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implementation, reliability, functionality and
scalability. They will be implemented by adding two
elements to each FPGA neuron module: i) a
synaptic/gap junction input field (pixel matrix for
wire connection, optical fibre plugs or light-
receptive pixels with light-to-charge conversion) and
an axonal output line with distribution elements that
communicate the neural response simultaneously to
individual synapses of one or many target neurons.
For the optical information transmission among
neurons, fast-switchable and intensity tuneable laser-
diodes are the emission light source(s) of choice.
They are triggered by the axonal output pin of an
FPGA neuron module. Their light is structured and
thus projected onto individual synaptic or gap
junction inputs of those target neurons that the active
neuron connects to. Any combination of reflective,
refractive, diffractive and masking elements such as
mirrors, micromirror arrays, microprism or
microlens arrays, gratings, colour filters and etched
shadow masks will be explored to ensure the correct
addressing and optical information transfer between
sending and receiving FPGA neurons. Synapses and
gap junctions of the receiving neurons will be
represented by arrays of photoelectric elements.
2.3 A Virtual Arena for Behavioural
Studies
Several C. elegans-specific descriptors of its
physiology, morphology and body mechanics exist,
including a realistic representation of the body (e.g.,
Virtual Worm Project, neuroConstruct) and aspects
of locomotion (Boyle, 2009; Bryden and Cohen,
2008; Gal Haspel, O'Donovan, and Hart, 2010;
Mailler, Avery, Graves, and Willy, 2010; Niebur and
Erdos, 1993; Stephens, Johnson-Kerner, Bialek, and
Ryu, 2010; Wakabayashi, 2006). Based on these
data, the Si elegans hardware nervous system of C.
elegans will be embodied through a biophysically
realistic virtual representation of the nematode in a
virtual environment. The virtual body will share the
shape, body-physics (e.g., elasticity, friction) and
cellular organization of C. elegans (including
realistic spatio-functional representations of sensory
cells). The interplay between active actuation
through sensory-driven control circuits of its
nervous system and passive actuation by
environmental factors (material-properties, arena
topology, gravity, air- or fluid-flow etc.) will be
considered. The simulation of this virtual body being
situated in a virtual arena will be running on
standard PC hardware. The virtual arena can be
freely configured to copy the 3D geometries and
biophysical features of an experimental environment
used in in vivo studies. It displays the native
behaviour of the C. elegans representation, provides
simulated environmental stimuli to its sensory
neurons and shows stimuli-induced responses (e.g.,
muscle actuation, secretory events). Information
flow is channelled in real-time through a bi-
directional interface between the computer and the
Si elegans nervous system. Sensory neurons of the Si
elegans nervous system receive their input from
programmable light sources. Si elegans’ motor
neuron activity actuates associated muscles of the
virtual body. Through a real-time closed-loop
feedback, any resulting sensory experience (e.g.,
change in posture, touch, change of chemical
concentration gradients) is coded and transmitted to
the Si elegans nervous system emulation as new
sensory input.
2.4 Neural Model Definition User
Interface and Network State
Analysis
The modelling space shall allow for the definition of
relevant neural processing parameters in a pictorial,
object-oriented flow diagram (graphical drag-and-
drop manner) or script. The desired network
structure can be created by simply selecting various
neuron, synapse or gap junction models from a
library of available components and connecting
them together. Alternatively, existing neural
response models can be imported from other
simulation engines (e.g., NEURON, BRIAN,
NeuroML, ...) (Figure 3).
An assembly can be stored as a neuron-specific
model to become a high-level, properly documented
building block for other researchers. The modelling
toolset provides all required design elements to
address and freely combine all known features and
events of neural signal transmission down to the
synaptic level, possibly including even abstracted
models of signalling cascades. These design
elements can be altered, thus personalized and
versioned by community members for experimental
purposes. E.g., the function of an individual synapse
may include cable properties of the pre-synaptic
axon and synapse to account for physiological and
morphological boundary conditions that shape/affect
signal properties such as signal transmission delays
and attenuation. We furthermore implement a
standard set of amplitude-invariant, self-terminating
action potentials with stereotyped waveforms as well
as graded regenerative potentials as the predominant
signal type in C. elegans with amplitudes and
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