A Communication Theoretic Analysis of Synaptic Channels Under Axonal Noise

Molecular communication is an emerging communication technology for applications requiring nanoscale networks. Transferring vital information about external and internal conditions of the body through the nervous system is an important type of intra-body molecular nanonetworks. Thus, investigating the performance of such systems from the communication theoretic perspective gives us insight on the limitation of neuro-spike communication and ways to design artificial neural systems. In this letter, we study the performance of the neuro-spike communication under different stochastic impairments such as axonal shot noise, synaptic noise, and random vesicle release. The objective is to optimally detect the spikes at the receiving neuron. Since several uncertainties occur under each hypothesis, composite hypothesis is employed to find the optimum detection policy. Furthermore, we obtain closed-form solutions for the optimal detector and derive the binary decision error at the postsynaptic terminal.

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

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

[3]  C. Helstrom,et al.  Statistical theory of signal detection , 1968 .

[4]  T. Branco,et al.  The probability of neurotransmitter release: variability and feedback control at single synapses , 2009, Nature Reviews Neuroscience.

[5]  J. Bekkers,et al.  Quantal amplitude and quantal variance of strontium‐induced asynchronous EPSCs in rat dentate granule neurons , 1999, The Journal of physiology.

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

[7]  A. Aldo Faisal,et al.  Axonal Noise as a Source of Synaptic Variability , 2014, PLoS Comput. Biol..

[8]  Peter Dayan,et al.  Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .

[9]  Andrew W. Eckford,et al.  Channel and Noise Models for Nonlinear Molecular Communication Systems , 2013, IEEE Journal on Selected Areas in Communications.

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

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

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

[13]  Laura Galluccio,et al.  Characterization of signal propagation in neuronal systems for nanomachine-to-neurons communications , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).