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Neural Pre-coding Increases the Pattern Retrieval Capacity of Hopﬁeld and Bidirectional Associative Memories

Publications associées (36)

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Slow dynamics in structured neural network models

Samuel Pavio Muscinelli

Humans and some other animals are able to perform tasks that require coordination of movements across multiple temporal scales, ranging from hundreds of milliseconds to several seconds. The fast timescale at which neurons naturally operate, on the order of ...

EPFL2018

Neural assemblies as core elements for modeling neural networks in the brain

Hesam Setareh

How does the brain process and memorize information? We all know that the neuron (also known as nerve cell) is the processing unit in the brain. But how do neurons work together in networks? The connectivity structure of neural networks plays an important ...

EPFL2017

Investigating Cross-lingual Multi-level Adaptive Networks: The Importance of the Correlation of Source and Target Languages

Petr Motlicek, Philip Neil Garner

The multi-level adaptive networks (MLAN) technique is a cross-lingual adaptation framework where a bottleneck (BN) layer in a deep neural network (DNN) trained in a source lan- guage is used for producing BN features to be exploited in a second DNN in a ta ...

2016

Scalable Neuron Circuit Using Conductive-Bridge RAM for Pattern Reconstructions

Behnoush Attarimashalkoubeh

— A novel neuron circuit using a Cu/Ti/Al2O3-based conductive-bridge random access memory (CBRAM) device for hardware neural networks that utilize nonvolatile memories as synaptic weights is introduced. The neuronal operations are designed and proved using ...

Institute of Electrical and Electronics Engineers2016

When Neurons Fail - Technical Report

Rachid Guerraoui, El Mahdi El Mhamdi

Neural networks have been traditionally considered robust in the sense that their precision degrades gracefully with the failure of neurons and can be compensated by additional learning phases. Nevertheless, critical applications for which neural networks ...

EPFL2016

Slow fluctuations in recurrent networks of spiking neurons

Tilo Schwalger

Networks of fast nonlinear elements may display slowfluctuations if interactions are strong. We find a transition in the long-term variability of a sparse recurrent network of perfect integrate-and-fire neurons at which the Fano factor switches from zero t ...

Amer Physical Soc2015

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

Bayesian Inference for Spiking Neuron Models with a Sparsity Prior

Matthias Seeger, Matthias Bethge

Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the expectation propagation algorithm, we are able to approximate the full poster ...

2008

Neural modeling of the cortical processes underlying imitative behaviors

Eric Sauser

Imitation is the ability to recognize, learn and reproduce the actions of others. In addition to facilitating the transmission of knowledge and skills, it has been suggested that this fundamental cognitive capacity is at the origin of other human faculties ...

EPFL2008

Cryptanalysis of an E0-like Combiner with Memory

Serge Vaudenay, Yi Lu

In this paper, we study an E0-like combiner with memory as the keystream generator. First, we formulate a systematic and simple method to compute correlations of the FSM output sequences (up to certain bits). An upper bound of the correlations is given, wh ...

2008