Supported by NSF Convergence Accelerator 
Supported by NSF Convergence Accelerator
ModECI will provide a standardized Model Description Format (MDF) that can be used to exchange models between different software modeling environments and scientific domains and will be presented as an exchange standard, not a programming language. ONNX and NeuroML will provide guidance for the scope of MDF, and will be potential input/output formats. Such an MDF would have numerous benefits, both scientific and technological, including:
The purpose of the MDF is to provide a standard
format for describing computational models across a wide range of scientific disciplines
The ModECI MDF is in early stages of development.
See details on GitHubThe goal of the MDF is to provide a common exchange format that allows models created in one environment that supports the standard to be expressed in a form – and in sufficient detail – that it can be imported into another modeling environment that supports the standard, and then executed in that environment with identical results, and/or integrated with other models in that environment.
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ONNX (Open Neural Network Exchange) is a well-established and actively supported standard in the machine learning community.
We have leveraged this, by developing a proof of concept interface between MDF and ONNX, showing not only that MDF can complement ONNX as an exchange-oriented format among machine learning environments, but also bridge to ONNX from environments in other domains.
NeuroML is a widely accepted standard for describing biophysically-realistic models of neurons, neuronal subcomponents, and neural circuits in machine readable form, that is supported by a large number of existing programming and execution environments.
We have shown that MDF can be used to encode neuroscientific models created in NeuroML, allowing such models to be run on a wider range of simulation platforms.
We are planning to extend MDF support for mapping to and from the SONATA format for large scale neuronal modeling.
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PyTorch is a widely used framework in machine learning and we have developed an initial mapping between MDF models and PyTorch
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We are planning to extend MDF support to TensorFlow in the next phase of this project.
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PsyNeuLink is an open-source, software environment written in Python, and designed for the needs of neuroscientists, psychologists, computational psychiatrists and others interested in learning about and building models of the relationship between brain function, mental processes and behavior.
We have developed mappings between a number of model types in PsyNeulink and MDF, as a proof-of-concept for use of MDF in cognitive modelling.
We are planning to implement MDF support for the Emergent simulator in the next phase of this project.
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We have developed an initial mapping to MDF from the ACT-R framework for modeling cognitive processes.
More info
We are planning to implement MDF support for the Nengo simulator in the next phase of this project.
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We are planning to extend MDF support to The Virtual Brain simulator in the next phase of this project.
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Computational models of neuronal processes inspired by the biological brain are a key target of the MDF format.
More info
Supporting models of cognitive processes will be a fundamentally important part of the design of the MDF format.
More info
Network models generated by machine learning frameworks such as PyTorch and TensorFlow, and serialised in ONNX format, are an important target of MDF.
More info
MDF's support for multiple export formats mean that the models can be executed across a range of conventional hardware platforms.
More info
We are actively investigating mapping of models in MDF format to quantum hardware with our collaborators in Track C of the NSF Convergence Accelerator program
More info
The goal of the MDF is to provide a common exchange format that allows models created in one environment that supports the standard to be expressed in a form – and in sufficient detail – that it can be imported into another modeling environment that supports the standard, and then executed in that environment with identical results, and/or integrated with other models in that environment.
More info
ONNX (Open Neural Network Exchange) is a well-established and actively supported standard in the machine learning community.
We have leveraged this, by developing a proof of concept interface between MDF and ONNX, showing not only that MDF can complement ONNX as an exchange-oriented format among machine learning environments, but also bridge to ONNX from environments in other domains.
NeuroML is a widely accepted standard for describing biophysically-realistic models of neurons, neuronal subcomponents, and neural circuits in machine readable form, that is supported by a large number of existing programming and execution environments.
We have shown that MDF can be used to encode neuroscientific models created in NeuroML, allowing such models to be run on a wider range of simulation platforms.
We are planning to extend MDF support for mapping to and from the SONATA format for large scale neuronal modeling.
More info
PyTorch is a widely used framework in machine learning and we have developed an initial mapping between MDF models and PyTorch
More info
We are planning to extend MDF support to TensorFlow in the next phase of this project.
More info
PsyNeuLink is an open-source, software environment written in Python, and designed for the needs of neuroscientists, psychologists, computational psychiatrists and others interested in learning about and building models of the relationship between brain function, mental processes and behavior.
We have developed mappings between a number of model types in PsyNeulink and MDF, as a proof-of-concept for use of MDF in cognitive modelling.
We are planning to implement MDF support for the Emergent simulator in the next phase of this project.
More info
We have developed an initial mapping to MDF from the ACT-R framework for modeling cognitive processes.
More info
We are planning to implement MDF support for the Nengo simulator in the next phase of this project.
More info
We are planning to extend MDF support to The Virtual Brain simulator in the next phase of this project.
More info
Computational models of neuronal processes inspired by the biological brain are a key target of the MDF format.
More info
Supporting models of cognitive processes will be a fundamentally important part of the design of the MDF format.
More info
Network models generated by machine learning frameworks such as PyTorch and TensorFlow, and serialised in ONNX format, are an important target of MDF.
More info
MDF's support for multiple export formats mean that the models can be executed across a range of conventional hardware platforms.
More info
We are actively investigating mapping of models in MDF format to quantum hardware with our collaborators in Track C of the NSF Convergence Accelerator program
More info
Interaction with the wider computational community is essential to the development of the Model Description Format
October 27-28, 2020
Experts in areas of cognitive science/psychology, computational neuroscience, neuroinformatics, brain imaging and cognitive/symbolic modeling, systems and AI architecture, and Machine Learning participated in discussions on the feasibility of a Model Description Format (MDF) as posited by the ModECI team. The overall consensus - there is a real need for a format like MDF and each participant saw a practical benefit of MDF furthering convergence science in their respective domains.
Attendees:
November 19, 2020
Domain experts gathered to deliberate the impact an MDF could have on the cognitive science process-modeling community. Rich discussions solicited insightful feedback and cautionary tales from participants’ experiences, thus leading to a better-informed prototype for the ModECI MDF.
Attendees:
December 18, 2020
The ModECI team hosted their second focused group workshop, working with the Artificial Intelligence and Machine Learning/Deep Learning communities to identify user-needs and areas of growth for the project.
Experts in several areas including Python, PyTorch, and IR discussed the potential for the MDF while also providing valuable insight into future pathways.
Attendees:
February 8, 2021
The ModECI team hosted their third Domain-specific focus group workshop, working with the Neuroscience international community. The objective was to identify end-user needs and areas of growth for the project. Several topics were discussed including the importance of identifying use-cases, precision, benchmarks, environments, and procedural coding. The lively discussion and debates provided much feedback for the ModECI team to consider in building the prototype and for future launching.
Attendees:
April 9, 2021
The ModECI team hosted their fourth domain-specific focus group workshop, working with the quantum computing community. The objective of this workshop was to explore the capacity for MDF to use quantum technology. The group identified several areas for convergence and collaborative efforts were solidified.
Attendees:
April 24, 2021
The ModECI team hosted their final focus group workshop. The goal was to examine options for an open-source exposure. Suggestions from the participants included: joining professional group forums such as the International Neuroinformatics Coordinating Facility (INCF), development of strong governance model for the initiative, and hosting activities such as hackathons to solicit community engagement while also stirring community excitement.
Attendees:
We are partnering with Super.tech to investigate executing a range of MDF models on quantum computing platforms
Sponsor: University of California-San Diego
Sponsor: Vanderbilt University
Sponsor: Howard University
Sponsor: University of Iowa
Sponsor: Columbia University
Sponsor: Pennsylvania State University
About the ModECI project
MDF has been generously supported by the NSF Convergence Accelerator Program.
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Princeton Neuroscience Institute
Jonathan Cohen is a cognitive neuroscientist with over three decades of expertise in computational modeling of brain and cognitive function, and contributions that range from models of the neural mechanisms underlying cognitive function (e.g., ServanSchreiber et al., 1990; Miller & Cohen, 2001; Shenhav et al., 2013) to models of higher level cognitive functions such as multitasking, planning, and relational reasoning that span from cognitive science to ML (e.g., Musslick et al., 2016; Segert et al., 2020; Ho et al., 2020). He has also been lead or co-lead developer for several large software development efforts, including: PsyScope, the first graphically-oriented tool for the design and implementation of computer based cognitive experiments (Cohen et al., 1993); BrainIAK, an open-source toolbox for the application of advanced machine learning methods to the analysis of brain imaging data (co-lead with Ted Willke, Intel); PsyNeuLink, an open-source, graph-based environment for developing models of systems-level brain and cognitive function (collaboratively with Bhattacharjee); and SweetPea, for the declarative description of factorial design and unbiased sampling of trials in empirical and modeling studies. Cohen will take primary responsibility for overall coordination of the project, as well as interactions with the cognitive science and cognitive neuroscience communities.
The University of Texas at Austin
Tal Yarkoni is an informaticist with expertise in psychology, cognitive neuroscience and computational social science applications. He is the creator of the Neurosynth framework for large-scale automated meta-analysis of functional neuroimaging data (Yarkoni et al., 2011) and a number of other measurement and modeling techniques. He is a core contributor to the Brain Imaging Data Standard (BIDS) ecosystem, including principal contributions to the BIDS-StatsModels standard for machine-readable representation of fMRI statistical models, and co-developed the prototype BIDS-MDF spec with Cohen and Gleeson. Yarkoni has extensive expertise in formal standard development and automation technologies more broadly in the social and behavioral sciences (Yarkoni, et al.; Yarkoni, 2010). In addition to core contributions to feature prioritization and prototype development, Yarkoni will take the lead on identifying potential use cases in the social sciences, and developing and analyzing the surveys used to solicit community input.
University College London
Padraig Gleeson is a domain expert in biophysically detailed neural modeling, and is a lead developer of the NeuroML model description format (Cannon et al., 2014; Gleeson et al., 2010), as well as a long-standing editor of the standard. He is also involved in a number of neuroinformatics initiatives, including Open Source Brain (Gleeson et al., 2019), where standardized models in computational neuroscience can be shared with the community. He has collaborated with Prof. Cohen on the original development of BIDS-MDF, which included creating a functioning prototype of NeuroML to PsyNeuLink interoperability. He will be a main point of contact with the computational neuroscience community.
Yale Computer Science Dpt.
Abhishek Bhattacharjee is an experimental computer scientist with experience in heterogeneous hardware platforms and software environments. He will add expertise on the hardware/software portability and efficiency goals of this work. Bhattacharjee and Cohen have been working actively over the last three years on relevant software development projects, including compilation and LLVM IR representation of models in PsyNeuLink.
Intel Labs
Ted Willke is the director of the Brain Inspired Computing Lab, focused on the development of state-of-the-art machine learning algorithms that are inspired by neuroscience. He works on techniques that advance deep-learning based imaging analysis (Vyas et al., 2018), reinforcement learning (Haj-Ali et al., 2020), similarity learning (Ma et al., 2019), and language modeling (Turek et al., 2020), with a focus on hardware/software co-design problems and applications to systems research. He will help coordinate interactions with the machine learning community, focusing on exchange requirements among deep learning models, applications in machine learning, and their relationship to neuroscientific and cognitive models, and help oversee overall software design.
Arizona State University
Sharon Crook is the co-director of the Informatics and Computation in Open Neuroscience Lab at Arizona State University. Along with Gleeson, she is a leader of the NeuroML model description format initiative (Cannon et al., 2014; Gleeson et al., 2010), and she has developed and maintains the NeuroML-DB portal, where models and their characterizations are shared for efficient selection of models and re-use on multiple simulation platforms. She is involved with several other neuroinformatics initiatives including SciUnit, a framework for validating computational models against experimental data or testing model/model agreement. She will contribute to the further development of the MDF and associated tools that provide bridges between computational neuroscience, machine learning, and other scientific communities.
Super.tech
Pranav Gokhale is the Co-founder and CEO of Super.tech, a quantum software company. His research focuses on bridging the gap from quantum hardware to practical applications. Pranav's work in quantum computing has led to over a dozen peer-reviewed publications, three best paper awards, three patent applications, and several software packages. Pranav will collaborate with ModECI to link MDF—particularly for graph problems involving Ising models—to quantum solvers. This work will bring the quantum computing community into the MDF ecosystem.
National Research Council Canada, University of Waterloo Collaboration Centre
Terrence Stewart is a Research Officer for the National Research Council Canada, as part of the University of Waterloo Collaboration Centre. His research is on how cognitive architectures can be implemented in the physical brain, emphasizing how dynamics, timing, and energy consumption put important constraints on the types of algorithms that are suitable for cognition. While his initial work was in ACT-R, for the last ten years he has worked with Chris Eliasmith at the University of Waterloo to adapt such algorithms to be implementable with neurons, using the Neural Engineering Framework to generate large-scale spiking models capable of performing basic cognitive tasks.
Workshops
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