## Lower Bounds for the Computational Power of Networks of Spiking Neurons (1995)

Venue: | Neural Computation |

Citations: | 53 - 11 self |

### BibTeX

@ARTICLE{Maass95lowerbounds,

author = {Wolfgang Maass and Technische Universitaet Graz},

title = {Lower Bounds for the Computational Power of Networks of Spiking Neurons},

journal = {Neural Computation},

year = {1995},

volume = {8},

pages = {1--40}

}

### Years of Citing Articles

### OpenURL

### Abstract

We investigate the computational power of a formal model for networks of spiking neurons. It is shown that simple operations on phasedifferences between spike-trains provide a very powerful computational tool that can in principle be used to carry out highly complex computations on a small network of spiking neurons. We construct networks of spiking neurons that simulate arbitrary threshold circuits, Turing machines, and a certain type of random access machines with real valued inputs. We also show that relatively weak basic assumptions about the response- and threshold-functions of the spiking neurons are sufficient in order to employ them for such computations. 1 Introduction and Basic Definitions There exists substantial evidence that timing phenomena such as temporal differences between spikes and frequencies of oscillating subsystems are integral parts of various information processing mechanisms in biological neural systems (for a survey and references see e.g. Kandel et al., ...