We describe the theoretical formulation of a learning algorithm in a model of the primary visual cortex (V1) and present results of the efficiency of this algorithm by comparing it to the SparseNet algorithm . As the SparseNet algorithm, it is based on a model of signal synthesis as a Linear Generative Model but differs in the efficiency criteria for the representation. This learning algorithm is in fact based on an efficiency criteria based on the Occam razor: for a similar quality, the shortest representation should be privileged. This inverse problem is NP-complete and we propose here a greedy solution which is based on the architecture and nature of neural computations ). It proposes that the supra-threshold neural activity progressively removes redundancies in the representation based on a correlation-based inhibition and provides a dynamical implementation close to the concept of neural assemblies from Hebb ). We present here results of simulation of this network with small natural images (available at http://incm.cnrs-mrs.fr/LaurentPerrinet/SparseHebbianLearning webcite) and compare it to the Sparsenet solution. Extending it to realistic images and to the NEST simulator http://www.nest-initiative.org/ webcite, we show that this learning algorithm based on the properties of neural computations produces adaptive and efficient representations in V1.
This was work supported by the 6th RFP of the EU (grant no. 15879-FACETS). Simulations use the PyNN software available at http://pynn.gforge.inria.fr/.