title: Learning with two sites of synaptic integration.
abstract: Since the classical work of D O Hebb 1949 The Organization of Behaviour (New York: Wiley) it is assumed that synaptic plasticity solely depends on the activity of the pre- and the postsynaptic cells. Synapses influence the plasticity of other synapses exclusively via the post-synaptic activity. This confounds effects on synaptic plasticity and neuronal activation and, thus, makes it difficult to implement networks which optimize global measures of performance. Exploring solutions to this problem, inspired by recent research on the properties of apical dendrites, we examine a network of neurons with two sites of synaptic integration. These communicate in such a way that one set of synapses mainly influences the neurons' activity; the other set gates synaptic plasticity. Analysing the system with a constant set of parameters reveals: (1) the afferents that gate plasticity act as supervisors, individual to every cell. (2) While the neurons acquire specific receptive fields the net activity remains constant for different stimuli. This ensures that all stimuli are represented and, thus, contributes to information maximization. (3) Mechanisms for maximization of coherent information can easily be implemented. Neurons with non-overlapping receptive fields learn to fire correlated and preferentially transmit information that is correlated over space. (4) We demonstrate how a new measure of performance can be implemented: cells learn to represent only the part of the input that is relevant to the processing at higher stages. This criterion is termed 'relevant infomax'.
authors.affiliation: Institute of Neuroinformatics, ETH/UNI Zürich, Switzerland. koerding@ini.phys.ethz.ch
authors.forename: K P
authors.initials: KP
authors.lastname: Körding
authors.forename: P
authors.initials: P
authors.lastname: König
mesh_terms: D002450:Cell Communication / Q000502:physiology; D002540:Cerebral Cortex / Q000166:cytology / Q000502:physiology; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer; D017966:Pyramidal Cells / Q000502:physiology*; D013569:Synapses / Q000502:physiology*