title: Online unsupervised formation of cell assemblies for the encoding of multiple cognitive maps.
abstract: Since their introduction sixty years ago, cell assemblies have proved to be a powerful paradigm for brain information processing. After their introduction in artificial intelligence, cell assemblies became commonly used in computational neuroscience as a neural substrate for content addressable memories. However, the mechanisms underlying their formation are poorly understood and, so far, there is no biologically plausible algorithms which can explain how external stimuli can be online stored in cell assemblies. We addressed this question in a previous paper [Salihoglu, U., Bersini, H., Yamaguchi, Y., Molter, C., (2009). A model for the cognitive map formation: Application of the retroaxonal theory. In Proc. IEEE international joint conference on neural networks], were, based on biologically plausible mechanisms, a novel unsupervised algorithm for online cell assemblies' creation was developed. The procedure involved simultaneously, a fast Hebbian/anti-Hebbian learning of the network's recurrent connections for the creation of new cell assemblies, and a slower feedback signal which stabilized the cell assemblies by learning the feedforward input connections. Here, we first quantify the role played by the retroaxonal feedback mechanism. Then, we show how multiple cognitive maps, composed by a set of orthogonal input stimuli, can be encoded in the network. As a result, when facing a previously learned input, the system is able to retrieve the cognitive map it belongs to. As a consequence, ambiguous inputs which could belong to multiple cognitive maps can be disambiguated by the knowledge of the context, i.e. the cognitive map.
authors.affiliation: IRIDIA-CoDE, Universite Libre de Bruxelles, 50, Av. F. Roosevelt, CP 194/6 1050 Brussels, Belgium. usalihog@iridia.ulb.ac.be
authors.forename: Utku
authors.initials: U
authors.lastname: Salihoglu
authors.forename: Hugues
authors.initials: H
authors.lastname: Bersini
authors.forename: Yoko
authors.initials: Y
authors.lastname: Yamaguchi
authors.forename: Colin
authors.initials: C
authors.lastname: Molter
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D005246:Feedback; D007858:Learning / Q000502:physiology*; D008570:Memory, Short-Term / Q000502:physiology; D011939:Mental Recall / Q000502:physiology; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology