A Communication Theoretical Modeling of Axonal Propagation in Hippocampal Pyramidal Neurons

Modeling and Evaluation of Vesicle Release Mechanisms in Neuro-Spike Communication

Neuro-spike communication (NSC) is a communication method that includes electrical communication process and molecular communication process, which has been investigated extensively in recent years. The vesicle release process has a great influence on the accuracy of NSC systems. So the modeling of the vesicle release process has become a hot spot. There exist different vesicle release mechanisms, including univesicular release (UVR) case, multivesicular release (MVR) case and hybrid vesicle release (HVR) case. When a spike arrives, the UVR case refers to that at most one vesicle can be released. The MVR case refers to that more than one vesicle can be released. The HVR case is a mixed case of the UVR and MVR cases. This paper proposes the modeling of these three vesicle release cases. The theoretical analysis is conducted to compare them in terms of efficiency. Simulations are performed to evaluate the impacts of main vesicle releasing parameters on signal transmission accuracy. The simulation results show that the HVR model can effectively improve the transmission accuracy compared with UVR and MVR models under some conditions.

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.

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.

Distortion Distribution of Neural Spike Train Sequence Matching With Optogenetics

OBJECTIVE

This paper uses a simple optogenetic model to compare the timing distortion between a randomly-generated target spike sequence and an externally-stimulated neuron spike sequence. Optogenetics is an emerging field of neuroscience where neurons are genetically modified to express light-sensitive receptors that enable external control when the neurons fire.

METHODS

Two different measures are studied to determine the timing distortion. The first measure is the delay in externally-stimulated spikes. The second measure is the root-mean-square-error between the filtered outputs of the target and stimulated spike sequences.

RESULTS

The mean and the distribution of the distortion are derived in closed form when the target sequence generation rate is sufficiently low. The derived results are verified with simulations.

CONCLUSION

The proposed model and distortion measures can be used to measure the deviation between neuron spike sequences that are prescribed and what can be achieved via external stimulation.

SIGNIFICANCE

Given the prominence of neuronal signaling within the brain and throughout the body, optogenetics has significant potential to improve the understanding of the nervous system and to develop treatments for neurological diseases. This work is a step towards an analytical model to predict whether different spike trains were observed from the same external stimulus, and the broader goal of understanding the quantity and reliability of information that can be carried by neurons.

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.

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.