Inait Sa

Ecole Polytechnique Federale De Lausanne

Felix Schürmann

Henry Markram

John Rahmon

Daniel Milan Lütgehetmann

Fabien Jonathan Delalondre

Kathryn Pamela Hess Bellwald

Constantin Cosmin Atanasoaei

Ran Levi

Michele De Gruttola

Dimitri Zaganidis

Eilif Benjamin Muller

James Gonzalo King

Sean Lewis Hill

Michael Wolfgang Reimann

Nicolas Alain Berger

A method for characterizing correctness of image processing includes receiving images of an instance of an object, wherein the images were acquired at different relative poses, identifying positions of corresponding landmarks on the object in each of the received images, receiving information characterizing a difference in position or a difference in orientation of at least one of the instance of the object and one or more imaging devices when the images were acquired, transferring the positions of landmarks identified in a first of the images based on the difference in position or the difference in orientation, and comparing the positions of the transferred landmarks with the positions of the corresponding of the landmarks identified in a second of the received images, and characterizing a correctness of the identification of the positions of the landmarks in at least one of the received images.

Characterizing and improving of image processing

Abstracting data that originates from different sensors and transducers using artificial neural networks. A method can include identifying topological patterns of activity in a recurrent artificial neural network and outputting a collection of digits. The topological patterns are responsive to an input, into the recurrent artificial neural network, of first data originating from a first sensor and second data originating from a second sensor. Each topological pattern abstracts a characteristic shared by the first data and the second data. The first and second sensors sense different data. Each digit represents whether one of the topological patterns of activity has been identified in the artificial neural network.

Input into a neural network

Distance metrics and clustering in recurrent neural networks. For example, a method includes determining whether topological patterns of activity in a collection of topological patterns occur in a recurrent artificial neural network in response to input of first data into the recurrent artificial neural network, and determining a distance between the first data and either second data or a reference based on the topological patterns of activity that are determined to occur in response to the input of the first data.

Distance metrics and clustering in recurrent neural networks

Application of the output from a recurrent artificial neural network to a variety of different applications. A method can include identifying topological patterns of activity in a recurrent artificial neural network, outputting a collection of digits, and inputting a first digit of the collection to a first application that is designed to fulfil a first purpose and to a second application that is designed to fulfil a second purpose, wherein the first purpose differs from the second purpose. The topological patterns are responsive to an input of data into the recurrent artificial neural network and each topological pattern abstracts a characteristic of the input data. Each digit represents whether one of the topological patterns of activity has been identified in the artificial neural network.

Output from a recurrent neural network

A method includes defining a plurality of different windows of time in a recurrent artificial neural network, wherein each of the different windows has different durations, has different start times, or has both different durations and different start times, identifying occurrences of topological patterns of activity in the recurrent artificial neural network in the different windows of time, comparing the occurrences of the topological patterns of activity in the different windows, and classifying, based on a result of the comparison, a first decision that is represented by a first topological pattern of activity that occurs in a first of the windows as less robust than a second decision that is represented by a second topological pattern of activity that occurs in a second of the windows.

Interpreting and improving the processing results of recurrent neural networks

Computer-implemented methods, software, and systems for determining functional synapses from given structural touches between cells in a neuronal circuit are described. One computer-implemented method for determining functional synapses from predetermined synapses of connections between two cells in a neuronal circuit, includes determining, from the predetermined synapses, the functional synapses by leaving a portion of the connections unused, e.g. for activation by plasticity mechanisms.

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Lausanne, Switzerland

Felix Schürmann