IARPA trying to revolutionize machine intelligence by using megascale neuronal circuits based on understanding of cortical elements

The IARPA MICrONS program is predicated on the notion that it will be possible to revolutionize machine intelligence if we can construct algorithms that utilize the same data representations, transformations, and learning rules as those employed and implemented by the cortical computing primitives.

Although a significant body of neuroscience data has been collected over the past 100+ years, the majority of what is known about the brain is about its microscale (one or a few neurons) or macroscale (hundreds of thousands or millions of neurons) operation. Much less is known about the detailed structure and function of the mesoscale cortical microcircuits (hundreds to tens of thousands of neurons) that embody the cortical computing primitives, because until recently there have been few tools available to interrogate the brain at the requisite resolution (nanometers) and scale (millimeters). MICrONS seeks to use emerging technologies in high-resolution and high-throughput brain mapping—such as serial electron microscopy and volumetric calcium imaging—to address this gap in our understanding of cortical computation and to exploit the findings to enhance machine intelligence.

The overall and specific goal of the MICrONS program is to create a new generation of machine learning algorithms derived from high-fidelity representations of cortical microcircuits to achieve human-like performance on complex information processing tasks.

Leading Artificial intelligence efforts at Google, Facebook, and Microsoft are not working with IARPA. The attendees were drawn from a mix of startups, universities, and IBM, which has a large-scale cognitive research effort.

Participants included companies such as: IBM, Qelzal Corp, Nvidia, Lambda Labs, Neuromorphic LLC, Numenta and Neurithmic Systems LLC.

And researchers from the following institutions were scheduled to turn up: Harvard and the Harvard Medical Center, SRI International, the Georgia Tech Research Institute, Rice, Rochester Institute of Technology, Downstate Medical Center, Oxford, Yale, Johns Hopkins, Washington University, Howard Hughes Medical Institute, Australia National University, Simons Foundation, University of Tennessee, University of California, George Mason University, Columbia, Arizona State University, University of Vienna, Baylor, Columbia, Princeton, UC Berkeley, UCLA, and MIT.

To achieve this goal, multidisciplinary teams will:

* Propose an algorithmic framework for information processing that is consistent with existing neuroscience data, but that cannot be fully realized without additional specific knowledge about the data representations, computations, and network architectures employed by the brain;

* Collect and analyze high-resolution data on the structure and function of cortical microcircuits believed to embody the cortical computing primitives underlying key components of the proposed framework;

* Generate computational neural models of cortical microcircuits informed and constrained by this data and by the existing neuroscience literature to elucidate the nature of the cortical computing primitives; and

* Implement novel machine learning algorithms that use mathematical abstractions of the identified cortical computing primitives as their basis of operation.

It is anticipated that algorithms created under MICrONS will be validated through their performance on complex auditory or visual scene parsing tasks, and will also demonstrate capacity for generalization to abstract, non-sensory data.



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