Ecole Polytechnique Federale De Lausanne

Idan Segev

Henry Markram

Eilif Benjamin Muller

James Gonzalo King

Christian Rössert

Willem Wybo

Csaba Erö

Wulfram Gerstner

Marc-Oliver Gewaltig

Christian Pozzorini

The simplification of neural network models is described. For example, a method for simplifying a neural network model includes providing the neural network model to be simplified, defining a first temporal filter for the conveyance of input from a neuron to an other spatially-extended neuron along the arborized projection, defining a second temporal filter for the conveyance of input from yet another neuron to the spatially-extended neuron along the arborized projection, replacing, in the neural network model, the first, spatially-extended neuron with a first, spatially-constrained neuron and the arborized projection with a first connection extending between the first, spatially-constrained neuron and the second neuron, wherein the first connection filters input from the second neuron in accordance with the first temporal filter and a second connection extending between the first spatially-constrained neuron and the third neuron.

Simplification of spiking neural network models

Simplification of neural models that include arborized projections

Growing community of inventors

Jerusalem, Israel

Idan Segev