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

© 2019 Elsevier B.V. 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.This work was supported in part by European Research Council (ERC) under grant ERC-2013-CoG 616922 (Project MINERVA) and ERC-2017-PoC 780645 (ERC Proof of Concept project MINRGRACE)

Information Theoretical Analysis of Synaptic Communication for Nanonetworks

© 2018 IEEE. Communication among neurons is the highly evolved and efficient nanoscale communication paradigm, hence the most promising technique for biocompatible nanonetworks. This necessitates the understanding of neuro-spike communication from information theoretical perspective to reach a reference model for nanonetworks. This would also contribute towards developing ICT-based diagnostics techniques for neuro-degenerative diseases. Thus, in this paper, we focus on the fundamental building block of neuro-spike communication, i.e., signal transmission over a synapse, to evaluate its information transfer rate. We aim to analyze a realistic synaptic communication model, which for the first time, encompasses the variation in vesicle release probability with time, synaptic geometry and the re-uptake of neurotransmitters by pre-synaptic terminal. To achieve this objective, we formulate the mutual information between input and output of the synapse. Then, since this communication paradigm has memory, we evaluate the average mutual information over multiple transmissions to find its overall capacity. We derive a closed-form expression for the capacity of the synaptic communication as well as calculate the capacity-achieving input probability distribution. Finally, we find the effects of variation in different synaptic parameters on the information capacity and prove that the diffusion process does not decrease the information a neural response carries about the stimulus in real scenario

Energy-efficient modulation and physical layer design for low terahertz band communication channel in 5G femtocell Internet of Things

© 2018 Elsevier B.V. High throughput capability of the terahertz band (0.3–10 THz) wireless communications is expected to be utilized by the fifth generation of mobile telecommunication systems and enable a plethora of new applications. Supporting devices will transfer large amounts of data in both directions, causing high energy consumption by the electronic circuitries of the equipment in use. Therefore, physical layer for these systems must be designed carefully in order to reduce energy consumption per bit. In this paper, the best performing modulation scheme and hardware parameters that minimize the energy consumption without affecting the system throughput are determined. THz band device technologies are outlined and a complete survey of the state-of-the-art low-THz band circuit blocks which are suitable for mass market production is given. It is shown that for short-range communications, M-ary quadrature amplitude modulation is the most energy-efficient technique that can lead up to 90% reduction in consumed energy. Moreover, optimal transceiver parameters which can be used to further minimize the energy consumption of the THz band system are examined

CAPITAL COST AND SUSTAINABILITY OF SMALL AND MEDIUM-SCALE ENTERPRISES (SMEs) IN NIGERIA

This study conceptually examines Small and Medium Enterprises (SMEs) funding issues, particularly in emerging nations using Nigeria as a case study. It analyses institutional barriers to funding small startup businesses, such as the high cost of capital, inadequate collateral and weak legal structures. It emphasizes the deficiencies they face regarding high maintenance costs, poor managerial experience, high competition from foreign firms, Government’s inability in financing small and medium firms for them to attain their full capacity, weak Policies, changes in unstable tax tariff, and finally suggesting ways that can improve finance access for small and medium firms. The study recommends that individuals should acquire the necessary knowledge and skills pertaining to their chosen business endeavor through participation in entrepreneurial training programs, that government should promote the enhancement of SMEs capabilities through the localization of supply chains, encourage effective leadership at the highest organizational level and should be encouraged by the localization of value creation by actively engaging with SMEs. It also recommended that the government should increase the campaigns of elucidating the significance of taxation in fostering the growth of nations, while concurrently providing assistance to small and medium firms in comprehending the tax legislation in Nigeria

Fundamentals of molecular information and communication science

© 1963-2012 IEEE. Molecular communication (MC) is the most promising communication paradigm for nanonetwork realization since it is a natural phenomenon observed among living entities with nanoscale components. Since MC significantly differs from classical communication systems, it mandates reinvestigation of information and communication theoretical fundamentals. The closest examples of MC architectures are present inside our own body. Therefore, in this paper, we investigate the existing literature on intrabody nanonetworks and different MC paradigms to establish and introduce the fundamentals of molecular information and communication science. We highlight future research directions and open issues that need to be addressed for revealing the fundamental limits of this science. Although the scope of this development encompasses wide range of applications, we particularly emphasize its significance for life sciences by introducing potential diagnosis and treatment techniques for diseases caused by dysfunction of intrabody nanonetworks

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

Analysis of information flow in MISO neuro-spike communication channel with synaptic plasticity

Communication among neurons is the most promising technique for biocompatible nanonetworks. This 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 their 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. Hence, in this paper, we provide a comprehensive model for multiple-input single-output (MISO) neuro-spike communication by integrating the spike timing dependent plasticity (STDP) into existing channel model. We simulate this model for a realistic scenario with correlated inputs and varying spiking threshold. We show that plasticity is strengthening the correlated input synapses at the expense of weakening the synapses with uncorrelated inputs. Moreover, a nonlinear behavior in signal transmission is observed with changing spiking threshold.This work was supported in part by the ERC projects MINERVA (ERC-2013-CoG #616922) and the ERC Proof of Concept project MINRGRACE (ERC-2017-PoC #780645)

Energy Neutral Internet of Drones

Extensive use of amateur drones (ADrs) poses a threat to the public safety due to their possible misuse. Hence, surveillance drones (SDrs) are utilized to detect and eliminate potential threats. However, limited battery, and lack of efficient communication and networking solutions degrade the quality of surveillance. To this end, we conceptualize the Energy Neutral Internet of Drones (enIoD) to enable enhanced connectivity between drones by overcoming energy limitations for autonomous and continuous operation. Power provisioning with recharging stations is introduced by wireless power transfer to energize the drones. Renewable energy harvesting is utilized to realize energy neutrality, which is minimization of deficit in harvested and consumed energy in enIoD. Communication and networking architectures and protocols for realization of multi-dimensional objectives are presented. Finally, possible application areas are explained with a case study to show how enIoD operates