Neuronunit optimization

1. Rigorous Neuron Model Optimization
using NeuronUnit
Russell Jarvis
PhD student Interdisciplinary neuroscience
Mentor Professor Sharon Crook Mathematics and Statistics
Laboratory for Informatics and Computation in Open Neuroscience
https://iconlab.asu.edu
Arizona State University

5. The Goal of My Current Research
● Make a general purpose optimizer that can fit abstract, general models to the
firing dynamics of specific biological neurons
• As a Proof of Concept:
• Fit Izhikivech model to a murine layer 5 neocortex pyramidal neuron
● Improve existing algorithms by making them faster and scalable.
http://neurolex.org/wiki/Category:Neocortex_pyramidal_cell_layer_5-6#tab=Basic

6. In silico Neural Models With Virtual
Electrophysiology.
1952

7. Fit Izhikevich Model to Layer 5 Pyramidal
Neuron
Description: http://neurolex.org/wiki/Category:Neocortex_pyramidal_cell_layer_5-6#tab=Detail
Firing patterns: (in response to normal physiological input) Regular firing and bursting
Spontaneous firing patterns: Regular firing and bursting
Spontaneous firing rate: medium firing

8. Can replicate a lot of complicated biological sceanarios with simpler eq
● In a digital representation of these equations values
for the parameters
● a,b,C,c,d,k,vr,v0,vt,vpeak were systematically
explored.
● The effects of changing these parameters were
explored by running dedicated simulations for some
but not all of their obtainable values.

9. MCMSC Mini-workshop 2011 Approaches for Model Reproducibility
Izhikevich, Dynamical Systems in
Neuroscience, 2007.

10. Real Layer 5 Pyramidal Recordings
Allen Brain Map
http://celltypes.brain-map.org/mouse/experiment/electrophysiology/354190013.

11. Meta Modeling
● Digital Neuronal Modelling has existed since the at most the early
80s (approaching 40 years).
● Digital models are no longer obscure
● Advances in computing, data digitization, model description,
machine readability and collaborative coding, has meant the
burden of scientific rigour against all digital models has rightfully
increased.
Hines, M. Efficient computation of branched nerve equations. International Journal of Bio-Medical Computing 15:69-76, 1984.
Journal of Mathematical Biology February 1986, Volume 23, Issue 2, pp 137–161 An analysis of a dendritic neuron model with an active membrane site

12. NeuronUnit
https://github.com/scidash/neuronunit
• Peripheral code includes virtual experiments and feature extraction.
• Differentiated from others by emphasising scientific rigor of models
• Facilitates testing and comparisons between model prediction and
experimental observations.
• This helped my research as
•Experimental observations are the best starting point for good
models
•This framework was leveraged to guide model fitting with great
effect.

13. Definition of Optimization
● Optimization is the process of searching for the combination of
parameters that will result in the best outcome according to a
given criterion.
● I used virtual electrophysiology experiments and returned
waveform features using neuronunit, combined with Allen Brain
Data to derive the most informative error functions.

14. Rheobase Current Injection
The rheobase (pA) value depended on behavior resulting from changing
the model parameters. In subsequent slides I will show how Vm (mV)
was then compared against specific criteria.

15. Sub Rheobase Current Injection

16. Supra Rheobase Current Injection

17. Rheobase

18. 12 spikes
4 spikes
Rheobase Search Distributed For Speed
116.66pA
Sub-firing
Sub firing CPU 0
CPU 1
CPU 2
CPU 3
CPU 4
CPU 6
CPU 5
Supra-firing
Supra-firing
Rheobase
Rheobase
50pA
66.66pA
83.33pA
166.66pA
0 spikes
150pA
0 spikes
133.33pA
0 spikes
0 spikes
0 spikes 0 spikes
2 spikes

19. Criteria Used to Evaluate Fitness

20. Criteria Used to Evaluate Fitness

21. Criteria Used to Evaluate Fitness

22. Criteria to Guide Search

24. Other Waveform Features

25. Conclusion
● In this talk I have described the concept and the usage of
insilico neuronal modelling.
● I have discussed tests contained inside NeuronUnit to fit
Izhikivech model to the layer 5 pyramidal neuron.
● I have discussed a way that I have speed up an existing
feature extraction algorithm
● And I have discussed optimization generally.

26. Acknowledgements: Sharon Crook, Rick Gerkin, Justas
Birgiolas, Reuben Haynes, Padraig Gleeson
NeuronUnit: a Social Product
Local History: Eugene M. Izhikevich
1996 2000 Visiting Professor of Department of Mathematics & Center for
Systems Sciences Arizona State University
1998-2000, Faculty Research Associate, Systems Science & Engineering Research Center
, Arizona State University.

27. NeuroML Editorial Board
Acknowledgements: Sharon Crook, Rick
Gerkin, Justas Birgiolas, Padraig Gleeson,
Reuben Haynes.

28. Future Directions.
Limitations:
How far away am I from general purpose optimization.
Porting the existing model onto NSG cluster.
Running parallel NeuronUnit as a web service using a cluster the NSG as a
backend, OSB as a front end.
Experiment with Substitution of Ipythonparallel for scoop as would facilitate
ipython notebooks.

29. 7 Error functions where used to guide the evolution of the GA. Each test is the
result of a virtual electrophysiology experiment.
Besides the rheobase test for rheobase current which has a simple scalar value, the rest of the neuronunit tests used the mean and standard
deviation measures in order to place model predictions inside a normalized error range.
RheobaseTest, candidate observation versus prediction
RheobaseTest value 130.0 pA,
InputResistanceTest mean 120672073.643411 ohm, std 77633160.8333564 ohm
TimeConstantTest mean 15.7342424242424 ms, std 7.31162636832495 ms
CapacitanceTest mean 1.50584166666667e-10 F, std 1.39683884626343e-10 F
RestingPotentialTest mean -68.2481434599156 mV std 6.53234788156637 mV
InjectedCurrentAPWidthTest mean 1.20769387755102 ms std 0.534345918375033 ms
InjectedCurrentAPAmplitudeTest mean 80.4351020408164 mV std 12.7488030357545 mV
InjectedCurrentAPThresholdTest mean -42.7357232704403 mV std 8.04073233409085 mV

30. What is Docker?
Not an actual VM but similarly providing containment and insulation of the HOST.
Recreating of complex build environments is automated in a script which actually .
Given the complicated git file merge history depicted previously, its very difficult to
keep developers on the same page.
If developers are not on the same page, bug replication is difficult.

31. Genetic Operations
Coordinates in parameter space, are represented as binary strings. New genes
are derived from very simple operations on these strings.

32. How Do we Optimize
Genetic Algorithm.
Step 1 randomly sample the parameter space.
Step 2 calculate error functions that results from random
sample.
Step 3 create offspring by recombining the random samples
that were best bc they lead to the least error values
Step 4 test the fitness of the offspring.
recombine the best offspring and add noise.

33. 10 constants: a,b,C,c,d,k,vr,v0,vt,vpeak.
Each constant is searched over 10 samples:
vr=[-75., -72.22222222, -69.44444444, -66.66666667, -63.88888889, -61.11111111, -58.33333333, -
55.55555556, -52.77777778, -50. ]
dim(a)*dim(b)*dim(C)*dim(c)*dim(d)*dim(k)*dim(vr)*dim(v0)*dim(vt)*dim(vpeak)
= 10^10
= 10 000 000 000
10billion samples.
Each sample corresponds to a 1-3 second simulation.

34. Parallel Implementation
map
Single processorMultiprocessor
flat topology
Multiprocessor
Tree topology

35. Why use Genetic Algorithms to guide
search.

36. Solution: Unit Testing
Test 1 Test 2 Test 3 Test 4 Overall
Model 1 0.1 -0.1 2.0 -0.3 0.6
Model 2 0.7 1.0 -0.1 -0.3 0.5
Model 3 0.3 0.3 -0.4 -0.3 0.3
Model 4 0.0 0.1 -0.1 -0.3 0.1
Model 5 -1.5 2.9 -2.4 -0.2 1.8
Overall 0.5 0.9 1.0 0.3
Model Test =
Expe
Obse
Model
Prediction

38. MCMSC Mini-workshop 2011 Approaches for Model Reproducibility
What properties of models will improve their
scientific impact across neuroscience?
Reproducibility: easy to rerun and validate simulation result reported in a
scientific paper.
Accessibility: available to theoretical and experimental neuroscientists in an
understandable format
Portability: cross-simulator validation and exchange of models and
components enabling reuse
Transparency: exposure of internal properties and automated validation

39. Whether its Warranted or Not Reduction
of Complexity

40. Layer 5

41. Progress of the GA genes from
conception to
convergences on a candidate.

42. Biological Preparation
How was this neuron synaptically disconnected?
Filtered, signal processing, spike sorted?