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Investigations on the Nervous System of
Caenorhabditis elegans
Ramin M. Hasani, Lukas Esterle, and Radu Grosu
Vienna University of Technology, Vienna, Austria,
{ramin.hasani, lukas.esterle, radu.grosu}@tuwien.ac.at
Introduction – Understanding the working principle of nervous systems of
living species has long been a huge source of inspiration in the artificial intel-
ligence (AI) community. In particular, the interpretation of reflexive behavior
employing neural circuits. Reflex is considered to be the fundamental reason of
many physiological behaviors in physical organs of creatures. High-level synchro-
nization of the neural activities as well as having an understanding of particular
physical action, is essential to represent such behavior in the brain. Providing an
artificial platform that resembles the brain under which one is able to identify
the corresponding working principles of such orchestrating neural activities is an
extremely helpful step towards decoding the brain´s perception of behavior.
Accordingly, the nervous system of the nematode Caenorhabditis elegans (C.
elegans) is a suitable system to be modeled due to its simplicity as it only has
302 identifiable neurons and a total of approximately 5,000 synapses. C. elegans
has been in the focus of research for decades investigating its nervous system
connectome [1], anatomy and physiology of individual neurons including gene
expressions [2]. However, before having the ability of imaging the entire brain,
studies on the distributed dynamics of the neural circuits in C. elegans has not
been fully explored [3]. Moreover, a comprehensive machine learning approach
to build up an artificial nervous system together with researching its dynamics
is still an open topic to be investigated.
In the present study, we create a platform for precisely looking at the nervous
system of the C. elegans from a computer science point of view. We divide our
research into four subgroups as follows:
Mathematical Model of a Neuron – Dynamics of the membrane poten-
tial of a neuron Vm, is defined by the kinetics of the ionic conductance channels
together with sum of the input currents stimulating the neuron as follows:
dVm
dt =−
1
Cm
(ICa +IK+IS K +ILeak) + 1
CmX(Iinput),
where Cmis the membrane capacitance, ICa,IK,IS K and ILeak represent the
calcium current, potassium current, calcium-gated potassium channel current
and the leakage current, respectively. We design a novel conductance-based neu-
ron model, where dynamics of ion channels are precisely modeled and therefore
the model explicitly reproduce the behavior of a biological neuron [4].
Neural circuit implementation – Preliminary, we implement the Tap
Withdrawal (TW) neural circuit, which controls the reflexive motion of the worm
when a mechanical tap is applied to the petri dish in which it swims[5]. Recently,
in [6], we performed a novel probabilistic model checking approach in order
to estimate the unknown and known parameter-space within the TW circuit.
Accordingly, our target is to equip neuroscientists with novel state of the art AI
tools which simplify the process of decoding the brain of the worm.
Learning in C. elegans – Kinetics of ionic channels as well as the mecha-
nism of transmission of information among neurons through synapses are proved
to be the fundamentals of learning in the C. elegans [7, 8]. By utilizing our de-
tailed model, we aim to extract learning algorithms and principles existing within
the C. elegans nervous system.
Applications for neural circuits – We envision various applications for
our neural circuits such as autonomous driving. We utilize a simple neural circuit
imitating the TW behavior of C.elegans for an autonomous parking algorithm.
Eight neurons are used to steer a self-driving vehicle in a given parking spot.
Furthermore, we employ a simple artificial neural circuit to control a Pan-Tilt-
Zoom camera in order to keep track of a designated object. The circuit takes the
distance to the object as an input and keeps the object in the center. We aim
to extend the circuits to incorporate feedback information allowing it to adapt
its own synaptic connections similar to mechanisms used in equivalent biological
neural networks.
References
1. White, J.G., Southgate, E., Thomson, J.N., Brenner, S.: The structure of the ner-
vous system of the nematode caenorhabditis elegans. Philos Trans R Soc Lond B
Biol Sci 314(1165) (1986) 1–340
2. Altun, Z., Herndon, L., Wolkow, C., Crocker, C., Lints, R., Hall, D.e.: Wormatlas.
www.wormatlas.org (2016)
3. Kato, S., Kaplan, H.S., Schr¨odel, T., Skora, S., Lindsay, T.H., Yemini, E., Lockery,
S., Zimmer, M.: Global brain dynamics embed the motor command sequence of
caenorhabditis elegans. Cell 163(3) (2015) 656–669
4. Koch, C., Segev, I.: Methods in neuronal modeling: from ions to networks. MIT
press (1998)
5. Kandel, E.R., Schwartz, J.H.: Molecular biology of learning: modulation of trans-
mitter release. Science 218(4571) (1982) 433–443
6. Islam, M.A., Wang, Q., Hasani, R.M., Bl´un, O., Clarke, E.M., Grosu, R., Smolka,
S.A.: Probabilistic reachability analysis of tap withdrawal circuit in caenorhabditis
elegans. In: International High-Level Design Validation and Test Workshop, IEEE
(2016) 1–8 Accepted for publication.
7. Ardiel, E.L., Rankin, C.H.: An elegant mind: learning and memory in caenorhabditis
elegans. Learning & Memory 17(4) (2010) 191–201
8. Rankin, C.H., Wicks, S.R.: Mutations of the caenorhabditis elegansbrain-specific
inorganic phosphate transporter eat-4affect habituation of the tap–withdrawal re-
sponse without affecting the response itself. The Journal of Neuroscience 20(11)
(2000) 4337–4344
