PetaVision: A Guided Tour Brian Broom-Peltz (April 24, 2015)

NICE 2015 - Deep, Sparse Representations of Form, Depth and Motion Garrett Kenyon (February 25, 2015)

Please enjoy the follow exerpt videos from the presentation

Check out the other talks from the 2015 NICE Conference here:

Using Locally Competitive Algorithms to Model Top-Down and Lateral Interactions Garrett Kenyon (November 7, 2013)

Check out the other talks from the Redwood Center:

Cracking the Neural Code: Discovering the Language of the Brain Garrett Kenyon (November 13, 2007)


Biologically Inspired Distributed Sensor Networks: Collective Signal Amplification via Ultra-Low Bandwidth Spike-Based Communication S Y Lundquist, D M Paiton, B M Nowers, P F Schultz, S P Brumby, A M Jorgensen, G T Kenyon (2013)

Abstract: Wireless networks of biologically inspired distributed sensors (BIDS) are hypothesized to enable improved overall detection accuracy using ultra-low power and low bandwidth spike-based communication between nodes. Unlike traditional sensor networks, in which nodes communicate via digital protocols that require precise decoding of binary signal packets, BIDS nodes communicate by broadcasting generic radio frequency pulses, or spikes. Individual BIDS nodes are modeled after leaky integrate-and-fire (LIF) neurons, in which both filtered sensory signals and inputs from other BIDS nodes are accumulated as capacitive charge that decays with a characteristic time constant. A BIDS node itself broadcasts a spike whenever its internal state exceeds a threshold value. Here we present detailed simulations of a BIDS network designed to detect a moving target -- modeled as a pure acoustic tone with a translating origin -- against a background of 1/f noise. In the absence of a target, the average internal state is well below threshold and noise-induced spikes recruit little additional activity. In contrast, the presence of a target pushes the average internal state closer to threshold, such that each spike is now able to recruit additional spikes, leading to a chain reaction. Our results show that while individual BIDS nodes may be noisy and unreliable, a network of BIDS nodes is capable of highly reliable detection even when the signal-to- noise ratio (SNR) on individual nodes is low. We demonstrate that collective computation between nodes supports improved detection accuracy in a manner that is extremely robust to the damage or loss of individual nodes.

PetaVision: Simulating the Thoughts behind the Scenes 1663 (May 2009)

1663 is a Los Alamos Science and Technology Magainze published in print and online throughout the year highlighting different research occuring up at the lab. To discover more science from the lab, check out past and present publications from their website: