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Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems
For years, researchers have used the theoretical tools of engineering to understand neural systems, but much of this work has been conducted in relative isolation. In Neural Engineering, ChrisExpand
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How to Build a Brain: A Neural Architecture for Biological Cognition
Contents 1 The science of cognition 1.1 The last 50 years 1.2 How we got here 1.3 Where we are 1.4 Questions and answers 1.5 Nengo: An introduction Part I: How to build a brain 2 An introduction toExpand
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A Large-Scale Model of the Functioning Brain
Modeling the Brain Neurons are pretty complicated cells. They display an endless variety of shapes that sprout highly variable numbers of axons and dendrites; they sport time- and voltage-dependentExpand
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Hyperopt: a Python library for model selection and hyperparameter optimization
Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriateExpand
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Nengo: a Python tool for building large-scale functional brain models
Neuroscience currently lacks a comprehensive theory of how cognitive processes can be implemented in a biological substrate. The Neural Engineering Framework (NEF) proposes one such theory, but hasExpand
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A Unified Approach to Building and Controlling Spiking Attractor Networks
  • C. Eliasmith
  • Medicine, Computer Science
  • Neural Computation
  • 1 June 2005
Extending work in Eliasmith and Anderson (2003), we employ a general framework to construct biologically plausible simulations of the three classes of attractor networks relevant for biologicalExpand
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Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn
Hyperopt-sklearn is a new software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice ofExpand
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Spiking Deep Networks with LIF Neurons
We train spiking deep networks using leaky integrate-and-fire (LIF) neurons, and achieve state-of-the-art results for spiking networks on the CIFAR-10 and MNIST datasets. This demonstrates thatExpand
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Large-Scale Synthesis of Functional Spiking Neural Circuits
In this paper, we review the theoretical and software tools used to construct Spaun, the first (and so far only) brain model capable of performing cognitive tasks. This tool set allowed us toExpand
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Deep networks for robust visual recognition
Deep Belief Networks (DBNs) are hierarchical generative models which have been used successfully to model high dimensional visual data. However, they are not robust to common variations such asExpand
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