Sum Rate of MISO Neuro-Spike Communication Channel With Constant Spiking Threshold

Communication among neurons, known as neuro-spike communication, is the most promising technique for realization of a bio-inspired nanoscale communication paradigm to achieve biocompatible nanonetworks. In neuro-spike communication, the information, encoded into spike trains, is communicated to various brain regions through neuronal network. An output neuron needs to receive signal from multiple input neurons to generate a spike. Hence, in this paper, we aim to quantify the information transmitted through the multiple-input single-output (MISO) neuro-spike communication channel by considering models for axonal propagation, synaptic transmission, and spike generation. Moreover, the spike generation and propagation in each neuron requires opening and closing of numerous ionic channels on the cell membrane, which consumes considerable amount of ATP molecules called metabolic energy. Thus, we evaluate how applying a constraint on available metabolic energy affects the maximum achievable mutual information of this system. To this aim, we derive a closed form equation for the sum rate of the MISO neuro-spike communication channel and analyze it under the metabolic cost constraints. Finally, we discuss the impacts of changes in number of pre-synaptic neurons on the achievable rate and quantify the tradeoff between maximum achievable sum rate and the consumed metabolic energy.

[1]  S. Laughlin,et al.  An Energy Budget for Signaling in the Grey Matter of the Brain , 2001, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[2]  P. Jonas,et al.  Dynamic Control of Presynaptic Ca2+ Inflow by Fast-Inactivating K+ Channels in Hippocampal Mossy Fiber Boutons , 2000, Neuron.

[3]  Özgür B. Akan,et al.  Information Theoretical Analysis of Synaptic Communication for Nanonetworks , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

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

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

[6]  Ozgur B. Akan,et al.  Rate region analysis of multi-terminal neuronal nanoscale molecular communication channel , 2017, 2017 IEEE 17th International Conference on Nanotechnology (IEEE-NANO).

[7]  D. Attwell,et al.  Energy-Efficient Information Transfer by Visual Pathway Synapses , 2015, Current Biology.

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

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

[10]  Ozgur B. Akan,et al.  A Communication Theoretical Modeling of Axonal Propagation in Hippocampal Pyramidal Neurons , 2017, IEEE Transactions on NanoBioscience.

[11]  A. L. Edelstein,et al.  Collective binding properties of receptor arrays. , 1997, Biophysical journal.

[12]  Shigeru Shinomoto,et al.  Made-to-Order Spiking Neuron Model Equipped with a Multi-Timescale Adaptive Threshold , 2009, Front. Comput. Neurosci..

[13]  Martin Stemmler,et al.  Power Consumption During Neuronal Computation , 2014, Proceedings of the IEEE.

[14]  Thomas A. Nielsen,et al.  Modulation of Glutamate Mobility Reveals the Mechanism Underlying Slow-Rising AMPAR EPSCs and the Diffusion Coefficient in the Synaptic Cleft , 2004, Neuron.

[15]  C. Stevens,et al.  Heterogeneity of Release Probability, Facilitation, and Depletion at Central Synapses , 1997, Neuron.

[16]  Oscar Herreras,et al.  The Influence of Synaptic Size on AMPA Receptor Activation: A Monte Carlo Model , 2015, PloS one.

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

[18]  Ozgur B. Akan,et al.  Information Capacity of Vesicle Release in Neuro-Spike Communication , 2018, IEEE Communications Letters.

[19]  Ryota Kobayashi,et al.  Optimal decoding and information transmission in Hodgkin-Huxley neurons under metabolic cost constraints , 2015, Biosyst..

[20]  Ozgur B. Akan,et al.  Diffusion-Based Model for Synaptic Molecular Communication Channel , 2017, IEEE Transactions on NanoBioscience.

[21]  O. Hornykiewicz.  Biochemical aspects of Parkinson's disease , 1998, Neurology.

[22]  Robert Plonsey,et al.  Bioelectricity: A Quantitative Approach Duke University’s First MOOC , 2013 .

[23]  Barry W. Connors,et al.  Neuroscience: Exploring the brain, 3rd ed. , 2007 .

[24]  T. Schikorski,et al.  Quantitative Ultrastructural Analysis of Hippocampal Excitatory Synapses Materials and Methods Terminology Fixation and Embedding , 2022 .

[25]  Mark D. McDonnell,et al.  Metabolic cost of neuronal information in an empirical stimulus-response model , 2013, Biological Cybernetics.

[26]  Terrence J. Sejnowski,et al.  Kinetic models for synaptic interactions , 2002 .

[27]  L. Savtchenko,et al.  The optimal height of the synaptic cleft , 2007, Proceedings of the National Academy of Sciences.

[28]  Murat Kuscu,et al.  Fundamentals of Molecular Information and Communication Science , 2017, Proceedings of the IEEE.

[29]  C. Stevens,et al.  Reversal of synaptic vesicle docking at central synapses , 1999, Nature Neuroscience.

[30]  M. Fernandez,et al.  Closed-Form Expression for the Poisson-Binomial Probability Density Function , 2010, IEEE Transactions on Aerospace and Electronic Systems.

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