Connectionists: New funding opportunity: Machine Intelligence from Cortical Networks (MICrONS) Program

[R. Jacob Vogelstein]

I am pleased to announce the release of the Machine Intelligence from
Cortical Networks (MICrONS) program Broad Agency Announcement (BAA).  MICrONS
seeks to revolutionize machine learning by reverse-engineering the
algorithms of the brain.  The program is expressly designed as a dialogue
between data science and neuroscience, in which participants will have the
unique opportunity to pose biological questions with the greatest potential
to advance theories of neural computation and obtain answers through
carefully planned experimentation and data analysis.  Over the course of
the program, participants will use their improving understanding of the
representations, transformations, and learning rules employed by the brain
to create ever more capable neurally-derived machine learning
algorithms.  Ultimate
computational goals for MICrONS include the ability to perform complex
information processing tasks such as one-shot learning, unsupervised
clustering, and scene parsing, aiming towards human-like proficiency.

The program overview is copied below; the full text of the announcement
(and any future versions) is available from the BAA link at
http://www.iarpa.gov/index.php/research-programs/microns/microns-baa.  For
your convenience, a PDF of the announcement is also attached to this
email.  All questions about the program and/or BAA must be submitted to
dni-iarpa-baa-14-06 at iarpa.gov by February 9, 2015.  Full proposals must be
submitted through the IARPA IDEAS <https://iarpa-ideas.gov> system by March
13, 2015.  *Do not *send any questions or proposal submissions to me
directly.

Please disseminate this information widely.  Offerors need not be U.S.
citizens or residents to apply or receive funding.

Thank you,
Jacob

R. Jacob Vogelstein, Ph.D.
Program Manager
ODNI/IARPA
http://go.usa.gov/FPPJ

--------------------------------------------------------

 Introduction

Despite significant progress in machine learning over the past few years,
today’s state of the art algorithms are brittle and do not generalize well.
In contrast, the brain is able to robustly separate and categorize signals
in the presence of significant noise and non-linear transformations, and
can extrapolate from single examples to entire classes of stimuli.  This
performance gap between software and wetware persists despite some
correspondence between the architecture of the leading machine learning
algorithms and their biological counterparts in the brain, presumably
because the two still differ significantly in the details of operation.  The
MICrONS program is predicated on the notion that it will be possible to
achieve major breakthroughs in machine learning if we can construct
synthetic systems that not only resemble the high-level blueprints of the
brain, but also employ lower-level computing modules derived from the
specific computations performed by cortical circuits.
 Background

Many contemporary theories of cortical computing suggest that the brain
performs common sensory information processing tasks—such as detection and
recognition of visual objects, sounds, and odors—with algorithms that
progressively transform data through a series of operations, or “stages.”  Each
stage of processing is further theorized to occur within a discrete region
of cortex.  Although different theories suggest different mathematical
bases for computation, it is commonly believed that neural algorithms
employ data representations, transformations, and learning rules that are
conserved across stages.
<#14ae94551cb4b667_14ae942f2d046d85_14acb3be21100ccb__ftn1>  It should
therefore be possible to apprehend the neural computations underlying
information processing (at least within a given sensory modality
<#14ae94551cb4b667_14ae942f2d046d85_14acb3be21100ccb__ftn2>) by
interrogating a small fraction of the entire cortex, so long as that
fraction is judiciously selected to contain sufficient evidence of the
representations, transformations, and learning rules of the algorithm(s) to
which it contributes.

Neuroscience has a long history of inspiring innovation in machine
learning, starting with the seminal work of McCulloch and Pitts in 1943.  This
influence is evident even in today’s state of the art “deep learning”
systems, which are loosely modeled on hierarchical visual processing
systems in the primate brain.  However, the rate of effective knowledge
transfer between neuroscience and machine learning has been slow because of
divergent scientific priorities, funding sources, knowledge repositories,
and lexicons.  As a result, very few of the ideas about neural computing
that have emerged over the past few decades have been incorporated into
modern machine learning algorithms.

Previous attempts to foster collaboration between neuroscience and machine
learning have been stymied in part by gaps in our knowledge about the brain.
The majority of what is known about the brain today regards its operation
at the “micro” scale (one or a few neurons) and the “macro” scale (hundreds
of thousands or millions of neurons), and some of this information is
indeed reflected in the design of leading artificial neural networks.  In
contrast, much less is known about the “mesoscale” cortical circuits
(hundreds to tens of thousands of neurons) that implement the specific data
representations, transformations, and learning rules of cortical
information processing algorithms, and these are therefore absent from (or
speculative in) existing machine learning solutions.  It is likely that
explicit knowledge and use of these computations is required to move beyond
the current generation of “neurally-inspired” machine learning algorithms.
Program Synopsis

The MICrONS program aims to create novel machine learning algorithms that
use neurally-inspired architectures *and* mathematical abstractions of the
representations, transformations, and learning rules employed by the brain
to achieve brain-like performance.  To guide the construction of these
algorithms, performers will conduct targeted neuroscience experiments that
interrogate the operation of mesoscale cortical computing circuits, taking
advantage of emerging tools for high-resolution structural and functional
brain mapping.  The program is designed to facilitate iterative refinement
of algorithms based on a combination of practical, theoretical, and
experimental outcomes: performers will use their experiences with the
algorithms’ design and performance to reveal gaps in their understanding of
cortical computation, and will collect specific neuroscience data to inform
new algorithmic implementations that address these limitations.  Ultimately,
as performers incorporate these insights into successive versions of the
machine learning algorithms, they will devise solutions that can perform
complex information processing tasks aiming towards human-like proficiency.

Program Structure

MICrONS is organized in three phases, totaling five years in duration.  During
each phase, performers conduct targeted neuroanatomical and
neurophysiological studies to inform their understanding of the cortical
computations underlying sensory information processing and, concurrently,
create neurally-derived machine learning algorithms that perform similar
functions.  Performers motivate their experimental and algorithmic designs
by formulating and updating a conceptual model or “theoretical framework”
for neural information processing in a given sensory modality.  They use
computational neural models (i.e., executable mathematic or algorithmic
simulations of neurons and neural circuits) to establish a correspondence
between the computations performed by biological wetware and the
computations employed by their machine learning software.  Each phase ends
with an information processing challenge that assesses how well the new
algorithms perform on increasingly challenging machine learning tasks:
similarity discrimination in Phase 1, generalization and classification in
Phase 2, and invariant recognition in Phase 3.  Performers use the results
of their experiments in each phase to guide their development of improved
algorithms in the subsequent phase (in Phase 1, performers base their
algorithms on the existing neuroscience literature).
Technical Areas

The MICrONS program comprises three Technical Areas (TAs).  Although IARPA
anticipates receiving a number of holistic proposals responding to all
three TAs, it recognizes that some prospective offerors may have
capabilities in only a subset of the overall program scope, and wishes to
maximize its opportunity to leverage these capabilities.  Therefore,
offerors may choose to propose to one, two, or all three TAs.  Because
achieving MICrONS program goals will require significant collaboration
across all three TAs, offerors who propose to only one or two TAs should be
prepared to work closely with performers in the remaining TAs.  The TAs in
MICrONS are defined as follows:


·         TA1 – experimental design, theoretical neuroscience,
computational neural modeling, machine learning, neurophysiological data
collection, and data analysis;

·         TA2 – neuroanatomical data collection; and

·         TA3 – reconstruction of cortical circuits from neuroanatomical
data and development of information technology systems to store, align, and
access neural circuit reconstructions with the associated
neurophysiological and neuroanatomical data.

Success in the MICrONS program will require extensive communication and
cooperation between performers in all three TAs within or across teams.  For
example, in TA2, performers must collect neuroanatomical data about the
same brain regions *in the same brain specimens* that are used in TA1 for
neurophysiological studies; in TA3, performers must reconstruct neural
circuits from the data collected in TA2; and in TA1, performers must
analyze the neural circuits generated in TA3 and use the resulting insights
in formulating their machine learning algorithms and theoretical frameworks.
All offerors are therefore required to include in their proposal a detailed
management plan  and a detailed description of how their proposed technical
approach in one or more TAs is likely to impact the other TAs.
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