## Abstract

In this paper we discuss how an analog signal can be encoded using biophysically realistic neural networks. Such a network differs from a standard artificial neural network because of the fact that a biological cell generates spikes and information is encoded as activity of this spike generator and transmitted through a synapse between two cells. Thus a Biological Neural Network is a dynamic ensemble of cells that interact perhaps to approximate a function, perform a recursive computation such as solving a differential equation, or retain a variable in its memory. The interaction between the cells is controlled by choosing a set of synaptic weights that have to be optimized in order that a portion of the network encode a suitable function. A new optimization algorithm for finding a set of optimal synaptic weights has been proposed and successfully implemented using a software program called GENESIS. The algorithm is illustrated by implementing a memory which is a simple network of cells encoding the identify function, together with a unity feedback.

Original language | English |
---|---|

Pages (from-to) | 257-262 |

Number of pages | 6 |

Journal | Proceedings of the American Control Conference |

Volume | 1 |

DOIs | |

State | Published - 2001 |

Event | 2001 American Control Conference - Arlington, VA, United States Duration: Jun 25 2001 → Jun 27 2001 |