortical region, each neuron receives inputs from multiple neurons that change its membrane potential. If the accumulated change in the membrane potential is more than a threshold value, a spike is generated. According to various studies in neuroscience, this spiking threshold adapts with time depending on the previous spike. This causes short-term changes in the neural responses giving rise to short-term plasticity. Therefore, in this paper, we analyze a multiple-input single-output (MISO) neuro-spike communication channel and study the effects of dynamic spiking threshold on mutual information and maximum achievable sum rate of the channel. Since spike generation consumes a generous portion of the metabolic energy provided to the brain, we further put metabolic constraint in calculating the mutual information and find a trade-off between maximum achievable sum rate and metabolic energy consumed. Moreover, we analyze three types of neurons present in the cortical region, i.e., Regular spiking, Intrinsic bursting and Fast spiking neurons. We aim to characterize these neurons in terms of encoding/transmission rates and energy expenditure. It will provide a guideline for the practical implementation of bio-inspired nanonetworks as well as for the development of ICT-based diagnosis and treatment techniques for neural diseases.

Joint Optimization of Input Spike Rate and Receiver Decision Threshold to Maximize Achievable Bit Rate of Neuro-Spike Communication Channel

Nano-networks employ novel nano-scale communication techniques. These techniques are inspired by biological systems. Neuro-spike communication is an example of this communication paradigm. A new example of nano-networks is the artificial neural system where nano-machines are linked to neurons to treat the neurodegenerative diseases. In these networks, nano-machines are used to replace the damaged segments of the nervous system and they behave exactly like biological entities. In this paper, by considering a point-to-point model for the neuro-spike communication which contains several sources of randomness such as the axonal noise, random amplitude, and the synaptic noise, we analyze the achievable bit rate of the neuro-spike communication channel. This model can be divided into two main parts. The first part is the axonal pathway and the second one encompasses the synaptic transmission and the spike generation. First, we focus on the axonal pathway and we model this part as a binary channel through defining the axonal shot noise probability. Next, we investigate the synaptic transmission part to model this part as a binary channel. Then, we consider the neuro-spike communication channel as two cascaded binary channels to obtain its error probability and achievable bit rate. To enhance the achievable bit rate of the neuro-spike communication, we design an optimum spike detection receiver and also we propose an optimum input spike rate. We derive closed-form descriptions for the decision threshold of the optimum receiver and the optimum input spike rate. Since they are coupled, we propose a recursive scheme to derive their optimum values. Finally, simulation results are used to investigate the impact of the axonal noise, the synaptic noise, and modifications of the spikes' shape due to propagation along the axon on the achievable bit rate of the neuro-spike communication channel.

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.

Axonal Channel Capacity in Neuro-Spike Communication.

Novel nano-scale communication techniques are inspired by biological systems. Neuro-spike communication is an example of this communication paradigm which transfers vital information about external and internal conditions of the body through the nervous system. The analysis of this communication paradigm is beneficial to exploit in the artificial neural systems where nano-machines are linked to neurons to treat the neurodegenerative diseases. In these networks, nano-machines are used to replace the damaged segments of the nervous system and they exactly behave like biological entities. In neuro-spike communication, neurons / nano-machines exploit the electro-chemical spikes and molecular communication to transfer information. This communication paradigm can be divided into three main parts, namely the axonal pathway, the synaptic transmission, and the spike generation. In this paper, we focus on the axonal transmission part as a separate channel since the capacity of the axonal pathway has a significant effect on the capacity of neuro-spike communication channel. In thinner axons, the capacity of this part is the bottleneck of the neuro-spike communication channel capacity. Hence, we investigate the restricting factors of the axonal transmission which limit its capacity. We derive the capacity of single-input single-output and multiple-input single-output (MISO) axonal channels. In the MISO case, we investigate the effect of the correlation among inputs on the channel capacity. Moreover, we derive a closed form description for the optimum value of the input spike rate to maximize the capacity of the axonal channel when the information is encoded by firing rate of neurons / nano-machines.

A Communication Theoretical Analysis of Synaptic Multiple-Access Channel in Hippocampal-Cortical Neurons

Communication between neurons occurs via transmission of neural spike trains through junctional structures, either electrical or chemical synapses, providing connections among nerve terminals. Since neural communication is achieved at synapses, the process of neurotransmission is called synaptic communication. Learning and memory processes are based on the changes in strength and connectivity of neural networks which usually contain multiple synaptic connections. In this paper, we investigate multiple-access neuro-spike communication channel, in which the neural signal, i.e., the action potential, is transmitted through multiple synaptic paths directed to a common postsynaptic neuron terminal. Synaptic transmission is initiated with random vesicle release process from presynaptic neurons to synaptic paths. Each synaptic channel is characterized by its impulse response and the number of available postsynaptic receptors. Here, we model the multiple-access synaptic communication channel, and investigate the information rate per spike at the postsynaptic neuron, and how postsynaptic rate is enhanced compared to single terminal synaptic communication channel. Furthermore, we analyze the synaptic transmission performance by incorporating the role of correlation among presynaptic terminals, and point out the postsynaptic rate improvement.

Molecular communication nanonetworks inside human body

To realize molecular nanonetworks, the foundations of molecular information theory should be established through identification of the existing molecular communication mechanisms, and architectures and networking techniques for nanomachines should be developed, which demand novel engineering efforts. Luckily, these engineering skills and technology have been prepared for us by the natural evolution in the last several billions of years. Indeed, the human body is a massive nanoscale molecular communications network as it is composed of billions of interacting nanomachines, i.e., cells. Intra-body biological systems are closely linked to each other and communicate primarily through molecular transactions. Thus, vital activities inside the human body are regulated by everlasting communication performance and operations of intra-body molecular nanonetworks. However, natural intra-body molecular nanonetworks are yet to be explored with the elegant tools of information and communication theories. In this paper, first, the elementary models for significant intra-body molecular communication channels, i.e., nanoscale neuro-spike communication channel, action potential-based cardiomyocyte molecular communication channel, and hormonal molecular communication channel, are introduced. Next, molecular nanonetworks belonging to multi-terminal extensions of channel models, i.e., nervous, cardiovascular molecular, and endocrine nanonetworks are discussed. Furthermore, heterogeneous communication network of intra-body molecular nanonetworks together with five senses, i.e., nanosensory networks, is explored from the perspectives of communication and network theories. Moreover, open research challenges, such as extension of molecular channel models to multi-terminal cases, and developing a communication theory perspective to understand the physiology and to capture potential communication failures of intra-body biological systems, are provided. Our objectives are to learn from the elegant molecular communication mechanisms inside us for engineering practical communication techniques for emerging nanonetworks, as well as to pave the way for the advancement of revolutionary diagnosis and treatment techniques inspired from information and communication technologies, which is promising for future nanomedicine and bio-inspired molecular communication applications.