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<!DOCTYPE html>
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<meta name="keywords" content="Neurokernel, Drosophila, brain emulation, computational neuroscience" />
<meta name="description" content="About Neurokernel" />
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<title>About Neurokernel</title>
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<h1>About Neurokernel</h1>
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<h2>Motivation</h2>
<p>Reverse-engineering how biological neural circuits process
information is key to understanding human brain function, developing
effective therapies for psychiatric and neurological disease, and
designing powerful new computer architectures that can replicate the
formidable information processing capabilities of biological brains. To
achieve this goal, we must focus on a model organism capable of complex
behavioral and information processing tasks whose brain structure is
within reach of current computational technology and whose neural
activity can be experimentally accessed.</p>
<h2>An Unprecedented Opportunity</h2>
<p>The fruit fly <i>Drosophila melanogaster</i> is capable of a range of
complex nonreactive behaviors driven by multiple sensory
modalities. Remarkably, these behaviors are governed by a brain
comprising only ~ 10<sup>5</sup> neurons! An extensive toolbox of
powerful genetic manipulation techniques developed through years of
fruit fly research enables the visualization of the fly's neural
circuitry and analysis of how its structure relates to the fly's
behavior. This toolbox has enabled researchers to characterize the fly's
sensory mechanisms and shed light on the structure of its brain
connectivity map, or connectome.
<a href="http://www.cell.com/current-biology/abstract/S0960-9822%2810%2901522-8">Recent work</a> has revealed that the fly's
connectome comprises 40 unique modular subdivisions called
<i>local processing units</i> (LPUs). Almost
all of these modules have been found to correspond to anatomical regions
of the fly brain associated with specific functional subsystems such as
sensation and locomotion. LPUs can therefore be regarded as
functional building blocks of the fly brain.</p>
<p>The computational tractability and genetic accessibility of the fly
brain have acquired increased importance in light of ongoing advances in
parallel computing and electrophysiological techniques. Graphics
Processing Units (GPUs) are a powerful and affordable commodity parallel
computing technology whose steady improvement promises the computational
power needed to test increasingly realistic neural circuit models of the
scale of the fly brain. While the fly's tiny size has made the recording
of neural activity from its brain challenging, innovative technologies
developed at the <a href="http://www.bionet.ee.columbia.edu">Bionet Group</a>
and elsewhere have made it possible to contemplate the obtaining of
simultaneous recording from multiple neurons in the fly
brain. Neuroscientists therefore have an unprecedented opportunity to
leverage these developments to build powerful new models of the fly
brain that are informed by hitherto inaccessible knowledge regarding its
structure and function.</p>
<h2>An Open Scalable Platform for Fruit Fly Brain Emulation</h2>
<p>The Neurokernel project aims to build an open software platform that
can scalably leverage the power of multiple GPUs to emulate the entire
fly brain. A key feature of the platform is its support for integrating
models of LPUs into models of entire functional subsystems. Neurokernel
defines a mandatory module interface for the communication of neuron
state data between LPU model implementations. Connectivity patterns
between any two LPUs are expressed independently both of other inter-LPU
connectivity patterns and the internal connectivity of the LPUs
themselves. Enforced LPU interoperability and the partitioning of the
fly connectome into connections responsible for distributing data
between processing modules and connections that are part of those
modules enables individual LPU models or connectivity patterns to be
improved or replaced without modification of other parts of a brain
emulation. This design facilitates the collaborative development of
whole-brain emulations by enabling the immediate interconnection of
independently developed LPU models with compatible
interfaces. Neurokernel's support for LPU model integration also enables
exploration of brain functions that cannot be exclusively attributed to
individual LPUs or brain subsystems.</p>
<p>A major goal of Neurokernel is to enable fly brain models to be
scaled up in computational complexity without necessitating their
reimplementation. To this end, Neurokernel will provide APIs that expose
the components required to implement and interconnect LPU models while
abstracting away the underlying GPU-dependent code that implements them;
this will also enable Neurokernel-based emulations to benefit from
future improvements in GPU technology. Neurokernel will also provide a
GPU resource management infrastructure that will enable brain emulations
to take advantage of multiple GPUs depending upon their availability.</p>
<h2>Help Build a Fly Brain!</h2>
<p>Building an accurate emulation of the fly brain is an
interdisciplinary effort that requires data, algorithms, and insight
from (but not limited to) the fields of neuroscience, computer science,
and systems engineering. Researchers interested in working towards this
goal are invited to join the Neurokernel project. The platform software
is developed in Python (a powerful, high-level language that enjoys wide
popularity in the computational neuroscience community) and CUDA
(NVIDIA's C language extension for GPU programming). All design
specifications, documentation, and code are <a href="http://opensource.org/licenses/BSD-3-Clause">open-source</a>
and <a href="http://github.com/neurokernel/neurokernel">publicly hosted</a> on GitHub.</p>
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