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Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
A new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks, based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry.
Simulation of networks of spiking neurons: A review of tools and strategies
- R. Brette, M. Rudolph-Lilith, A. Destexhe
- Computer ScienceJournal of Computational Neuroscience
- 28 November 2006
This work provides a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin–Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies.
Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning
- W. Zellinger, Thomas Grubinger, E. Lughofer, T. Natschläger, Susanne Saminger-Platz
- Computer ScienceICLR
- 1 February 2017
Central Moment Discrepancy achieves a new state-of-the-art performance on most domain adaptation tasks of Office and outperforms networks trained with MMD, Variational Fair Autoencoders and Domain Adversarial Neural Networks on Amazon reviews.
Real-Time Computation at the Edge of Chaos in Recurrent Neural Networks
It is shown that only near the critical boundary can recurrent networks of threshold gates perform complex computations on time series, which strongly supports conjectures that dynamical systems that are capable of doing complex computational tasks should operate near the edge of chaos.
Spatial and temporal pattern analysis via spiking neurons.
This paper shows how these delays can be learned using exclusively locally available information and gives rise to a biologically plausible algorithm for finding clusters in a high-dimensional input space with networks of spiking neurons, even if the environment is changing dynamically.
Computational models for generic cortical microcircuits
A computational model that could explain the potentially universal computational capabilities and does not require a task-dependent construction of neural circuits is proposed, based on principles of high dimensional dynamical systems in combination with statistical learning theory, and can be implemented on generic evolved or found recurrent circuitry.
PCSIM: A Parallel Simulation Environment for Neural Circuits Fully Integrated with Python
This paper investigates how the automatically generated bidirectional interface and PCSIM's object-oriented modular framework enable the user to adopt a hybrid modeling approach: using and extending PC SIM's functionality either employing pure Python or C++ and thus combining the advantages of both worlds.
A Model for Real-Time Computation in Generic Neural Microcircuits
A new computational model is proposed that is based on principles of high dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry.
Computer models and analysis tools for neural microcircuits
The features of a new website that facilitates the creation of computer models for cortical neural microcircuits of various sizes and levels of detail are described, as well as tools for evaluating the computational power of these models in a Matlabenvironment.
The "Liquid Computer": A Novel Strategy for Real-Time Computing on Time Series
This survey article discusses a new framework for analysing computations on time series and in particular on spike trains, introduced in Maass et.