# A Technical Overview of the Neural Engineering Framework

@inproceedings{Stewart2012ATO, title={A Technical Overview of the Neural Engineering Framework}, author={Terrence C. Stewart}, year={2012} }

The Neural Engineering Framework (NEF) is a general methodology that allows you to build largescale, biologically plausible, neural models of cognition [1]. In particular, it acts as a neural compiler: you specify the properties of the neurons, the values to be represented, and the functions to be computed, and it solves for the connection weights between components that will perform the desired functions. Importantly, this works not only for feed-forward computations, but recurrent connections…

## 23 Citations

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### Implementation of the Neural Engineering Framework on the TrueNorth Neurosynaptic System

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### Dynamics and Control in Spiking Neural Networks

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Results show that classical control architectures, including, planning, feedback control, estimation, and learning, could indeed be enacted by a single recurrent spiking network with biophysically plausible dynamics, and provide a way to achieve distributed control over networks, which may confer robustness properties above and beyond traditional centralized control designs.

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