Simulator Development for Neuron-based Molecular Communication Jun Suzuki UMass Boston

Simulator Development forNeuron-based

Molecular Communication

Jun SuzukiUMass Boston

Background

Nanoscale Communication

• An emerging research paradigm that aims to provide communication capabilities between nanoscale machines (nanomachines).• The first publication at 2005

• Nanomachine• The most basic functional unit in nanoscale

systems.• Scale: one to a few hundred nanometers. • Consists of biological materials (e.g.,

molecules) and perform very simple computation, sensing and/or actuation tasks.

Nanomachines

Two Major Approaches toNanoscale Communication

• Electromagnetic communication• Top-down miniaturization approach

• Molecular communication• Bottom-up approach• Inspired by the communication mechanisms that

naturally occur among living cells.• Utilizes molecules as a communication medium. • Several advantages over electromagnetic

communication• Inherent nanometer scale, biocompatibility and energy

efficiency• A major application domain: in-body nanonetworks• Nanomachines are networked through molecular

communication to perform sensing and actuation tasks in the body for biomedical and prosthetic purposes.• e.g., vital information sensing, targeted drug release and neural

signal augmentation.

Short- and Long-rangeMolecular Communication

• Short-range communication• nanometers to millimeters• Uses molecular motors, calcium signaling

and bacteria communication

• Long-range communication• millimeters to meters• Uses neurons.

Neurons

• Fundamental components of the nervous system.– The central nervous system

• the brain, spinal cord and retina

– The peripheral nervous system• sensory neurons, clusters of neurons

called ganglia

• Electrically excitable cells• Process and transmit information by

electrical and chemical signaling.

The Structure of an Neuron

• Cell body (soma)– 4 to 100 um in diameter

• Dendrites• Up to a few hundred um in length• Thin structures that arise from the soma• Form a complex “dendritic tree.”• the majority of inputs to a neuron • Generate the majority of inputs to a neuron.

Dendrite

Nucleus

Soma

Axon

Myelin sheath

Shwann cell

Node ofranvier

Axon terminal

• An axon• Up to 1 m in humans• A special cellular extension that arises from the soma.• Branches hundreds, or even thousands, of times before it terminates. • Travels thru the body in bundles called nerves.

Dendrite

Nucleus

Soma

Axon

Myelin sheath

Shwann cell

Node ofranvier

Axon terminal

Neuronal Network

• Neurons self-organize to connect each other and form a network.

Neuron-to-Neuron Communication

• Neurons communicate with others via synapses.• A synapse

– A membrane-to-membrane junction– Contains molecular machinery that allows a (presynaptic) neuron to

transmit a chemical signal to another (postsynaptic) neuron. • An axon makes thousands of synaptic contacts.

Synapse

Presynaptic Neuron

Postsynaptic Neuron

Signal

Neurotransmitters

• When an axon terminal is stimulated with an electric signal, it initiates a chemical process in a synapse. – Generates neurotransmitter

molecules.• e.g., Acetylcholine (ACh)

• Neurotransmitters electrically excites a postsynaptic neuron.

Action Potential

• When a neuron is electrically excited, it generates an electrochemical pulse called action potential.

• The refractory period follows a signal transmission.

• Signal transmission speed: 90 m (295 fts, 98 yds)/second

Neuron-based IBSANs

The Proposed Communication Framework for Neuron-based IBSANs

• Neuronal TDMA– TDMA (Time Division Multiple Access) protocol framework

TDMA Optimization

• Which neuron should start signaling in a given time slot?– Assumption: Each neuron transmits at least one signal in the signaling

cycle.

• Optimization objectives– Signaling yield

• How many signals the sink receives from all sensors during the scheduling cycle.– Signaling fairness among sensors

• How fairly/equally sensors access the shared neuronal network. – Signaling delay

• A multiobjective optimization problem

Current Status

• A multiobjective genetic algorithm (GA) has been developed and tested. – Can optimize TDMA scheduling by balancing the trade-offs among

3 objectives.

What’s the Future of MolCom?

• Nanomachine development has been actively carried out in the academia and industry.

• Molecular communication protocols/mechanisms have been studied through simulations in the academia since mid ’00.

• Practical nanomachines will be developed in 10 (?) years.

• Molecular communication will be integrated with nanomachines.

• Applications– Metical monitoring– Prosthetic and rehabilitation devices

Cellular System Development and TDMA

• TDMA– is/was used in 2G cellular systems such as GSM. – is still widely used in satellite systems.

– was extensively studied in the academia for wireless communication in 70s and early 80s

• through simulations.

• GSM– The standardization of GSM started in ’82 and completed in ’90.– The first GSM call in 1991.– Practically usable cell phones became available on the market around 1993 -

1995. – GSM subscribers worldwide passed 10M in 1995, 100M in 1998, 500M in 2001,

1B in 2004 and 2B in 2008.– Has been fading out as 3G and 4G systems emerge.

CS682/3 Project:A Generic Simulation Environment for

Neuronal TDMA

Architectural Overview

– Neuronal Network Visualizer/Editor • Aids the user to manually draw neuronal networks in 2D/3D

• Performs several visualization effects

• e.g., zoom in, zoom out, etc.

• Generates/serializes a text file (in XML?) according to a drawn neuronal network and stores it in the local disk or a remote cloud (e.g., Dropbox and Google Drive).

• Reads a text file and visualize a neuronal network.

Manual 2D/3Dtopology editor

GUIFrontend

AutomaticAutomaticTopology Topology GeneratorGenerator

Topology Topology DataData

GeneratorGenerator

Topology Topology DataData

ReaderReader

Topology Topology DataData

(XML?)(XML?)

OptimizatioOptimizationn

CodeCodeOptimizatioOptimizatio

nnCodeCode

Opt. Algo.Opt. Algo.(NSGA-II)(NSGA-II)

Opt. Algo.Opt. Algo.(Simplex)(Simplex)

Opt. EngineOpt. Engine(jMetal)(jMetal)

Opt. EngineOpt. Engine(GLPK)(GLPK)

– Automatic Topology Generator• Artificially grow neuronal networks based on a topology construction algorithm(s) and serializes

their topology information in text files (XML?).

• Optimization Code• I/F code to run optimization algorithms with a given neuronal network topology.

Manual 2D/3Dtopology editor

GUIFrontend

AutomaticAutomaticTopology Topology GeneratorGenerator

Topology Topology DataData

GeneratorGenerator

Topology Topology DataData

ReaderReader

Topology Topology DataData

(XML?)(XML?)

OptimizatioOptimizationn

CodeCodeOptimizatioOptimizatio

nnCodeCode

Opt. Algo.Opt. Algo.(NSGA-II)(NSGA-II)

Opt. Algo.Opt. Algo.(Simplex)(Simplex)

Opt. EngineOpt. Engine(jMetal)(jMetal)

Opt. EngineOpt. Engine(GLPK)(GLPK)

What You can Gain from this Project.

• Extensive Java programming– GUI, 2D/3D visualization, text data handling, optimization, self-

organizing topology formation

• Very futuristic research experience– Potential co-authored publications (if you are interested)