Reproducible Neural Data Analysis with Elephant, Neo and odml
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1 Reproducible Neural Data Analysis with Elephant, Neo and odml Michael Denker Institute of Neuroscience and Medicine (INM-6) & Institute for Advanced Simulation (IAS-6) Jülich Research Center, Jülich, Germany Elephant Tutorial at HBP CodeJam 8 Lausanne, Switzerland Sept 15, 2017
2 Structured, reproducible, collaborative workflow using open software tools Denker, M., and Grün, S. (2016). Designing Workflows for the Reproducible Analysis of Electrophysiological Data. In Brain-Inspired Computing, K. Amunts, L. Grandinetti, T. Lippert, and N. Petkov, eds. (Cham: Springer International Publishing), pp Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
3 Reproducible workflows and open software tools Key element for reproducibility & validation Platform for communitydriven development metadata Comparison of experimental and simulated data Ease of use for exploration and visualization data Facilitates access to parallelization for data analysis data analysis Simplification of collaborations Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
4 Behavioral metadata Complex, natural behavior (e.g. Reach-to-grasp task) Involves long training Complicated cue presentations Registration of events (e.g. reaction time) Control of behavior Riehle et al (2013) Front Neural Circuits Measurement of behavior Many parallel recording channels Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
5 Experimental setup Reach-to-grasp study: 120 trials / recording ~ 5 recordings / day ~ 70 days / monkey 3 monkeys Actual neural data in only two files! Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
6 odml: open metadata markup language for electrophysiological experiments Grewe, Wachtler, Benda (2011) Front. Neuroinf. Zehl, Jaillet, Stoewer, Grewe, Sobolev Wachtler, Brochier, Riehle, Denker, Grün (2016) Front Neuroinf. Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
7 Why standardized metadata becomes important (benefits!?) Manual screening Filtering of data sets Overview of data sets Post-recording enrichment Formalization of language Zehl, Jaillet, Stoewer, Grewe, Sobolev Wachtler, Brochier, Riehle, Denker, Grün (2016) Front Neuroinf. Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
8 Metadata agglomeration Zehl, Jaillet, Stoewer, Grewe, Sobolev Wachtler, Brochier, Riehle, Denker, Grün (2016) Front Neuroinf. Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
9 Reproducible workflows and open software tools Key element for reproducibility & validation Platform for communitydriven development metadata Comparison of experimental and simulated data Ease of use for exploration and visualization data Facilitates access to parallelization for data analysis data analysis Simplification of collaborations Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
10 Neo common, vendor-independent representation of data Tools can employs Neo for common internal data representation load data from different (proprietary) formats into Neo data object model Key concept: not a common file format, but I/O bridge to common object model Semantics delivered by annotations Garcia,, Davison (2014) Front Neuroinform Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
11 Neo connecting diverse tools Use of Neo as a common data model and API to connect diverse software tools gin Elephant NIX Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
12 Combining data and metadata Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
13 Hands-on: Working with an annotated Neo structure Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
14 NIX: data and metadata integration general data model (derived from Neo) to represent recorded data, derived data, relations of data full metadata integration (odml) enables selection of data by metadata enables storing all necessary information to create a scientifically correct plot, including labels, units HDF5 file format, structure reflects data model, easy to understand libraries for different languages (C++, Python, Matlab, Java) New: also native Python library and Neo IO Stoewer et al. (in prep.) Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
15 NWB: generic neuroscience data format Teeters, J.L., Godfrey, K., Young, R., Dang, C., Friedsam, C., Wark, B., Asari, H., Peron, S., Li, N., Peyrache, A., et al. (2015). Neurodata Without Borders: Creating a Common Data Format for Neurophysiology. Neuron 88, Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
16 Massively Parallel Spike Trains Collaboration with A. Riehle and T. Brochier, INT, CNRS-AMU, Marseille Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
17 Overview reproducible data analysis using Elephant Aims: provide generic tools to analyse brain dynamics from experiments and simulations large neuron populations (massively parallel spike trains, local field potentials) relationship of such multi-scale data create toolbox for hosting a broad range of methods based on the data models provided by the Neo library modular design of analysis functions community-centered, open-source, curated github.com/neuralensemble/elephant elephant.readthedocs.org/en/latest Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
18 Contributions Implemented contributions Spike detection from intracellular data Spike train metrics Spike / time series data correlations Higher order correlation (HOC) Unitary Events Synfire detection (ASSET) CuBIC Adaptive instantaneous rate Kernels Surrogate data generation Stochastic spike train generation Current source density Inverse CSD Kernel CSD Upcoming contributions Multitaper analysis LFP / LFP-spike (phase) analysis Spatio-temporal FCA Marked state analysis Rate vector analysis (GPFA) Pattern Search (Russo et al) Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
19 Sources of Documentation Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
20 Elephant in practice: time histogram Simple analysis functions to facilitate modular design Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
21 Elephant in practice: correlations Standard analysis functions to compare analysis results across datasets Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
![al, [Re]science (2017) Advanced analysis](/docs-images/76/73504198/images/22-4.jpg "functions to promote their use in data analysis")
22 Elephant in practice: replication Replication of: Riehle et al (1997) Science In: Rostami et al, [Re]science (2017) Advanced analysis functions to promote their use in data analysis and enable the replication of complex analysis protocols Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
23 An exercise in reproducing a study Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
24 Hands-on: Performing an Elephant analysis Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
25 SPADE: Data Mining by Frequent Itemset Mining raw data surrogate data significant patterns Efficient counting of patterns Histogram of patterns of same size and number of occurrences * Torre et al (2013) Front Comput Neurosci July 15, 2017 Michael Denker - CNS Tutorial 2017
26 Estimation of Significance of Synchronous Spike Patterns raw data surrogate data significant patterns Extraction of significant spike synchrony patterns by comparison to (independent) surrogate data * Torre et al (2013) Front Comput Neurosci July 15, 2017 Michael Denker - CNS Tutorial 2017
27 Estimation of Significance of Synchronous Spike Patterns raw data surrogate data significant patterns Extraction of significant spike synchrony patterns by comparison to (independent) surrogate data * Torre et al (2013) Front Comput Neurosci July 15, 2017 Michael Denker - CNS Tutorial 2017
28 Estimation of Significance of Synchronous Spike Patterns raw data surrogate data significant patterns Extraction of significant spike synchrony patterns by comparison to (independent) surrogate data * Torre et al (2013) Front Comput Neurosci July 15, 2017 Michael Denker - CNS Tutorial 2017
29 Estimation of Significance of Synchronous Spike Patterns raw data surrogate data significant patterns * Torre et al (2013) Front Comput Neurosci July 15, 2017 Michael Denker - CNS Tutorial 2017
30 Pattern Set Reduction FPs due to chance pattern of correlated pattern and background activities Additional, conditional filtering to avoid FPs with background * Torre et al (2013) Front Comput Neurosci July 15, 2017 Michael Denker - CNS Tutorial 2017
31 Hands-on: Performing an Elephant analysis Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
32 Interactive Loops validate neuronal simulations using experimental data Work in Interactive Loops unlocks the potential of reproducible workflows built on a common software infrastructure Experimental Structural Data Model Building Theory Experimental Functional Data Simulation etc Data Analysis Validation enables to validate simulations across on the level scales of network of measurement dynamics Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
33 Structured, reproducible, collaborative workflow using open software tools Denker, M., and Grün, S. (2016). Designing Workflows for the Reproducible Analysis of Electrophysiological Data. In Brain-Inspired Computing, K. Amunts, L. Grandinetti, T. Lippert, and N. Petkov, eds. (Cham: Springer International Publishing), pp Sept 15, 2017 Michael Denker - Elephant Tutorial 2017
34 Sept 15, 2017 Michael Denker - Elephant Tutorial 2017 Workshops for Masters/PhD cand. Analysis of spiketrains using Elephant & neo ANDA 2017: 2 weeks with 25 participants Deadline extended: YRE 2017: online tutorial and exercises in HBP collab #5183l
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