Axonal Channel Capacity in Neuro-Spike Communication

Novel nano-scale communication techniques are inspired by biological systems. Neuro-spike communication is an example of this communication paradigm which transfers vital information about external and internal conditions of the body through the nervous system. The analysis of this communication paradigm is beneficial to exploit in the artificial neural systems where nano-machines are linked to neurons to treat the neurodegenerative diseases. In these networks, nano-machines are used to replace the damaged segments of the nervous system and they exactly behave like biological entities. In neuro-spike communication, neurons / nano-machines exploit the electro-chemical spikes and molecular communication to transfer information. This communication paradigm can be divided into three main parts, namely the axonal pathway, the synaptic transmission, and the spike generation. In this paper, we focus on the axonal transmission part as a separate channel since the capacity of the axonal pathway has a significant effect on the capacity of neuro-spike communication channel. In thinner axons, the capacity of this part is the bottleneck of the neuro-spike communication channel capacity. Hence, we investigate the restricting factors of the axonal transmission which limit its capacity. We derive the capacity of single-input single-output and multiple-input single-output (MISO) axonal channels. In the MISO case, we investigate the effect of the correlation among inputs on the channel capacity. Moreover, we derive a closed form description for the optimum value of the input spike rate to maximize the capacity of the axonal channel when the information is encoded by firing rate of neurons / nano-machines.

[1]  Amit Manwani.  Information-theoretic analysis of neuronal communication , 2000 .

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

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

[4]  A. Aldo Faisal,et al.  Stochastic Simulation of Neurons, Axons, and Action Potentials , 2009 .

[5]  Izhak Rubin,et al.  Regular point processes and their detection , 1972, IEEE Trans. Inf. Theory.

[6]  Ilangko Balasingham,et al.  Peer-to-Peer Communication in Neuronal Nano-Network , 2016, IEEE Transactions on Communications.

[7]  C. Stevens,et al.  Origin of variability in quantal size in cultured hippocampal neurons and hippocampal slices. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

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

[9]  Behrouz Maham,et al.  Axonal transmission analysis in neuro-spike communication , 2017, 2017 IEEE International Conference on Communications (ICC).

[10]  Tomás Palacios,et al.  Applications of graphene devices in RF communications , 2010, IEEE Communications Magazine.

[11]  G. Shepherd,et al.  Single‐axon action potentials in the rat hippocampal cortex , 2003, The Journal of physiology.

[12]  Toby Berger,et al.  A Mathematical Theory of Energy Efficient Neural Computation and Communication , 2010, IEEE Transactions on Information Theory.

[13]  A. Aldo Faisal,et al.  Stochastic Simulations on the Reliability of Action Potential Propagation in Thin Axons , 2007, PLoS Comput. Biol..

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

[15]  Ilangko Balasingham,et al.  On the Upper Bound of the Information Capacity in Neuronal Synapses , 2016, IEEE Transactions on Communications.

[16]  Behrouz Maham,et al.  Capacity bounds of neuro-spike communication by exploiting temporal modulations , 2018, 2018 IEEE Wireless Communications and Networking Conference (WCNC).

[17]  L. P. O'Keefe,et al.  Processing of first- and second-order motion signals by neurons in area MT of the macaque monkey , 1998, Visual Neuroscience.

[18]  Thomas Kailath,et al.  The modeling of randomly modulated jump processes , 1975, IEEE Trans. Inf. Theory.

[19]  I. Akyildiz,et al.  Graphene-based nano-antennas for electromagnetic nanocommunications in the terahertz band , 2010, Proceedings of the Fourth European Conference on Antennas and Propagation.

[20]  Behrouz Maham,et al.  Error probability analysis of neuro-spike communication channel , 2017, 2017 IEEE Symposium on Computers and Communications (ISCC).

[21]  Don H. Johnson,et al.  Information Theory and Neural Information Processing , 2010, IEEE Transactions on Information Theory.

[22]  Massimiliano Pierobon,et al.  A physical end-to-end model for molecular communication in nanonetworks , 2010, IEEE Journal on Selected Areas in Communications.

[23]  S. Laughlin,et al.  Ion-Channel Noise Places Limits on the Miniaturization of the Brain’s Wiring , 2005, Current Biology.

[24]  C. Beenakker,et al.  Sub-Poissonian shot noise in graphene. , 2006, Physical review letters.

[25]  J. White,et al.  Channel noise in neurons , 2000, Trends in Neurosciences.

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

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