The Center for Biomedical Informatics
State University of Campinas, Brazil

Research Abstracts

The description, representation and simulation of complex neural network structures by means of computers is an essential step in the investigation of model systems and inventions in the growing field of biological information processing and neurocomputing. The handcrafting of neural net architectures, however, is a long, tedious, difficult and error-prone process, which can be substituted satisfactorily by the neural network analogue of a computer program or formal symbolic language. Several attempts have been made to develop and apply such languages: P3, Hecht-Nielsen's AXON, and Rochester's ISCON are some recent examples. We present here a new tool for the formal description and simulation of artificial neural tissues in microcomputers. It is a network editor and simulator, called NEUROED, as well as a compiler for NEUROL, a high-level symbolic, structured language which allows the definition of the following elements of a neural tissue: a) elementary neural architectonic units: each unit has the same number of cells and the same internal interconnecting pattern and cell functional parameters; b) elementary cell types: each cell can be defined in terms of its basic functional parameters; synoptic interconnections inside an architectonic unit (axonic delay, weights and signal can be defined for each); a cell can fan out to several others, with the same synoptic properties; c) synaptic interconnections among units; d) cell types and architectonic units can be replicated automatically across neural tissue and interconnected; e) cell types and architectonic units can be named and arranged in hierarchical frames (parameter inheritance). NEUROED's underlying model processing element (PE) is a simplified Hodgkin-Huxley neuron, with RC-model, temporal-summation, passive electrotonic potentials at dendritic level, and a step transfer function with threshold level, a fixed-size, fixed-duration, fixed-form spike, and an absolute refractory period. Inputs Iij (i=1...NI) synapses for j-th neuron are weighted with Wij (i=1...NI), where Wij 0 is defined for a inhibitory synapse, Wij = 0 for an inactive or non-existent synapse and Wij 0 for an excitatory synapse. Outputs Okj (k=1...NO) can have axonic propagation delays Dkj (a delay can be equal to zero). Firing of neurons in a network follows diffusion process, according to propagation delays; random fluctuations in several processes can be simulated. Several learning algorithms can be implemented explicitly with NEUROL; a Hebbian synapse-strength reinforcement rule has specific language support now. NEUROED's basic specifications are: a) written in Turbo BASIC 1.0 for IBM-PC compatible machines, with CGA monochrome graphics display and optional numerical coprocessor; b) capacity of 100 neurons and 10.000 synapses; c) three neural tissue layers: input, processing and output. d) real-time simulation of neural tissue dynamics, with three display modes: oscilloscope mode (displays membrane potentials along time for several cells simultaneously); map mode (displays bidimensional architecture with individual cells, showing when they fire) and Hinton diagram (displays interconnecting matrix with individual synapses, showing when they fire); e) Realtime, interactive modification of net parameters; and f) capability for building procedures, functions and model libraries, which reside as external disk files. NEUROED and NEUROL are easy to learn and to use, intuitive for neuroscientists, and lend themselves to modeling neural tissue dynamics for teaching purposes. We are currently developing a basic "library" of NEUROED models to teach basic neurophysiology to medical students. Implementations of NEUROED and for parallel hardware are also under way.

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Last Updated: March 2, 1996

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