IARPA posts RFI for neurally inspired computing principles
On January 4, IARPA posted the following request for information for Neurally Inspired Computing Principles (Solicitation Number: IARPA-RFI-16-02). Responses to this RFI are due no later than 5:00pm Eastern Time on January 29, 2016.
The Intelligence Advanced Research Projects Activity (IARPA) is seeking information on neurally-inspired computing principles. This request for information (RFI) is issued solely for information gathering purposes; the RFI does not constitute a formal solicitation for proposals. IARPA anticipates that responses to this RFI will be used to help organize a workshop on this topic at the 2016 Neuro Inspired Computational Elements conference (http://niceworkshop.org/) and to inform future programs in this technical area. The following sections of this announcement contain details of the scope of technical efforts of interest, along with instructions for the submission of responses.
Seventy years ago, John von Neumann found inspiration for the design of the 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.
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).
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.
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.
Full information is available here.