Publications related to Supervised learning and inference of spiking neural networks with temporal coding

Assessing directed information as a method for inferring functional connectivity in neural ensembles

Neurons in the brain form complicated networks through synaptic connections. Traditionally, functional connectivity between neurons has been analyzed using simple metrics such as correlation, which do not provide direction of influence. Recently, an inform ...

IEEE2011

Effective Stimuli for Constructing Reliable Neuron Models

Henry Markram, Sean Lewis Hill, Thomas Berger, Idan Segev

The rich dynamical nature of neurons poses major conceptual and technical challenges for unraveling their nonlinear membrane properties. Traditionally, various current waveforms have been injected at the soma to probe neuron dynamics, but the rationale for ...

Public Library of Science2011

Exponential Pattern Retrieval Capacity with Non-Binary Associative Memory

Mohammad Amin Shokrollahi, Krishna Kumar, Amir Hesam Salavati

We consider the problem of neural association for a network of non-binary neurons. Here, the task is to recall a previously memorized pattern from its noisy version using a network of neurons whose states assume values from a finite number of non-negative ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Extracting and Locating Temporal Motifs in Video Scenes Using a Hierarchical Non Parametric Bayesian Model

Jean-Marc Odobez, Rémi Emonet, Jagannadan Varadarajan

In this paper, we present an unsupervised method for mining activities in videos. From unlabeled video sequences of a scene, our method can automatically recover what are the recurrent temporal activity patterns (or motifs) and when they occur. Using non p ...

2011

PyBrain

Yi Sun, Daniël Pieter Wierstra, Tom Schaul

PyBrain is a versatile machine learning library for Python. Its goal is to provide flexible, easy-to-use yet still powerful algorithms for machine learning tasks, including a variety of predefined environments and benchmarks to test and compare algorithms. ...

Massachusetts Institute of Technology Press2010

Network Simplification: the Gaussian N-Relay Diamond Network

Christina Fragouli, Ayfer Özgür Aydin

We consider the Gaussian

N

-relay diamond network, where a source wants to communicate to a destination through a layer of

N

-relay nodes. We investigate the following question: What fraction of the capacity can we maintain by using only

k

out of the $ ...

2010

Code-specific policy gradient rules for spiking neurons

Wulfram Gerstner, Guillaume Hennequin

Although it is widely believed that reinforcement learning is a suitable tool for describing behavioral learning, the mechanisms by which it can be implemented in networks of spiking neurons are not fully understood. Here, we show that different learning r ...

Massachusetts Institute of Technology Press2009

Perceptual learning: why mice do not play backgammon

Elisa Tartaglia

Perceptual learning is the ability to modify perception through practice. As a form of brain plasticity, perceptual learning has been studied for more than thirty years in different fields including psychology, neurophysiology and computational neuroscienc ...

EPFL2009

Multiple task learning in recurrent neural networks using layered reservoirs

Martin Hasler, Ali Ajdari Rad, Mahdi Jalili

In this paper, to learn multiple tasks sharing same inputs, a two-layer architecture for a reservoir based recurrent neural network is proposed. The inputs are fed into the general workspace layer where the weights are adapted to provide maximum informatio ...

2009

Evolutionary Selection of Features for Neural Sleep/Wake Discrimination

Dario Floreano, Claudio Mattiussi, Walter Karlen, Peter Dürr, Jérémie Guignard

In biomedical signal analysis, artificial neural networks are often used for pattern classification because of their capability for nonlinear class separation and the possibility to efficiently implement them on a microcontroller. Typically, the network to ...

Hindawi Publishing Corporation2009

Supervised learning and inference of spiking neural networks with temporal coding

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Assessing directed information as a method for inferring functional connectivity in neural ensembles

Effective Stimuli for Constructing Reliable Neuron Models

Exponential Pattern Retrieval Capacity with Non-Binary Associative Memory

Extracting and Locating Temporal Motifs in Video Scenes Using a Hierarchical Non Parametric Bayesian Model

PyBrain

Network Simplification: the Gaussian N-Relay Diamond Network

Code-specific policy gradient rules for spiking neurons

Perceptual learning: why mice do not play backgammon

Multiple task learning in recurrent neural networks using layered reservoirs

Evolutionary Selection of Features for Neural Sleep/Wake Discrimination

Code-specific policy gradient rules for spiking neurons

Assessing directed information as a method for inferring functional connectivity in neural ensembles

Perceptual learning: why mice do not play backgammon

Network Simplification: the Gaussian N-Relay Diamond Network

Exponential Pattern Retrieval Capacity with Non-Binary Associative Memory

Evolutionary Selection of Features for Neural Sleep/Wake Discrimination

Extracting and Locating Temporal Motifs in Video Scenes Using a Hierarchical Non Parametric Bayesian Model

Effective Stimuli for Constructing Reliable Neuron Models

PyBrain

Multiple task learning in recurrent neural networks using layered reservoirs