Neurally Inspired Computing Principles

The Intelligence Advanced Research Projects Activity (IARPA) is seeking information on methods to model and forecast rare events. This request for information (RFI) is issued solely for information gathering and planning purposes; this RFI does not constitute a formal solicitation for proposals. The following sections of this announcement contain details of the scope of technical efforts of interest, along with instructions for the submission of responses.

Background & Scope

Seventy years ago, John von Neumann found inspiration for the design of the Electronic Discrete Variable Automatic Computer (EDVAC) in what was then known about the design of the brain. However, despite the early influence of neuroscience on what has become known as the von Neumann architecture, the principles of computing underlying today's state of the art digital systems deviate substantially from the principles that govern computing in the brain. In particular, whereas mainstream computers rely on synchronous operations, high precision, and clear physical and conceptual separations between storage, data, and logic; the brain relies on asynchronous messaging, low precision storage that is co-localized with processing, and dynamic memory structures that change on both short and long time scales. To understand the potential opportunities and challenges in developing next-generation computers that exploit these and other principles of neural computing, IARPA is seeking information from two groups of experts: (1) computer scientists with experience in designing or building computing systems that rely on the same or similar principles as those employed by the brain; and (2) neuroscientists who have credible ideas for how neural computing principles can offer practical benefits for next-generation computers.

Responses to this RFI should clearly and concisely answer the questions posed below (to neuroscientists, computer scientists, or both) in one or more of the following topics:

Topic 1: Spike-based representations

Brains operate using spike-based codes that often appear sparse in time and across populations of neurons. In many systems, these codes appear noisy or imprecise, suggesting a plausible role for approximate computation in brain function.

For neuroscientists:
  • What practical benefits could our current understanding of the brain's use of spike-based representations, sparse coding, and/or approximate computation offer for next-generation computers? What gaps in understanding or challenges must first be overcome?
  • Have there been simulations or demonstrations of how spike-based representations, sparse coding, and/or approximate computation can be used to perform real-world tasks?
For computer scientists:
  • What is the current state of research in the use of spike-based representations, sparse coding, and/or approximate computations for digital or analog computing systems?
  • Are there existing hardware systems that utilize representations similar to spikes? If so, in which application areas and use cases have these been deployed, and what are their performance characteristics?

Topic 2: Asynchronous computation

Brains do not employ a global clock signal to synchronously update all computing elements at once. Instead, neurons function independently by default, and only transiently coordinate their activity (e.g. when participating in a coherent cell assembly).

For neuroscientists:
  • What practical benefits could our current understanding of the brain's use of asynchronous computation and/or transient coordination offer for next-generation computers? What gaps in understanding or challenges must first be overcome?
  • Have there been simulations or demonstrations of how asynchronous computation and/or transient coordination can be used to perform real-world tasks?

For computer scientists:

  • What is the current state of research in the use of asynchronous computation and/or transient coordination for digital or analog computing systems?
  • Are there existing hardware systems that utilize asynchronous computation and/or transient coordination? If so, in which application areas and use cases have these been deployed, and what are their performance characteristics?
Topic 3: Learning

Brains employ plasticity mechanisms that operate continuously and over multiple time scales to support online learning. Remarkably, brains continue to operate stably during ongoing plasticity.

For neuroscientists:
  • What practical benefits could our current understanding of the brain’s use of online learning over short and long time scales offer for next-generation computers? What gaps in understanding or challenges must first be overcome?
  • Have there been simulations or demonstrations of how online learning over short and long time scales can be used to perform real-world tasks?
  • For computer scientists:
  • What is the current state of research in the use of online learning over short and long time scales for digital or analog systems?
  • Are there existing hardware systems that utilize online learning over short and long time scales? If so, in which application areas and use cases have these been deployed, and what are their performance characteristics?
Topic 4: Co-local memory storage and computation

Brains do not strictly segregate memory and computing elements, as in the traditional von Neumann architecture. Rather, the synaptic inputs to a neuron can serve the dual role of storing memories and supporting computation.

For neuroscientists:
  • What practical benefits could our current understanding of the brain's use of co-local memory storage and computation offer for next-generation computers? What gaps in understanding or challenges must first be overcome?
  • Have there been simulations or demonstrations of how co-local memory storage and computation can be used to perform real-world tasks?
  • For computer scientists:
  • What is the current state of research in the use of co-local memory storage and computation for digital or analog computing systems?
  • Are there existing hardware systems that utilize co-local memory storage and computation? If so, in which application areas and use cases have these been deployed, and what are their performance characteristics?

Preparation Instructions to Respondants

IARPA appreciates responses from all capable and qualified sources from within and outside of the US. This announcement contains all of the information required to submit a response. No additional forms, kits, or other materials are needed. All responses shall be formatted for printing on standard 8.5” x 11” paper with 1-inch margins. Text shall be no smaller than 12-point Times New Roman font. Responses shall include:

  • A 1-page cover sheet that identifies the document as a response to IARPA RFI 16-02; lists all contributing authors, their respective organizations, and their email addresses; and provides a primary technical and administrative point of contact;
  • A substantive, focused, one-page executive summary;
  • Responses to the questions (for neuroscientists, computer scientists, or both) in one or more of the topics enumerated above (limited to 10 pages);
  • A list of citations; and
  • Copies of any unpublished or otherwise inaccessible manuscripts referenced in the text.

Submission Instructions to Respondents

Responses to this RFI are due no later than 5:00pm Eastern Time on January 29, 2016. All submissions must be electronically submitted to dni-iarpa-rfi-16-02@iarpa.gov as a single PDF document. Inquiries to this RFI must be submitted to dni-iarpa-rfi-16-02@iarpa.gov. Do not send questions with proprietary content. No telephone inquiries will be accepted.

Disclaimers and Important Notes

This is an RFI issued solely for information and planning purposes and does not constitute a solicitation. Respondents are advised that IARPA is under no obligation to acknowledge receipt of the information received, or provide feedback to respondents with respect to any information submitted under this RFI. Responses to this notice are not offers and cannot be accepted by the Government to form a binding contract. Respondents are solely responsible for all expenses associated with responding to this RFI. IARPA will not provide reimbursement for costs incurred in responding to this RFI. It is the respondent's responsibility to ensure that the submitted material has been approved for public release by the information owner. The Government does not intend to award a contract on the basis of this RFI or to otherwise pay for the information solicited, nor is the Government obligated to issue a solicitation based on responses received. Neither proprietary nor classified concepts or information should be included in the submission. Input on technical aspects of the responses may be solicited by IARPA from non-Government consultants/experts who are bound by appropriate non-disclosure requirements.

Contact Information:

Dr. Jacob Vogelstein
Intelligence Advanced Research Projects Activity
dni-iarpa-rfi-16-02@iarpa.gov

IARPA-RFI-16-02  CLOSED

Posted Date: January 4, 2016
Responses Due: January 29, 2016