Leveraging Spiking Deep Neural Networks to Understand the Neural Mechanisms Underlying Selective Attention

Spatial attention enhances sensory processing of goal-relevant information and improves perceptual sensitivity. Yet, the specific neural mechanisms underlying the effects of spatial attention on performance are still contested. Here, we examine different attention mechanisms in spiking deep convolutional neural networks. We directly contrast effects of precision (internal noise suppression) and two different gain modulation mechanisms on performance on a visual search task with complex real-world images. Unlike standard artificial neurons, biological neurons have saturating activation functions, permitting implementation of attentional gain as gain on a neuron's input or on its outgoing connection. We show that modulating the connection is most effective in selectively enhancing information processing by redistributing spiking activity and by introducing additional task-relevant information, as shown by representational similarity analyses. Precision only produced minor attentional effects in performance. Our results, which mirror empirical findings, show that it is possible to adjudicate between attention mechanisms using more biologically realistic models and natural stimuli.

Machine learning approaches to text representation using unlabeled data

Mikaela Keller

With the rapid expansion in the use of computers for producing digitalized textual documents, the need of automatic systems for organizing and retrieving the information contained in large databases has become essential. In general, information retrieval systems rely on a formal description or representation of documents enabling their automatic processing. In the most common representation, the so-called bag-of-words, documents are represented by the words composing them and two documents (or a user query and a document) are considered similar if they have a high number of co-occurring words. In this representation, documents with different, but semantically related terms will be considered as unrelated, and documents using the same terms but in different contexts will be seen as similar. It arises quite naturally that information retrieval systems can use the huge amount of existing textual documents in order to "learn", as humans do, the different uses of words depending on the context. This information can be used to enrich documents' representation. In this thesis dissertation we develop several original machine learning approaches which attempt at fulfilling this aim. As a first approach to document representation we propose a probabilistic model in which documents are assumed to be issued from a mixture of distributions over themes, modeled by a hidden variable conditioning a multinomial distribution over words. Simultaneously, words are assumed to be drawn from a mixture of distributions over topics, modeled by a second hidden variable dependent on the themes. As a second approach, we proposed a neural network which is trained to give a score for the appropriateness of a word in a given context. Finally we present, a multi-task learning approach, which is trained jointly to solve an information retrieval task, while learning on unlabeled data to improve its representation of documents.

EPFL2008

Online processing of multiple inputs in a sparsely-connected recurrent neural network

Wulfram Gerstner, Julien Mayor

The storage and short-term memory capacities of recurrent neural networks of spiking neurons are investigated. We demonstrate that it is possible to process online many superimposed streams of input. This is despite the fact that the stored information is spread throughout the network. We show that simple output structures are powerful enough to extract the diffuse information from the network. The dimensional blow up, which is crucial in kernel methods, is efficiently achieved by the dynamics of the network itself.

Springer2003

Machine Learning Approaches to Text Representation using Unlabeled Data

Mikaela Keller

With the rapid expansion in the use of computers for producing digitalized textual documents, the need of automatic systems for organizing and retrieving the information contained in large databases has become essential. In general, information retrieval systems rely on a formal description or representation of documents enabling their automatic processing. In the most common representation, the so-called bag-of-words, documents are represented by the words composing them and two documents (or a user query and a document) are considered similar if they have a high number of co-occurring words. In this representation, documents with different, but semantically related terms will be considered as unrelated, and documents using the same terms but in different contexts will be seen as similar. It arises quite naturally that information retrieval systems can use the huge amount of existing textual documents in order to ``learn'', as humans do, the different uses of words depending on the context. This information can be used to enrich documents' representation. In this thesis dissertation we develop several original machine learning approaches which attempt at fulfilling this aim. As a first approach to document representation we propose a probabilistic model in which documents are assumed to be issued from a mixture of distributions over themes, modeled by a hidden variable conditioning a multinomial distribution over words. Simultaneously, words are assumed to be drawn from a mixture of distributions over topics, modeled by a second hidden variable dependent on the themes. As a second approach, we proposed a neural network which is trained to give a score for the appropriateness of a word in a given context. Finally we present, a multi-task learning approach, which is trained jointly to solve an information retrieval task, while learning on unlabeled data to improve its representation of documents.

Ecole Polytechnique Fédérale de Lausanne2006

Machine learning approaches to text representation using unlabeled data

Mikaela Keller

EPFL2008

Machine Learning Approaches to Text Representation using Unlabeled Data

Mikaela Keller

With the rapid expansion in the use of computers for producing digitalized textual documents, the need of automatic systems for organizing and retrieving the information contained in large databases has become essential. In general, information retrieval systems rely on a formal description or representation of documents enabling their automatic processing. In the most common representation, the so-called bag-of-words, documents are represented by the words composing them and two documents (or a user query and a document) are considered similar if they have a high number of co-occurring words. In this representation, documents with different, but semantically related terms will be considered as unrelated, and documents using the same terms but in different contexts will be seen as similar. It arises quite naturally that information retrieval systems can use the huge amount of existing textual documents in order to ``learn'', as humans do, the different uses of words depending on the context. This information can be used to enrich documents' representation. In this thesis dissertation we develop several original machine learning approaches which attempt at fulfilling this aim. As a first approach to document representation we propose a probabilistic model in which documents are assumed to be issued from a mixture of distributions over themes, modeled by a hidden variable conditioning a multinomial distribution over words. Simultaneously, words are assumed to be drawn from a mixture of distributions over topics, modeled by a second hidden variable dependent on the themes. As a second approach, we proposed a neural network which is trained to give a score for the appropriateness of a word in a given context. Finally we present, a multi-task learning approach, which is trained jointly to solve an information retrieval task, while learning on unlabeled data to improve its representation of documents.

Ecole Polytechnique Fédérale de Lausanne2006