Biomaterial Database

A simple model for neural computation with firing rates and firing correlations. 1998

W Maass

Institute for Theoretical Computer Science, Technische Universität Graz, Austria. maass@igi.tu-graz.ac.at

A simple extension of standard neural network models is introduced which provides a model for neural computations that involve both firing rates and firing correlations. Such an extension appears to be useful since it has been shown that firing correlations play a significant computational role in many biological neural systems. Standard neural network models are only suitable for describing neural computations in terms of firing rates. The resulting extended neural network models are still relatively simple, so that their computational power can be analysed theoretically. We prove rigorous separation results, which show that the use of firing correlations in addition to firing rates can drastically increase the computational power of a neural network. Furthermore, one of our separation results also throws new light on a question that involves just standard neural network models: we prove that the gap between the computational power of high-order and first-order neural nets is substantially larger than shown previously.

UI	MeSH Term	Description	Entries
D009474	Neurons	The basic cellular units of nervous tissue. Each neuron consists of a body, an axon, and dendrites. Their purpose is to receive, conduct, and transmit impulses in the NERVOUS SYSTEM.	Nerve Cells,Cell, Nerve,Cells, Nerve,Nerve Cell,Neuron
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron