Detection in neuronal communications with finite channel state

Nano-networks are the key factors in developing future nano-machines. The use of molecules to transmit information in such networks is the subject of molecular communications. The future of the nano-networks depends on the development of the molecular communications. Neuro-synaptic communication, which models the data transmission in the body nervous system, is an important example of molecular communication. In this paper, we introduce a comprehensive channel model for the neuro-synaptic communication channel. Our model incorporates the effect of different channel states and spike rates that exist in the body nervous system. We propose a finite state Markov channel scheme for the communication system. This scheme has a two dimensional state space according to the neural firing rate and the number of available carrier resources. Next, we suggest an M-ary signaling scheme for the synaptic area that models the synaptic multi-site activity. In this activity, several neuronal endpoints participate in the neurotransmitter release process. Moreover, we obtain a closed-form solution for the detector at the destination neuron. Finally, we evaluate the decision rules in the detector with simulations.

[1]  X. Wang,et al.  Implications of All-or-None Synaptic Transmission and Short-Term Depression beyond Vesicle Depletion: A Computational Study , 2000, The Journal of Neuroscience.

[2]  Behrouz Maham,et al.  A Communication Theoretic Analysis of Synaptic Channels Under Axonal Noise , 2015, IEEE Communications Letters.

[3]  Jerry M. Mendel,et al.  Maximum a posteriori estimation of multichannel Bernoulli-Gaussian sequences , 1989, IEEE Trans. Inf. Theory.

[4]  Chin-Hui Lee,et al.  Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains , 1994, IEEE Trans. Speech Audio Process..

[5]  W. Wildman,et al.  Theoretical Neuroscience , 2014 .

[6]  Laura Galluccio,et al.  Modeling signal propagation in nanomachine-to-neuron communications , 2011, Nano Commun. Networks.

[7]  Behrouz Maham,et al.  Inter-Neuron Interference Analysis in Neuro-Synaptic Communications , 2017, IEEE Communications Letters.

[8]  Christof Koch,et al.  Coding of Time-Varying Signals in Spike Trains of Integrate-and-Fire Neurons with Random Threshold , 1999, Neural Computation.

[9]  Brent Doiron,et al.  Short Term Synaptic Depression Imposes a Frequency Dependent Filter on Synaptic Information Transfer , 2012, PLoS Comput. Biol..

[10]  S. Mennerick,et al.  Action potential initiation and propagation in CA3 pyramidal axons. , 2007, Journal of neurophysiology.

[11]  Özgür B. Akan,et al.  A Physical Channel Model for Nanoscale Neuro-Spike Communications , 2013, IEEE Transactions on Communications.

[12]  Behrouz Maham,et al.  Inter-Symbol Interference analysis in neuro-synaptic communications , 2016, 2016 8th International Symposium on Telecommunications (IST).

[13]  Joseph Lipka,et al.  A Table of Integrals , 2010 .

[14]  J.E. Mazo,et al.  Digital communications , 1985, Proceedings of the IEEE.

[15]  Jianfeng Feng,et al.  Computational neuroscience , 1986, Behavioral and Brain Sciences.

[16]  C. Koch,et al.  Encoding of visual information by LGN bursts. , 1999, Journal of neurophysiology.

[17]  I. S. Gradshteyn,et al.  Table of Integrals, Series, and Products , 1976 .

[18]  Ian F. Akyildiz,et al.  Nanonetworks: A new communication paradigm , 2008, Comput. Networks.

[19]  Pravin Varaiya,et al.  Capacity, mutual information, and coding for finite-state Markov channels , 1996, IEEE Trans. Inf. Theory.

[20]  John G. Proakis,et al.  Digital Communications , 1983 .

[21]  Özgür B. Akan,et al.  A Communication Theoretical Analysis of Synaptic Multiple-Access Channel in Hippocampal-Cortical Neurons , 2013, IEEE Transactions on Communications.

[22]  Giovanni Sparacino,et al.  Maximum-likelihood versus maximum a posteriori parameter estimation of physiological system models: the c-peptide impulse response case study , 2000, IEEE Transactions on Biomedical Engineering.

[23]  R. Mann,et al.  Human Physiology , 1839, Nature.

[24]  Christof Koch,et al.  Detecting and Estimating Signals over Noisy and Unreliable Synapses: Information-Theoretic Analysis , 2001, Neural Computation.

[25]  Kerstin Vogler,et al.  Table Of Integrals Series And Products , 2016 .

[26]  David C. Paris Capacity , 2019, Change: The Magazine of Higher Learning.

[27]  Robert Rosenbaum,et al.  The impact of short term synaptic depression and stochastic vesicle dynamics on neuronal variability , 2012, Journal of Computational Neuroscience.

[28]  Laura Galluccio,et al.  Characterization of molecular communications among implantable biomedical neuro-inspired nanodevices , 2013, Nano Commun. Networks.