295 research outputs found
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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)
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Energy Minimization With Network Coding via Latin Hypercubes
© 2016 IEEE. Network coding is mostly used to achieve the capacity of communication networks. In this paper, motivated by the nanoscale communications where the energy cost for the channel symbols is asymmetric due to the widely employed on-off keying modulation, we design energy-minimizing network codes. We develop the best mapping between the input and output symbols at the network coding node that minimizes the average codeword energy using Latin squares, which we call the minimum energy network code (MENC). We define the class of networks composed of coding nodes with N incoming and 1 outgoing symbols as in-N networks. First, we derive the condition on the network code to minimize the average energy in in-two networks and propose two linear MENCs. Later, we investigate the MENCs for in-N networks using the Latin hypercubes and propose a low-energy network code (LENC) to reduce the average energy with network coding. We compare MENC with the classical XOR and random network codes for in-two networks. The performance comparison between LENC and random network codes for in-N networks shows that the proposed network codes provide significant energy gains
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Utilizing sidelobe ASK based joint radar-communication system under fading
© 2017 IEEE. A joint radar-communication (JRC) system can provide cost-effective and spectrum-efficient platform solution with simultaneous operation, while accomplishing important tasks, sensing via radar processing and allocation of communication links. Existing modulation techniques where information embedding is achieved using sidelobe Amplitude-Shift Keying (ASK) for the JRC system are not investigated so far under fading channels and an optimum threshold estimation algorithm is yet to be developed. Specifying an optimum threshold level under fading can become a comprehensive problem, especially for mobile communication systems. In this paper, a novel non-data aided (NDA) threshold estimation technique and a receiver design are introduced. Furthermore, a new sidelobe ASK modulation technique is proposed for utilizing JRC system for mobile platforms under fading. Proposed modulation technique implements dual Sidelobe Level (SLL) ASK with waveform diversity by exploiting multiple orthogonal waveforms. One pair is modulated with dual SLL in amplitude rotational manner and initiates NDA threshold estimation process at the receiver. This method utilizes K bits of information using only K + 1 orthogonal waveform. The performance of the proposed technique is investigated in terms of the bit error rate (BER) and data rate. Simulations reveal that the operation of proposed method coupled with NDA threshold estimation process can reach more data rate, since it exhibits almost the same BER performance as existing methods under fading channel without requiring more orthogonal waveform
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Information Capacity of Vesicle Release in Neuro-Spike Communication
© 1997-2012 IEEE. Information transmission in the nervous system is performed through the propagation of spikes among neurons, which is done by vesicle release to chemical synapses. Understanding the fundamentals of this communication can lead to the implementation of bio-inspired nanoscale communication paradigms. In this letter, we utilize a realistic pool-based model for vesicle release and replenishment in hippocampal pyramidal neurons and evaluate the capacity of information transmission in this process by modeling it as a binary channel with memory. Then, we derive a recurrence relation for the number of available vesicles, which is used to find successful bit transmission probabilities and mutual information between input and output. Finally, we evaluate the spiking probability that maximizes mutual information and derive the capacity of the channel
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An information theoretical analysis of multi-terminal neuro-spike communication network in spinal cord
© 2018 Association for Computing Machinery. Communication theoretical understanding of healthy and diseased connections in the spinal cord motor system is crucial for realizing future information and communication technology (ICT) based diagnosis and treatment techniques for spinal cord injuries (SCI). A spinal cord motor nucleus associated with a particular muscle constitutes an ideal candidate for studying to have an understanding of SCI. Typical spinal cord motor nucleus system contains pool of lower motor neurons (MNs) controlling a muscle by integrating synaptic inputs from spinal interneurons (INs), upper motor neurons (DNs) and sensory neurons (SNs). In this study, we consider this system from ICT perspective. Our aim is to quantify the rate of information flow across a spinal cord motor nucleus. To this end, we model an equivalent single-hop multiterminal network, where multiple transmitting nodes representing heterogeneous population of DNs, INs and SNs send information to multiple receiving nodes corresponding to MNs. To identify the outputs at receiving nodes, we define corresponding neurospike communication channel and then find the bound on total rates across this network. Based on the network model, we analyze achievable rates for a particular motor nucleus system called Tibialis Anterior (TA) motor nucleus in the spinal cord numerically and simulate several spinal cord dysfunction scenarios. The numerical results reveal that decrease in the maximum total rates with the lower motor neuron injury causes weakness in the affected muscle
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Electric-Field Energy Harvesting in Wireless Networks
© 2002-2012 IEEE. Electric-field energy harvesting (EFEH) can be considered as an emerging and promising alternative for self-sustainable next-generation WSNs. Unlike conventional harvesting methods that rely on ambient variables, EFEH provides more reliable and durable operation as it is operable with any voltage-Applied conductive material. Therefore, it is better suited for advanced throughput and applications requiring a certain QoS. In this article, we introduce this newly emerging WSN paradigm, and focus on enabling EFEH technology for smart grid architectures, such as home, building, and near area networks, where the field intensity is relatively low. To this end, a practical methodology and a general use implementation framework have been developed for low-voltage applications by regarding compelling design issues and challenging source scarcity. The proposed double-layer harvester model is experimentally evaluated. Its performance in terms of implementation flexibility, sensor lifetime, and communication throughput is investigated. In addition, current challenges, open issues, and future research directions are discussed for the design of more enhanced EFEH wireless networks
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
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Rate region analysis of multi-terminal neuronal nanoscale molecular communication channel
© 2017 IEEE. In this paper, we investigate the communication channel capacity among hippocampal pyramidal neurons. To this aim, we study the processes included in this communication and model them with realistic communication system components based on the existing reports in the physiology literature. We consider the communication between two neurons and reveal the effects of the existence of multiple terminals between these neurons on the achievable rate per spike. To this objective, we derive the power spectral density (PSD) of the signal in the output neuron and utilize it to calculate the rate region of the channel. Moreover, we evaluate the impacts of vesicle availability on the achievable rate by deriving the expected number of available vesicles in input neuron using a realistic vesicle release model. Simulation results show that number of available vesicles for release does not affect the achievable rate of neuro-spike communication with univesicular release model. However, in neurons that multiple vesicles can release from each synaptic terminal, achievable rate is significantly affected by depletion of vesicles. Moreover, we show that increasing the number of synaptic terminals between two neurons makes the synaptic connection stronger. Hence, it is an important factor in learning and memory, which occur in the hippocampal region of the brain based on the synaptic connectivity
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300 GHz broadband transceiver design for low-THz band wireless communications in indoor internet of things
© 2017 IEEE. This paper presents the architectural design of a 300 GHz transceiver system that can be used to explore the high speed communication opportunities offered by the Terahertz (THz) band for advanced applications of Internet-of-Things (IoT). We use low cost industry ready components to prepare a fully customizable THz band communication system that provides a bandwidth of 20 GHz that is easily extendable up to 40 GHz. Component parameters are carefully observed and used in simulations to predict the system performance while the compatibility of different components is ensured to produce a reliable design. Our results show that the receiver provides a conversion gain of 51 dB with a noise figure (NF) of 9.56 dB to achieve a data rate of 90.31 Gbps at an operation range of 2 meters, which is suitable for high speed indoor IoT nodes. The flexible design of the transceiver provides groundwork for further research efforts in 5G IoT applications and pushing boundaries of throughputs to the order of terabits per second (Tbps)
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