Download Mike Vella

Optimization of neuron models
using grid computing
Mike Vella
Department of Physiology, Development and
Neuroscience
Department of Physiology, Development and Neuroscience
Cortical pyramidal neurons
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Abundant in the cortex of virtually every
mammal
Found in structures associated with advanced
cognitive function
Department of Physiology, Development and Neuroscience
Display excitability, plasticity
Pyramidal neurons are very
complex
Highly-elaborate dendritic
morphology
Voltage-dependent ion
channels
Ligand-dependent ion
channels (receptors)
Various uniform and nonuniform spatial distributions of
all ion channels
Set of information to be
included in a model is large
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Department of Physiology, Development and Neuroscience
Single neuron multi-compartment
models
Department of Physiology, Development and Neuroscience
Why optimize?
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Single neuron models provide a basis for
understanding cell and local circuit function
Maximal conductances, compartment
capacitances , channel distributions – the large
number of parameters makes it difficult to
“hand-tune”
Provides a basis for understanding if ion
channel characterisation is sufficient
Department of Physiology, Development and Neuroscience
Genetic Algorithms
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Genetic algorithms
(GAs) optimize
solutions by
mimicking evolution,
this includes:
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Mutation
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Crossover
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Reproduction
ECSPY
Department of Physiology, Development and Neuroscience
Experimental and simulated data
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“Current clamp” measurements are used
Records membrane potential through injecting
fixed current into a cell through recording
electrode
Department of Physiology, Development and Neuroscience
Camgrid architecture
Department of Physiology, Development and Neuroscience
Work flow
Department of Physiology, Development and Neuroscience
Preparing code to run on CamGrid
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Python installed on most nodes, but I prepared
pre-compiled version, with the right python
version and all needed libraries (numpy, scipy,
sqlite etc..)
NEURON – prepared a pre-compiled “portable
NEURON”
Shell scripts
Department of Physiology, Development and Neuroscience
Data management: SQLite
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File-based, embeddable database system
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Makes handing large amounts of data clean
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Integrates well with Python, C/C++ etc..
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Makes life much easier
Department of Physiology, Development and Neuroscience
Communication with execute node:
Paramiko
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Paramiko – SSH2 Protocol for Python
Wrote a library for basic CamGrid tasks
(contact me if you want this,
[email protected])
Department of Physiology, Development and Neuroscience
My CamGrid experience
pros:

Speedup ~ (number of execute nodes) / 2
=>300 day optimization takes ~ 1 week => 1
million simulations possible
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Great support
cons:
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Takes time to learn
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Pre-compilation of code can be tricky
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Problem diagnosis harder
Department of Physiology, Development and Neuroscience
Results
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Initial results show optimizer finds correct parameter set when tested against
known solution
Department of Physiology, Development and Neuroscience
Results
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Becomes less accurate as simulation evolves
Good fit can have similar features – not necessarily identical voltage
trace
Department of Physiology, Development and Neuroscience
Conclusions
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Neuron optimization complements experiments
for building accurate models of single cells
Computationally intensive task suited to
CamGrid
Preparation of software to run on CamGrid can
be difficult, but may be very worthwhile.
Department of Physiology, Development and Neuroscience