Communication in nano devices: Electronic based biophysical model of a neuron

Abstract

Investigating new strategies and signaling techniques for nano-devices and systems is quite challenging. The communication systems considered to be feasible in nano-devices are inspired from biophysical systems which communicate with electro-chemical signals organized with respect to excitation. While the electrical pulses transmitted along with the cell membrane the chemical signal transmitted in the synaptic cleft. Developing new chemical signal based communication which termed as the molecular communication with minimum error is now the central deal for the researchers. Strategic approaches to the issue in variety of perspective such as systematic, experimental and electronic circuitry viable for chip based robotic and nano-device design are now available in the literature. Biological signaling pathways, in accordance with the action potentials generated in pre-synaptic neuron some chemical substances called neurotransmitters released into the synaptic cleft and hence the post-synaptic neuron is accordingly triggered. In this way the information transmitted from one cell to another by electro chemical signal carriers. About this process some electronic neuron models have also been introduced to simulate dynamic behavior of neuronal cells. In this study, a novel simple electronic integrate and fire model which has been designed previously was further developed and used to simulate and analyze the communication of neurons. The proposed electronic model not only simulates the neuronal cell’s behavior and also can transmit the information to the following neuron. The rate of correct transmission depends on the synaptic channel model. The characteristics of the used semiconductor components with overall structure of the proposed electronic model are very close to the biophysical nature of neuron and can be designed on semiconductor chips which is the advantage of the model.

Introduction

With the introduction of nano-devices and systems the need for advanced nano-scale signaling methods increases. The well-developed methods used in modern digital communication technologies involving coding, modulation, detection, decoding and correction are not usually sufficient to solve the issues concerned with the communication of nano-devices. Table 1 shows a comparison of modern digital and nano-device communication systems in fundamental terms. In this direction, researchers are continuously working on investigating some new communication techniques, generally inspired from biological systems. All of living beings, even in cellular scale, use variety forms of biophysical transceivers on their particular communication pathways which naturally developed for information exchange and control in and between organs. The information transmission from one neuronal cell to another involves both electrical and chemical signals as a hybrid carriers of information. While the electrical information carriers are in the format of spikes with temporal and pulse rate modulated fashion (pulse position pulse frequency modulation) propagating in terms of spike-streams through the neuronal network, the chemical signals are in the form of molecules, ions as well as particular chemical structures called neurotransmitters carry information in the fluid region between neurons called synaptic cleft [1], [2]. This chemical signals are in general modulated in terms of rate and format of chemical release into the synaptic cleft by electrical spikes propagating along with the axonal ends called dendrites. However, the propagation speed and shape (delay and attenuation) of electro-chemical pulses through axon and cleft somehow could alter within limits due to the stochastic properties of axon. These tolerable alteration could be termed as a kind of biological stochastic noise [3], [4].

Similar to the neuronal information transmission, the molecular communication (MC) that have been proposed for nano-devices today, uses chemical signals as the carrier of information in gaseous or liquid medium through which molecules propagate with Brownian Motion scheme. The molecules are released into the medium and transmitted through the fluid channel and recognized by receptors set on the receiver’s surface in a probabilistic manner as schematically shown in Fig. 1.

The challenging side in MC is to receive information with minimum error at the receiver side. The error is significantly increases with the increase of inter symbol interference (ISI) and noise that pollutes molecular channel as in digital communication systems. Since wet laboratory based studies are quite expensive, usually theoretical and simulation based studies are conducted to examine and accordingly increase the performance of MC. While some researchers work on modeling molecular transmitters some others work on modeling synaptic channel and receivers for maximization of the received signal in terms of hitting probability of chemical signal on the receiver’s surface where receptors are cited [5]. The diffusion of chemical signal through synaptic channel is considered to be in the format of Brownian Motion. Through a detailed review in this concept, it is realized that using electronic neuron models instead of mathematical simulations is going to be feasible for analyzing and hence increasing the performance of cellular based communication.

In this paper therefore, an electronic based model of the neuron [6] which possess integrate and fire behavior was used to assess the performance of neuronal communication which also includes MC. The model comprises some basic electronic components and possesses electronic properties which are very close to the ionic properties of a real nerve cells. The model composed of two successive parts, while the former is representing soma consisting of nonlinear electronic channels and their controlling elements, the later is representing axonal tail modeled in the format of transmission cable models with the ignorance of series resistances. The model works with none or all behavior alike neuronal cells and the signal at the output of the model very well simulates the output of real neurons. The inter neuron cleft was modeled with a parallel connected RC circuit showing the capacitance (simulates the permeability) and resistance (simulates the conductivity) of the diffusive medium for molecule transport.

The organizations of this paper is as follows. In Section 2, the general communication of neurons is discussed. In Section 3, some related works introduced to the literature so far is reviewed. In Section 4, the design of proposed electronic model of soma and axon are discussed, and the model is demonstrated for a few scenarios. In Section 5, the obtained results are discussed. Finally the conclusion of the study is made.