B. Chandler

In the foreseeable future, synergistic advances in high-density memristive memory, scalable and massively parallel hardware, and neural network research will enable modelers to design large-scale, adaptive neural systems to support complex behaviors in virtual and robotic agents. Expand

MoNETA (Modular Neural Exploring Traveling Agent) is the software we're designing at Boston University's department of cognitive and neural systems, which will run on a braininspired microprocessor under development at HP Labs. Expand

The activities of neurons vary within small intervals that are bounded both above and below, yet the inputs to these neurons may vary many-fold. Expand

IPv4 fragmentation is not sufficiently robust for use under some conditions in today's Internet. Expand

In a synchronous digital platform for building large cognitive models, memristive nanodevices form dense, resistive memories that can be placed close to conventional processing circuitry. Expand

We introduce a novel method for mitigating the difficulty of classification through dataset augmentation via inpainting, which in turn relies on the ability to segment object pixels from background. Expand

The DARPA Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) initiative aims to create a new generation of high-density, low-power consumption chips capable of replicating adaptive and intelligent behavior observed in animals. Expand

This paper characterizes a subset of local learning rules amenable to implementation in memristive hardware. Expand

We focus on constraints related to learning in a simple visual system and those imposed by a new neural simulator for heterogeneous hardware systems, CogExMachina (Cog). Expand