Computational Neuroscience Working Group

Leads:
Ted Berger (University of Southern California): (berger@usc.edu)
Bill Lytton (SUNY, Downstate): (bill.lytton@downstate.edu)
Raj Vadigepalli, (Thomas Jefferson University): (rajanikanth.vadigepalli@jefferson.edu)
 

Goals and Objectives:

The key issue of neuroscience is to understand how different cellular, network and systems neural mechanisms are integrated across multiple levels of organization to produce motor behavior and adapt this behavior to various external and internal conditions. Therefore, multiscale modeling is a tool of critical importance for neuroscience. Quantitative multi-scale models can help us to understand the complex brain mechanisms and processes as well as to assist in generating experimentally testable hypotheses and in selecting informative experiments. The development realistic models of different brain function is a major challenge in the field.

This working group focuses on multiple scale analysis and simulation of neural systems and processes with a special emphasis on the cross-level integration of the intrinsic cellular, network, and systems-level brain mechanisms. Our first goal is to accumulate the available up-to-date information on the modeling tools, novel concepts, and major relevant review papers in the field. Our second goal is to promote new collaborations between experimental neuroscientists and mathematicians and modelers involved in the multiscale modeling of brain mechanisms and processes.

Working Group Team Publication: Multiscale modeling in the clinic: diseases of the brain and nervous system

MSM 2017 (10th Anniversary) meeting Discussion Items

appropriate levels of details

difficulties of credibility in an area where "outputs" may be cognitive or otherwise hard to get a handle on

use of common tools vs use of generic programs

 

Mar 22, 2017 1320 MSM CN WG discussion

model sharing -- model genetic organism ; sharing vs publishing
policy and sharing statements -- can balance the two; hold on to things till get the IP or get the submissions and get sufficient yields
NIH prefers to see sharing but is not blocking monetization so understands IP/patents
sharing helps with finding sign errors and 6.3deg
cf cancer moonshot - Biden said shouldn't be stovepiping their data -- NEJM: "data vampires"
people don't want to have other people find their errors;
use of common tools vs use of generic programs -- difficulty archiving very specific programs written on a currently 'standard environment'
SBML community has evolved the standards -- standards have to evolve but also need backcompatability
standards or widely used platforms

MSM modeling -- hard problems of having levels talking to one another
phenomenological vs mechanistic
going between memory systems -- plug hippocampal model into a larger model
MUSIC -- stick together module; MPI-based framework originally designed for spike exchange
increasing levels of complexity - need to speak the same language
idally people should speak the same language -- gene people will not necessarily speak to fMRI people -- how about some
translation to make it adaptable to another framework
getting a common language -- 2 diff languages: as people who are modeling constructing models and exchanging;
what do the experimentalists record and what do they want
different professional models -- physicists vs engineering

molecular pathology vs anatomical pathology -- what are the best markers to segregate patients for treatment
elaborate agent-based bioinformatics (Steven Piper)
appropriate levels of details

uses in the clinic
DBS -- how to place the electrodes
epilepsy -- the personalized med of epilepsy surgery;

credibility of neural models
drug companies can't replicate experiments
reproducibility and rigor
making 100s of models that captures different variance
complex system
difficulties of credibility in an area where "outputs" may be cognitive or otherwise hard to get a handle on
authentication of bio-agents
make models that are sufficiently complex -- either go thru them with a fine-tooth comb vs reproducing
how much reuse is going on -- always easier to reuse than to rebuild

the brain is special organ;
resistance to 'medicalization' of the brain from those involved in consciousness/cognition studies
what do neurolinguists do? -- why don't we do some of that
brain functions in terms of circuits -- this brain area talks to this area talks to this area
you never get a reliable functionality -- everything is stochastic (or hidden components)
saccade is very reliable but none of the cells is reliable -- reduce to the components
how to make models that give you that fundamental infrastructure
connectome idea may be misguided -- there is no standard brain
modularity; cf fMRI studies - by the 6th time task is presented don't see any response

standards
SBML, cellML, neuroML, sedML, NSDF (neuroscience data format), Allen Brain has a standard (Neo, NineML), wavesurfer,
neurodata without borders; spike sorting software is still needed -- been decades


COMMENTS ON 2017 meeting

IMAG working group Computational Neuroscience 03-22-17

  • Presentation of the table
  • Introduction of the participants for the working group

o   Circadian rhythms

o   Hippocampal network

o   Calcium dynamics

o   Modeling in the dendrites

o   Motor control-motor cortex.

  • Model sharing

o   NIH funding encourage model sharing

o   Publishing should not be dependent of sharing the models

o   Replication is a problem

o   There are errors when sharing

o   Robustness of the models depend on sharing data

o   Funding agencies encourage sharing data because they would like to see secondary analysis

o   What to share?

o   Typos on the model, it is important to give feedback to modelers

o   Sometimes those errors can lead to mislead conclusions

o   This can credibility problems

o   Sharing can accelerate progress of the field

o   Researchers tend to not share data

o   There needs to reliability on research

o   Models can be complex, better to share and reproduce than start from zero

o   Reliability and reproducibility

  • Should scientists be encouraged to use standards tools?

o   Credibility also depends of the use of standards tools

o   Problem: tools can work in certain environments but can be fragile in others or in future changes in the environments

  • Levels on complexity- multicompartment/multiscale of simplified models?

o   No stochastics models but reorganization of the system

o   Models cannot be reliable at the cellular levels (reproducible?)

o   There is no fix system- failure of the connectome

  • From cellular level to network model

o   Multiscale modeling, lower levels affect upper levels

  • Increasing level of complexity

o   Need to use the same language

o   Work should be adaptable to other levels of complexity

o   Personalized medicine example- complexity adaptable and applicable to the individual level

o   Standardized the model and standard data file storage

o   BRAIN initiative has standard for the electrophysiology

o   There is a need to standardize the modelers from the NIH

o   Example: neurodata without borders (http://www.nwb.org)

o   Problem: proliferation of formats.

 

 

2015 Meeting Breakout:

Accomplishments:

  • White paper for US BRAIN Initiative 
  • Set of white papers coming out on MSM applications to clinic  (Annals of BME, Tom Yankeelov)
  • Workshop on MSM organized by Bill Lytton at Computational Neuroscience (CNS'15) meeting
  • BRAIN Initiative Session organized by Raj Vadigepalli at American Institute of Chemical Engineers Annual Meeting http://www.aiche.org/conferences/aiche-annual-meeting/2015/events/unders...

 

Possible Meeting and Paper Plans:

  • Apply for an SFN Symposium on MSM; NB Cell/Neuron organizes satellite symposium
  • Consider a cosine workshop focused on implications of modeling for behavior -- drug abuse issues and/or schizophrenia
  • Set of white papers coming out on MSM applications to clinic  (Annals of BME, Tom Yankeelov)
  • Statistical and applied maths Institute (NSF MBI) organized by Mark Kramer and others - virtual working group how statistics and mathematics can be applied to multiscale Neuroscience data
  • Expand Current BRAIN white paper - use as starting point to expand and deepen ; populate with examples, papers, ways of attacking

 

Challenges:

  • Stochastic modeling and non-stationarity (hypothesis: get one shot at the data; most neurons not related to stimulus investigated)
  • Social challenge - leadership of the field focused largely on the network with little focus on biological details down to cellular or molecular scale; would like to refocus on biological issues instead of current simplifying paradigms; reconceptualize/redefine what we do to fit 
  • Need to define what we are, what makes us different; what do we offer that defines what this group is?
    • Compared to traditional neuroscience: we should provide solutions for a category of problems in Neuroscience (multiscale)
    • Compared to rest of MSM we have to deal with information representations, cognitive and memory processes, behavior (cf control theory, information theory)
    • Need tools to share multiscale knowledge with different communities/audiences (eg. psychologists) 
    • Rather than tools, we can provide specific questions and then have tools for each question (matlab package, math eqns,..)
    • Define language, principles, methodology, approximations, empirical rules - develop standardized methods for each problem
    • Ebola example integrates data and model: cyber-infrastructure that is queryable - produces feasible answer
    • Gigantic models (cf HBP in EU) vs specific model forms focused on specific questions -- middleware to support this
    • Importance of archiving data that could be be accessed in the context of genomes -- cf proteome, genome data related to neural dynamics or to behavior
    • Linking modelers and experimentalists - how to explain what we do?; we claim we can build bridges between molecular, cellular, network -- but need clear examples of how this has been done; eg. explain behavior at different scales/levels
    • Include tools for HPC multiscale simulations (similar to NSG portal)
    • MSM is about making non-trivial prediction in a different area of investigation
  • How to get people to collaborate from different fields?
    • Models/data that can make predictions for different level (eg. biology to psychology)
    • Explain same question at different levels; hypothesis sharing, scientific affordances, can lead to collaborations 
    • Can help decide what questions to work on
    • Difficult to get working group to work same problem (eg. on addiction) with bottom up participation and no funding
  • Centralization vs distributed/independent research (eg. Human Brain Project, NEURON)

Additional 2016 Plans:

Share computer models of primary motor cortex (M1) neurons (pyramidal, interneuron) at varying levels of complexity (point-neuron, multicompartmental with full dendritic geometry) on ModelDB, and the open-source brain (OSB) websites.

Share hybrid network models of M1 microcircuit on ModelDB and OSB. These hybrid models will consist of point-neurons interconnected with detailed multicompartmental models.

Share custom Python/NEURON scripts for fitting single neuron models on ModelDB and OSB. Scripts will use genetic algorithms and the PRAXIS algorithm, and will include multiple custom-designed fitness functions and cell models, along with somatic whole-cell recorded data from M1.

Share microglial cytokine regulatory network model (Vadigepalli).

Additional 2015 accomplishments:

Shared primary motor cortex (M1) microcircuit data (Top-down laminar organization of the excitatory network in motor cortex, Weiler et al, Nature Neurosci. 2008) on the new NeuroscidataDB Senselab database, that was designed in collaboration between experimentalists and modelers (https://senselab.med.yale.edu/_siteNET/eavData.aspx?db=18&cl=154&o=147217). The model was also previously made available here: https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=114655 .

Devised new formats for multiscale model specifications that range from subcellular synaptic to cellular to network levels. This new format was designed for later incorporation into the NeuroML standard together with our UCL collaborators (Padraig Gleeson).

Translation of the Weiler 2008 dataset into the OSB format, and uploaded to the OSB site (URL: http://www.opensourcebrain.org/projects/weileretal08-laminarcortex)

Translation of the Izhikevich point-neuron model (of cortical neurons) to the OSB format and shared on the OSB website (URL: http://www.opensourcebrain.org/projects/izhikevichmodel).

Sharing of a model that integrates Angiotensin II signaling and electrophysiology in brainstem neurons (Makadia et al., Biophys J 2015 http://dx.doi.org/10.1016/j.bpj.2014.11.1851). Shared on Senselab ModelDB. URL: http://senselab.med.yale.edu/modelDB/ShowModel.cshtml?model=156830

Organized (Bill Lytton, Wim van Drongelen) workshop on multiscale modeling (Beyond the canon: temporal and spatial multiscale organization in cortex) at the annual organization for computational neurosciences meeting (7/23/15; Prague, CZ).

Introduction:

Details of neuroanatomy and neurophysiology are critical to understanding the brain: function follows forms through multiple scales from molecular up through local connections (microconnectomics), to large-scale brain connectomics.  Properties emerge at each scale.  Simulations and high-level modeling are both used to detect and understand these emergences.

Multiscale Computational Modeling for the US BRAIN initiative  White Paper:  PDF iconComputationalmodelingforUSBRAINinitiative.pdf

How will we understand the brain?  To start, we must observe the brain's dynamic activity, which consists of rich patterns propagating across the brain’s spatial and temporal scales, within the context of the brain's complex anatomy.  A fundamental component of the BRAIN Initiative is the development of new technologies. Here we present suggestions for new technologies in simulation and mathematical modeling – spanning spatial scales from molecules to large neuronal populations – leveraging the strengths of the Multiscale Modeling (MSM) community.

Past Presentations: 

2017:

Sep 12-13, 2017 Bernstein conference: Workshop on Multiscale modeling and simulation

Goettingen Germany

Organizers: Dura-Bernal, Lytton

13:00    Bill Lytton, SUNY Downstate, USA Opening remarks13:40    Jean Pierre Changeux, Pasteur Institute, France Climbing brain levels of organisation from genes to consciousness14:20    Volker Steuber, University of Hertfordshire, UK Multi-scale models of synaptic plasticity and information processing in single neurons and networks15:00    Coffee Break15:30    Robert McDougal, Yale University, USA Multiscale modeling with NEURON Reaction­Diffusion Module (NRxD)16:10    Salvador Dura­-Bernal, SUNY Downstate, USA NetPyNE tool and multiscale model of mouse M1 microcircuits16:50    Cliff Kerr, University of Sydney, Australia The forest and the trees: how the dynamical environment influences small­scale network computations17:30    General discussionWed, Sept 13, 20179:00    Szabolcs Kali, Hungary Tools and workflows for reproducible, collaborative computational neuroscience: modeling hippocampal neurons and network in the Human Brain Project9:50    Marianne Bezaire, BU Modeling conductance­specific and cell type­specific contributions to functional activity in hippocampal microcircuits and networks10:30    Coffee break11:00    Daniel Durstewitz, ZI Mannheim, Germany Merging computational modeling with statistics: Towards quantitative, highly data­driven, predictive models of neural systems11:40    Kael Dai, Allen Institute for Brain Science Software Development Kit for Multiscale Modeling of Large-Scale Brain Circuits12:20    Organizers: Closing remarks on the workshop 

 2014 Meeting:Organized BRAIN Theme Discussion of modeling/simulation opportunities associated with the Presedential BRAIN (Brain Research through Advancing Innovative Neurotechnologies) initiative

 

Activities in 2012-2013

Workshop: Multi-scale Modeling in Computational Neuroscience at University of Lübeck, Germany April 30 - May 5, 2012 Organizer: James M. Bower

Workshop: Multi-Scale Modeling in Computational Neuroscience II: Challenges and Opportunities at CNS*2012, Atlanta/Decatur, July 25, 2012. Organizers: James M. Bower and Ilya A. Rybak

IMAG/MSM Webinar: Modeling Neurons as Model Building Devices for Systems Medicine October 10, 2012. Presenter: James S. Schwaber

IMAG/MSM Webinar: Multiscale Modeling of Neural Control of Breathing April 30, 2012. Presenter: Yaroslav I. Molkov

IMAG/MSM (2013) Meeting: Session: The BRAIN Initiative and its links to MSM Keynote by Terry Sejnowski, Advisory Board to the NIH Director October 2, 2013. Organizers: Bill Lytton, Raj Vadigepalli

IMAG/MSM Webinar: Multi-scale seizure dynamics December, 2013 (to be scheduled) Presenter: Mark Kramer

Discussion of plans and activities in 2014

(1) WG re-organization, lead rotation

(2) Integration with the BRAIN Initiative

(3) Several IMAG/MSM webinars on multi-scale modeling in neuroscience

(4) Workshop: Multi-Scale Modeling in Computational Neuroscience at CNS*2014, July 13-18, Quebec, Canada

(5) Funding issues

News

  • Thursday, December 5, 2013 at 3pm EDT: Multi-scale seizure dynamics

Mark Kramer

Abstract: Epilepsy - the condition of recurrent, unprovoked seizures - is a common brain disease, affecting 1% of the world’s population. Seizures are typically identified as abnormal patterns in brain voltage activity. Many open questions surround epilepsy and seizures, and identifying the associated answers promises new insights for treatment and prevention. In this talk, we will consider brain voltage activity during seizure as observed at multiple spatial scales. We will show how techniques from mathematics and statistics can be used to characterize these data, identify common features, and connect observed brain activity to mechanisms. We will first examine how brain electrical activity couples and decouples during the the course of a seizure. We will then focus on a specific, open question in epileptology: why do seizures spontaneously terminate? Through analysis of human brain electrical activity at various spatial scales, we will propose that seizures self-terminate via a common dynamical mechanisms: a discontinuous critical transition or bifurcation. In contrast, prolonged seizures (status epilepticus) repeatedly approach, but do not cross, the critical transition. To support these results, we will also consider a computational model to demonstrate that alternative stable attractors, representing the seizure and post-seizure states, emulate the observed brain dynamics. These results suggest that self-terminating seizures end through a common dynamical mechanism. This description constrains the specific biophysical mechanisms underlying seizure termination, suggests a dynamical understanding of status epilepticus, and demonstrates an accessible system for studying critical transitions in nature.

Room Link - please use this link to interact with the speaker

Webcast Link (for viewing only) - please use this link only if you are not able to get the Room Link to work (you will not be able to see or hear anything until the recording begins)

  • Tuesday April 30, 2013 at 2-3pm ET: MSM Webinar of Computational Neuroscience WG:

Multiscale Modeling of Neural Control of Breathing

Dr. Yaroslav Molkov, Math Dept., Indiana and Purdue Universities

Abstract: Neural circuits involved in generation of the respiratory rhythm and control of breathing is one of the most studied systems in the mammalian brain. Yet the more experimental data become available the more heated become the debates concerning the neural mechanisms involved. The main reason of the controversy is that particular oscillatory regimes observed result from interactions of mechanisms and processes operating on significantly different temporal and spatial scales. In this talk, I will address a number of experimental observations leading to seemingly contradicting hypotheses about mechanisms of respiratory rhythm generation, and analyze them from the single theoretical perspective which includes a spectrum of time and spatial scales from the sub-cellular and cellular levels (voltage-dependent ionic channels, ionic concentrations, pumps, etc.) to the network and system levels of operation. I will demonstrate the conditions in which interventions on smaller scales may or may not lead to significant perturbations on the higher hierarchical level and/or on longer time scales.

Webinar Live Video Link: https://Demo.vidyo.com/flex.html?roomdirect.html&key=QWH88rENFALv

  • Monday December 17, 2012

Requests for Applications:

International Traumatic Brain Injury Research Initiative. NIH Cooperative Program for Comparative Effectiveness of Clinical Tools and Therapies (U01)

(RFA-NS-13-008)

National Institute of Neurological Disorders and Stroke

National Institute of Biomedical Imaging and Bioengineering

National Institute on Deafness and Other Communication Disorders

 

Application Receipt Date(s): April 01, 2013

 

Application topics include:

Development of predictive multiscale modeling approaches to elucidate the mechanisms of traumatic brain injury and simulate potential treatment outcomes.

 

  • Tuesday, October 22; Meeting of Computational Neuroscience WG during 2012 MSM Consortium Meeting

Participants: Ilya A. Rybak (Drexel Univ., lead), Carl Berdahl (American Airlines); Thomas Dick (CWRU); Mounya Elhilali (Johns Hopkins Univ.); William W. Lytton (SUNY, Downstate); Grace C. Y. Peng (NIBIB/NIH); James S. Schwaber and Rajanikanth Vadigepalli (Thomas Jefferson Univ.); Susan Volman (NIDA/NIH) .

Discussion topics:

• Perspectives in multiscale modeling in neuroscience (Rybak, Dick, Elhilali, Litton, Schwaber, Vadigepalli, Volman). Suggested important directions to focus in the future: (1) Modeling of systems biology with computational neuroscience (Schwaber, Vadigepalli) (2) Neural code involving cell selection not population firing rate (Litton) (3) Neuron-glia interactions (Dick)

• MSM funding for neuroscience (Peng, Volman). We hope that NINDS and NIMH will return to participation in the MSM Consortium. Otherwise the potential future support of neuroscience-related proposals in the framework of MSM Program would be problematic.

• WG reorganization. We decided to have in this WG two additional leads in the group: Carl Berdahl (American Airlines) and Rajanikanth Vadigepalli (Thomas Jefferson Univ.).

 

  • Wednesday October 10, 2012 at 3:00pm ET

Modeling neurons as model building devices for systems medicine.--Jim Schwaber

Extended ABSTRACT

What is the function of the extensive variability observed in individual members of a homogenous cell phenotype? This question is particularly relevant to the highly differentiated organization of the brain. High throughput gene expression data from several hundred single neurons from a brainstem homeostatic regulatory nucleus were collected and analyzed in terms of synaptic input type. The results, defined by inputs, showed that the variability in the data contains a surprising structure as a continuum of neuronal gene expression sub-phenotypes. This structured variability is manifest as a gradient of co-regulated gene expression modules at baseline and following hypertensive challenge. This observation indicates that analog tuning of underlying regulatory networks by inputs could shape a state-dependent response of autonomic control networks to physiological challenges. These principles of input-structured phenotype may extend to other neuronal systems where inconsistent, variable responses are common, and where the role of large populations of neurons is uncertain. Analyses of this type could also be extended outside the brain to other environments where cells clearly vary and receive different inputs. Finally, reduction of variability of the transcriptional identity and response of these cells may make identification of specific gene network topologies possible. This may illuminate a molecular physiology interacting with the biophysics, synaptic organization, and connectivity of many different kinds of neurons.

Adobe Connect: https://webmeeting.nih.gov/r95899642/

Call-in: 866-546-3377, Passcode: 474962

 

Three workshops on Multi-Scale Modeling in Computational Neuroscience in 2012

(1) Workshop “Multi-scale Modeling in Computational Neuroscience” to be held at the University of Lübeck, Lübeck, Germany April 30 - May 5, 2012

Organized by James M. Bower University of Texas Health Science Center, San Antonio, TX.

https://www.gradschool.uni-luebeck.de/index.php?id=366

Through simulation projects, participants will have the opportunity to create realistic neural models from sub-cellular to network levels. This will provide an excellent opportunity for those with previous experience in neural simulation to learn new techniques and strategies for multi-scale modeling. Although participants can use the simulator of their choosing, this workshop will also introduce GENESIS 3 (G-3), a modular reimplementation of the GENESIS neural simulator that has capabilities uniquely suited for multi-scale modeling.

The international faculty includes:

Dr. James M. Bower (University of Texas System) who has been involved in the development of software tools for multi-scale modeling for 30 years.

Dr. David Beeman (University of Colorado) who has supported multi-scale modeling both as an instructor in numerous international courses in computational biology as well as in his role as director of the GENESIS users group.

Dr. Avrama Blackwell (George Mason University) who’s modeling and experimental expertise involves the investigation of molecular synaptic mechanisms.

Dr. Hugo Cornelis (Lead GENESIS developer) with expertise both in the design and construction of multi-scale simulation systems as well as modeling at single cell and network levels.

Dr. Volker Steuber (University of Hertfordshire) with expertise in biochemical, single cell, network and systems level modeling and analysis.

Mr. Armando Rodriguez (University of Texas San Antonio) an expert in interface design and interoperability in simulations systems.

Application deadline is April 10.

Applications and inquiries should be sent to: gen3@gradschool.uni-luebeck.de

 

(2) Workshop “Multi-Scale Modeling in Computational Neuroscience II: Challenges and Opportunities”. to be held at the Computational Neuroscience Conference (CNS 2012) in Atlanta/Decatur, July 25, 2012

Organizers: James M. Bower University of Texas Health Science Center, San Antonio, TX (bower@uthscsa.edu) and Ilya A. Rybak Drexel University College of Medicine, Philadelphia, PA (rybak@drexel.edu).

http://www.cnsorg.org/assets/CNS_Meetings/CNS2012/Workshops2012/bower_workshop_at_cns2012-final.pdf

Following last year’s highly successful CNS 2011 workshop, we will once again consider and discuss challenges and issues in multi-scale modeling as they apply to understanding nervous systems. The workshop is being organized by the co-chairs of the Computational Neuroscience Working Group of IMAG, a multi-federal agency consortium based at the National Institutes of Health, tasked with exploring and developing multi-scale modeling in biology. including the U.S. National Institutes of Health, the U.S. National Science Foundation. (http://www.imagwiki.nibib.nih.gov/mediawiki/index.php?title=Main_Page). The results of this discussion will be added to the IMAG wiki and will be presented to the Multi-scale Modeling Consortium at NIH. This workshop therefore represents an opportunity for the CNS community to influence the direction of future funding formodeling in general and multi-scale modeling efforts in particular.

 

(3) Workshop “Multi-Scale Modeling in the Nervous System” at the 34th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC 2012) (www.embc2012.embs.org) was held at the Bayfront Hilton Hotel, San Diego on August 28th, 2012

Organized and chaired by Vasilis Z. Marmarelis Professor of Biomedical & Electrical Engineering, University of Southern California (USC).

http://bmsr.usc.edu/

This Workshop brought together experts on the subject of “Multi-Scale Modeling in the Nervous System”, which is attracting increasing attention worldwide because of its fundamental importance in understanding the hierarchical functional organization of the nervous system. The Workshop speakers presented their summary thoughts and recent work that pertain to the fundamental issues of multi-scale modeling of the nervous system (neuronal interconnectivity, emerging properties of neuronal ensembles etc.).

The list of speakers (in alphabetical order):

Theodore Berger (USC) Kwabena Boahen (Stanford University) Sam Deadwyler (Wake-Forest University) Mounya Elhilali (Johns Hopkins University) Jack Gallant (UC, Berkeley) Bill Lytton (SUNY Downstate) Vasilis Marmarelis (USC) Mayank Mehta (UCLA) Terry Sanger (USC) Christoph Schreiner & Craig Atencio (UCSF) James Schwaber (Jefferson Univ.) Terry Sejnowski (Salk Institute)

Current State of the Art:NineML - A new simulation tool for multiscale modeling in neuroscience

The International Neuroinformatics Coordinating Facility (INCF, Chair: Erik De Schutter, Okinawa Institute of Science and Technology, Okinawa, Japan) developed a new standard markup language for model description. Based on lessons learned with previous efforts in computational neuroscience and in other fields like systems biology, a concerted effort is made to develop a well-defined but flexible syntax for a self-documenting markup language that will be easy to extend and that can form the basis for specific implementations covering a wide range of modeling scales. The initial effort focuses on describing a growing area of computational neuroscience, spiking networks. This language, called NineML (Network Interchange format for NEuroscience) is based on a layered approach: an abstraction layer allows a full mathematical description of the models, including events and state transitions, while the user layer contains parameter values for specific models. Official release was expected by the end of 2010.

NineML - A description language for spiking neuron network modeling: The user layer

Anatoli Gorchetchnikov¹, and the INCF Multiscale Modeling Taskforce²

¹ Department of Cognitive and Neural Systems, Boston University, Boston, MA 02215, USA ² INCF Secretariat, Karolinska Institutet, Nobels väg 15 A, SE-171 77 Stockholm, Sweden E-mail: anatoli@bu.edu

With an increasing number of studies related to large-scale neuronal network modeling, the International Neuroinformatics Coordinating Facility (INCF) has identified a need for standards and guidelines to ease model sharing and facilitate the replication of results across different simulators. To create such standards, the INCF formed a program on Multiscale Modeling with a goal to develop a common standardized description language for neuronal network models. The first version of the proposed standard - the Network Interchange for Neuroscience Modeling Language (NineML) - is designed for the description of large networks of spiking neurons. NineML consists of two layers: an abstraction layer that provides the core concepts, mathematics and syntax with which model variables and state update rules are explicitly described and a user layer that provides syntax to specify the instantiation and parameterization of a network model in biological terms. Here we describe the details of the user layer from the first draft proposal of NineML. The user layer provides the syntax for specifying the model and parameters to be used to instantiate the key elements of a spiking neuron network. This includes descriptions of individual elements (cells, synapses, inputs) and the constructs for describing the grouping of these entities into networks. In addition the user layer defines the syntax for specifying a range of connectivity patterns. The user layer is intended to be primarily machine-readable and uses XML syntax. It is designed with a focus on ease of parsing, verification, and automatic model construction. This does not prevent advanced users from editing the user layer XML descriptions by hand, but the primary means for creation of these descriptions is expected to be software tools that will convert GUI- or script-based representations of objects and properties into valid XML. NineML aims to provide a tool to explicitly define a spiking neuron network model both conceptually and mathematically in a simulator independent manner. In addition, NineML is designed to be self-consistent and highly flexible, allowing addition of new models and mathematical descriptions without modification of the previous structure and organization of the language. To achieve these goals, the language is being iteratively designed using several representative models with various levels of complexity [1-6] as test cases. Acknowledgments

This work is supported by the International Neuroinformatics Coordinating Facility (INCF). Members of this Task Force include primary contributors to projects including the Blue Brain Project, GENESIS-3, KInNeSS, MOOSE, NEURON, NEST, PyNN and NeuroML.

References

1. Brunel N. Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. Journal of Computational Neuroscience 2000, 8:183-208.

2. Morrison A, Aertsen A, and Diesmann M. Spike timing dependent plasticity in balanced random networks. Neural Computation 2007, 19:1437-1467. 3. Hill SL, Tononi G. Modeling sleep and wakefulness in the thalamocortical system. J Neurophysiol 2005, 93:1671-1698.

4. Marino J, Schummers J, Lyon DC, Schwabe L, Beck O, Wiesing P, Obermayer K, and Sur M. Invariant computations in local cortical networks with balanced excitation and inhibition. Nat Neurosci 2005, 8(2):194-201.

5. Troyer TW, Krukowski AE, Priebe NJ, and Miller KD. Contrast-invariant orientation tuning in cat visual cortex: thalamocortical input tuning and correlation-based intracortical connectivity. J Neurosci 1998, 18:5908-5927.

6. Vogels TP and Abbott LF. Signal propagation and logic gating in networks of integrate-and-fire neurons. J Neurosci 2005, 25(46):10786-10795.

NineML- A Description Language for Spiking Neuron Network Modeling: The Abstraction Layer

Ivan Raikov¹,³ and the INCF Multiscale Modeling Taskforce²

¹ Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Okinawa, Japan ² INCF Secretariat, Karolinska Institutet, Nobels väg 15 A, SE-171 77 Stockholm, Sweden ³ University of Antwerp, Antwerp, Belgium E-mail: raikov@oist.jp

With an increasing number of studies related to large-scale neuronal network modeling, the International Neuroinformatics Coordinating Facility (INCF) has identified a need for standards and guidelines to ease model sharing and facilitate the replication of results across different simulators. To create such standards, the INCF has formed a program on Multiscale Modeling to develop a common standardized description language for neuronal network models. The name of the proposed standard is Network Interchange for Neuroscience Modeling Language (NineML) and its first version is aimed at descriptions of large networks of spiking neurons. The design of NineML is divided in two semantic layers: an abstraction layer that provides the core concepts, mathematics and syntax with which model variables and state update rules are explicitly described and a user layer that provides a syntax to specify the instantiation and parameterization of a network model in biological terms. The key concepts of spiking neuron network modeling are 1) spiking neurons 2) synapses 3) populations of neurons and 4) connectivity patterns across populations of neurons. Accordingly, the INCF task force on multiscale modeling has identified a set of mathematical abstractions that are capable of representing these concepts in a computer language. First, we propose a flexible block diagram notation for describing spiking dynamics. The notation represents continuous and discrete variables, their evolution according to a set of rules such as a system of ordinary differential equations, and the conditions that induce a regime change, such as the transition from subthreshold mode to spiking and refractory modes. The notation we have developed is an explicit formalization of event handling and is an important step in ensuring model simulation consistency. In addition, the abstraction layer provides the notation to describe a variety of topographical arrangements of neurons and populations, and to describe random connectivity patterns between neuronal populations, based on structural properties of neuronal networks.

This work is supported by the International Neuroinformatics Coordinating Facility (INCF). Members of this Task Force include primary contributors to projects including the Blue Brain Project, GENESIS-3, KInNeSS, MOOSE, NEURON, NEST, PyNN and NeuroML.

http://www.nineml.org/index.shtml

http://www.nineml.org/news/news.shtml

Challenges and Opportunities:Workshop: Multi-Scale Modeling in Computational Neuroscience: Challenges and Opportunities

This workshop was held as a part of the Annual Computational Neuroscience Meeting, CNS*2011

July 28, 2011 in Stockholm, Sweden

Organizers:

James M. Bower, University of Texas Health Science Center, San Antonio, TX (bower@uthscsa.edu);

Ilya A. Rybak, Drexel University College of Medicine, Philadelphia, PA (rybak@drexel.edu).

In this workshop, Dr. Bower lead a discussion around the challenges and opportunities represented by multi-scale modeling in computational neuroscience. The workshop started with several short presentations concerning various aspects of multi-scale modeling in computational neuroscience. The results of this discussion will be summarized in the IMAG wiki and presented to the Multi-scale Modeling Consortium in August.

We hope that the workshop will allow the CNS community to influence the direction of future funding for modeling in general and multi-scale modeling efforts in particular. Details will follow...

Participants:

  1. Avrama Blackwell (George Mason U., USA); avrama@gmu.edu
  2. James Bower (U. Texas SA, USA); bower@uthscsa.edu
  3. Ekaterina Brocke (CSC, KTH, Sweden); remedius@kth.se
  4. Robert Cannon (Okinawa Inst. of Sci. and Technol., Japan); robert@tetensor.com
  5. Sharon Crook (Arizona State U., USA); sharon.crook@asu.edu
  6. David Devilbiss (U. Wisconsin, USA); ddevilbiss@wisc.edu
  7. Dennis Glanzman (NIMH, NIH, USA); glanzman@nih.gov
  8. Padraig Gleeson (U. College London, UK); p.gleeson@ucl.ac.uk
  9. Yuqiao Gu (French Natl. Inst. for Agriculture Research, France); yuqiao.gu@versailles.inra.fr
  10. Kevin Gurney (U. Sheffield, UK); k.gurney@shef.ac.uk
  11. Susanne Hollbacher (GCSC, U. Frankfurt, Germany); susanne.hoellbacher@gcsc.uui-frankfurt.de
  12. Marcus Kaiser (Newcastle U., UK); m.kaiser@newcastle.ac.uk
  13. Tae-Wook Ko (NIMS, South Korea); twko@nims.re.kr
  14. Daphne Krioneciti (U. Luebeck, Germany); daphne@inb.uni-luebeck.de
  15. Yuan Liu (NINDS, NIH, USA); liuyuan@ninds.nih.gov
  16. Oliver Muthmenn (NCBS, Bangalore, India); jensoliver@ncbs.res.in
  17. James Perlewitz (UC-LLNL, USA); perlewitz@earthlink.net
  18. Arnd Roth (U. College London, UK); arnd.roth@ucl.ac.uk
  19. Jonathan Rubin (U. Pittsburgh, USA); jonrubin@pitt.edu
  20. Ilya Rybak (Drexel U., USA); rybak@drexel.edu
  21. Malin Sandstrom (INCF, Sweden); malin.sandstrom@incf.org
  22. Ausra Saudargiene (Vyteseitas Magnus U., Lithuania); a.saudargiene@if.vdu.pt
  23. Volker Steuber (U. Hertfordshire, UK); v.steuber@herts.ac.uk
  24. Tyler Stigen (U. Minnesota, USA); tstigen@umn.edu
  25. Man Yi Yim (Bernstein Center, Freiburg, Germany); yim@bcf.uni-freiburg.de

NIH Program Predictive Multiscale Models of the Physiome in Health and Disease (PAR-08-023)

Three multiscale projects in the field of neuroscience were awarded in 2010:

1. PD/PI – Terence D. Sanger, Ph.D.

University of Southern California

R01 NS069214 (02/01/2010-12/31/2013)

High-speed simulation of developmental motor disorders

Abstract: When the brain is injured during development, there is an effect not only on immediate brain function but also on the future ability to learn and acquire skills. We propose to create and simulate multi-scale computational models of the effect of early structural injury on future motor function of the cortex and spinal cord. We will use programmable chips (FPGA's) and the new mathematical theory of "Likelihood Calculus" to build simulations of 300,000 neurons that run 500 times faster than real-time. The model will be fitted to electromyographic and kinematic data from children at two visits spaced one year apart, and predictions will be tested by comparison to a third visit one year later. High speed simulation of the effect of early injury has the potential to revolutionize the treatment of developmental neurological deficits because it can, in one week, simulate 10 years of future change. In doing so, it allows prediction of disease progression, prediction of the future effects of treatments, and detailed understanding of the interaction between brain injury and resulting disorders of movement, perception, and behavior. Because of these predictions, we can intervene much earlier with treatments customized to each patient's disease profile. With early intervention, it may be possible to attenuate or block the natural progression of their disease. With over 750,000 US children and adults affected by developmental brain disorders, early acquired brain injury, or childhood progressive brain disease, the applicability and potential impact of an early intervention and disease prediction technique are significant.

2. PD/PI – Mounya Elhilali, Ph.D.

Johns Hopkins University

R01 AG036424 (06/01/2010-05/31/2015)

Overcoming the Cocktail Party problem: A multi-scale perspective on the neurobio

Abstract: Despite the enormous advances in computing technology over the last decades, there are stills many tasks that are easy for a child, yet difficult for advanced computer systems. A particular challenge to most existing systems is dealing with complex acoustic environments, background noises and competing talkers: A challenge often experienced in cocktail parties (Cherry, 1953) and formally referred to as auditory scene analysis (Bregman, 1990). Progress in this field has tremendous implications and long- term benefits covering the medical, industrial, military and robotics domains; as well as improving communication aids (hearing aids, cochlear implants, speech-based human-computer interfaces) for the sensory-impaired and aging brains. Despite its importance for both engineering and perceptual sciences, the study of the neural underpinnings of auditory scene analysis remains in its infancy. This field is particularly challenged by the lack of integrative theories which incorporate our knowledge of the perceptual bases of scene analysis with the neural mechanisms along various stages of the auditory pathway. Because of the nature of the problem, the neural circuitry at play is intricate and multi-scale by design. The objective of the proposed research is to provide a systems view to modeling scene analysis which integrates mechanisms at the single neuron level, population level and across area interactions. The intellectual merit of the proposed theory is to elucidate the specific mechanisms and computational rules at play; facilitate its integration in engineering systems and enable generating novel testable predictions. The proposal investigates the key hypothesis that attention to a feature of a complex sound instantiates all elements that are coherent with this feature, thus binding them together as one perceptual "object" or stream. This "binding hypothesis" requires three scales of analyses: a micro-level mapping of complex sounds into a multidimensional cortical feature representation; a meso-level coherence analysis correlating activity in populations of cortical neurons; and macro-level feedback processes of attention and expectations that mediate auditory object formation. We shall formulate this hypothesis within a multi-scale computational framework that provides a unified theory for the neural underpinnings of auditory scene analysis. The three core research aims of this project explore all facets of this model employing computational and physiological approaches: Aim I. A multi-scale coherence model: The main goal is to formulate the "binding hypothesis" as a unified biologically plausible theory of auditory streaming, integrating multi-scale sensory with cognitive cortical mechanisms. This computational effort will incorporate findings from experiments in Aims II and III, generate testable predictions, as well as provide effective algorithmic implementations to tackle the "cocktail party problem" in biomedical applications; Aim II. Physiological investigations of the multi-scale coherence theory: Our aim is to use an animal model to record single-unit (micro-level, meso-level) and across area (macro-level) physiological activity in both primary auditory and prefrontal cortex, while presenting sufficiently complex acoustic environments so as to test and refine the computational model; Aim III. Refinement of the coherence theory with physiological and perceptual testing in humans: The objective is to directly test predictions from the model in human subjects, using magnetoencephalography (MEG) and psychoacoustic experiments. We shall particularly focus on the role of cortical mechanisms in scene analysis in normal and aging brains. The proposed research draws upon the expertise of a cross-disciplinary team integrating neurobiology and engineering. It is unique in that it is the first effort to postulate a role for coherence in the scene analysis problem, and to investigate the "binding hypothesis" integrating cortical and attention mechanisms in auditory streaming experiments. In addition, by testing the theory directly on human subjects and comparing normal and aging brains (known to face perceptual difficulties in cocktail party settings), we hope to better understand the neural underpinnings of scene analysis under their normal and malfunctioning states, hence enhancing the translational potential of the model. The broader impact of this effort is to provide versatile and tractable models of auditory stream segregation, significantly facilitating the integration of such capabilities in engineering systems.

3. PD/PI – Ilya A. Rybak

Drexel University

R01NS069220 (09/01/2010- 08/31/2015)

Multiscale model of neural control of breathing

Abstract: Respiration in mammals is a primal homeostatic process, regulating levels of oxygen (O2) and carbon dioxide (CO2) in blood and tissues and is crucial for life. Rhythmic respiratory movements must occur continuously throughout life and originate from neural activity generated by specially organized circuits in the brain stem constituting the respiratory central pattern generator (CPG). The respiratory CPG generates rhythmic patterns of motor activity that produce coordinated movements of the respiratory pump (diaphragm, thorax, and abdomen), controlling lung inflation and deflation, and upper airway muscles, controlling airflow. These coordinated rhythmic movements drive exchange and transport of O2 and CO2 that maintain physiological homeostasis of the brain and body. Uncovering complex multilevel and multiscale mechanisms operating in the respiratory system, leading to mechanistic understanding of breathing, including breathing in different disease states requires a Physiome-type approach that relies on the development and explicit implementation of multiscale computational models of particular organs and physiological functions. The specific aims of this multi-institutional project are: (1) develop a Physiome-type, predictive, multiscale computational model of neural control of breathing that links multiple physiological mechanisms and processes involved in the vital function of breathing but operating at different scales of functional and structural organization, (2) validate this model in a series of complementary experimental investigations and (3) use the model as a computational framework for formulating predictions about possible sources and mechanisms of respiratory pattern alteration associated with heart failure. The project brings together a multidisciplinary team of scientists with long standing collaboration and complementary expertise in respiration physiology, neuroscience and translational medical studies (Thomas E. Dick, Case Western Reserve University; Julian F.R. Paton, University of Bristol, UK; Robert F. Rogers, Drexel University; Jeffrey C. Smith, NINDS, NIH, intramural), mathematics, system analysis and bioengineering (Alona Ben-Tal, Massey University, NZ), and computational neuroscience and neural control (Ilya A. Rybak, Drexel University). The end result of our proposed cross-disciplinary modeling and experimental studies will be the development and implementation of a new, fully operational, multiscale model of the integrated neurophysiological control system for breathing based on the current state of physiological knowledge. This model can then be used as a computational framework for formulating predictions about possible neural mechanisms of respiratory diseases and suggesting possible treatments.

 

 
Working Group Activities: 

Computational Neuroscience Working Group discussion notes - Plans for 2014-15:

Objective

  • Robust webinar schedule for education and training
  • BRAIN initiative is a timely and appropriate choice
  • as a central theme (will not be limited to this theme exclusively)

Enabler, TO DO:

  • BRAIN funded investigators – data types, modeling and analysis
  • Filter for investigators that are employing modeling
  • CRCNS investigators with focus on the multi-scale ones
  • Comput Neuroscience WG members
  • Industry

Objective

  • Consensus, formal position on the role of MSM in BRAIN and related initiatives
  • Enabler, TO DO:
  • BRAIN and MSM white paper
  • Need community edits and input
  • Need examples, specifics
  • Issues:
  • How to take this to NIH for consideration in future funding opportunities?
  • Should not be NIH specific, but other agencies may not be as influence-able?

Objective

  • Interactions with other WGs
  • Model Sharing, Multiscale Systems Biology, Clinical/Translational, etc

Issues:

  • modelDB example, may be a good anchoring resource as a starting point
  • Model credibility and provenance
  • Reproducibility of model simulations and results
  • NASA negative example – cardiovascular model

Enabler, TO DO:

  • Illustrative cases describing experiences in reproducing/reusing models
  • Crowd-source the experiences across the working group members
  • New wiki page on documenting and sharing model reuse experiences

Objective

  • Provide a discussion forum for asking questions on modeling methods

Enabler, TO DO:

  • Look at, settle on and use one of ResearchGate, Google Groups, etc

 

Webinars:Microconnectomics of primary motor cortex: a multiscale computer model

The dual-output hypothesis, based on analysis of wiring patterns in M1, describes neocortex as organized around Layer-5 corticostriatal pyramidal cells (STRs) which project to other cortical areas (as well as to striatum) and Layer-5  corticospinal pyramidal cells (SPIs), which project out of cerebrum to brainstem and spinal cord.  We propose the dual-output hypothesis as a replacement for  the old canonical-circuit model of Douglas and Martin.  The canonical-circuit was a major contribution in 1989, and remains today a touchstone for thinking about neocortex.  Although basic concepts from that model are valid, this model is insufficient for multiscale modeling since it implicitly uses point neurons and thereby neglects the key multiscale feature of cortex -- the large L5 pyramidal cells that span circuit layers at a higher scale.
 

Time Zone: Eastern Time (EST)

Webinar Date: 
Mon  Mar 2, 2015 15:00-16:00 
 
Presenters: 
William W. Lytton MD SUNY Downstate
Gordon MG Shepherd MD PhD Northwestern University

 

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