534 research outputs found
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
The Role Of HR Managers In Developing Intellectual Capital: A Comparative Case Study And Viewpoints On Some Selected Companies
In modern times, the role of human resource (HR) managers has changed as HR policies are planned in accordance with the changing global environment. Their role has expanded to include team building and development of intellectual capital (IC). Workforce diversity has driven many CEOs and HR directors to develop a systematic method for dealing with and ensuring cooperation from the workforce in order to maintain organizational discipline. Rapid advancement, innovations, and changing business trends provide a platform for HR managers to formulate strategies and develop IC to lead the organization in a better manner. This case study research proposes two models - Employees’ Commitment Model (ECM) and Organizational Commitment Model (OCM) - which explain the methodology and techniques for developing IC in modern companies. Further, these models are applied to some selected companies. ECM pertains to developing an individual’s IC, while OCM presents the organizational strategy for adopting and developing IC in modern companies. In a rapidly changing global environment, service-oriented activities are in high demand across sectors. Consequently, HR managers are willing to innovate and invest in IC. Management tools of training, coaching, dealing, and instilling a sense of ownership best develop a company’s IC. Until a few decades back, companies invested in the production processes; later, the focus shifted to technology. Today, the focus is on workers’ knowledge or investment in IC
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
New chromosome numbers in the genus Trigonella L. (Fabaceae)from Turkey
Somatic chromosome numbers of 45 Trigonella L. (Fabaceae), collected from different localities in Turkey was examined. Chromosome numbers were determined as 2n = 14, 16, 30 and 46. B chromosome was also observed in somatic cells of some taxa (Trigonella arcuata C.A. Meyer and Trigonella procumbens (Besser) Reichb.). In addition, one or two satellites were observed in some taxa (Trigonella lunata Boiss., Trigonella velutina Boiss., Trigonella strangulata Boiss., Trigonella crassipes Boiss. and Trigonella cariensis Boiss.).Keywords: Chromosome number, Leguminosae, Trigonell
Transmitter and Receiver Architectures for Molecular Communications: A Survey on Physical Design with Modulation, Coding, and Detection Techniques
Inspired by nature, molecular communications (MC), i.e., the use of molecules to encode, transmit, and receive information, stands as the most promising communication paradigm to realize the nanonetworks. Even though there has been extensive theoretical research toward nanoscale MC, there are no examples of implemented nanoscale MC networks. The main reason for this lies in the peculiarities of nanoscale physics, challenges in nanoscale fabrication, and highly stochastic nature of the biochemical domain of envisioned nanonetwork applications. This mandates developing novel device architectures and communication methods compatible with MC constraints. To that end, various transmitter and receiver designs for MC have been proposed in the literature together with numerable modulation, coding, and detection techniques. However, these works fall into domains of a very wide spectrum of disciplines, including, but not limited to, information and communication theory, quantum physics, materials science, nanofabrication, physiology, and synthetic biology. Therefore, we believe it is imperative for the progress of the field that an organized exposition of cumulative knowledge on the subject matter can be compiled. Thus, to fill this gap, in this comprehensive survey, we review the existing literature on transmitter and receiver architectures toward realizing MC among nanomaterial-based nanomachines and/or biological entities and provide a complete overview of modulation, coding, and detection techniques employed for MC. Moreover, we identify the most significant shortcomings and challenges in all these research areas and propose potential solutions to overcome some of them.This work was supported in part by the European Research Council (ERC) Projects MINERVA under Grant ERC-2013-CoG #616922 and MINERGRACE under Grant ERC-2017-PoC #780645
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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
Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach
This paper proposes a novel, data-agnostic, model poisoning attack on
Federated Learning (FL), by designing a new adversarial graph autoencoder
(GAE)-based framework. The attack requires no knowledge of FL training data and
achieves both effectiveness and undetectability. By listening to the benign
local models and the global model, the attacker extracts the graph structural
correlations among the benign local models and the training data features
substantiating the models. The attacker then adversarially regenerates the
graph structural correlations while maximizing the FL training loss, and
subsequently generates malicious local models using the adversarial graph
structure and the training data features of the benign ones. A new algorithm is
designed to iteratively train the malicious local models using GAE and
sub-gradient descent. The convergence of FL under attack is rigorously proved,
with a considerably large optimality gap. Experiments show that the FL accuracy
drops gradually under the proposed attack and existing defense mechanisms fail
to detect it. The attack can give rise to an infection across all benign
devices, making it a serious threat to FL.Comment: 15 pages, 10 figures, submitted to IEEE Transactions on Information
Forensics and Security (TIFS
240201
This paper proposes a novel, data-agnostic, model poisoning attack on Federated Learning (FL), by designing a new adversarial graph autoencoder (GAE)-based framework. The attack requires no knowledge of FL training data and achieves both effectiveness and undetectability. By listening to the benign local models and the global model, the attacker extracts the graph structural correlations among the benign local models and the training data features substantiating the models. The attacker then adversarially regenerates the graph structural correlations while maximizing the FL training loss, and subsequently generates malicious local models using the adversarial graph structure and the training data features of the benign ones. A new algorithm is designed to iteratively train the malicious local models using GAE and sub-gradient descent. The convergence of FL under attack is rigorously proved, with a considerably large optimality gap. Experiments show that the FL accuracy drops gradually under the proposed attack and existing defense mechanisms fail to detect it. The attack can give rise to an infection across all benign devices, making it a serious threat to FL.info:eu-repo/semantics/publishedVersio
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)
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