Modelling and Simulation of a Neuron and a Neuron Network Using a
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Modelling and Simulation of a Neuron and a Neuron Network using a Visual HDL Kate R Worland, John D Zakis and Brian J Lithgow ABSTRACT This work used the Summit Visual HDL for VHDL – a Visual Hardware Description package which com- piles flowcharts, state diagrams, truth tables etc into synthesisable VHDL (VHISIC Hardware Description Language). We used this design environment to design and simulate a digital mode l of a single neuron. This neuron models the superficial input/output characteristics of a biological neuron, producing an ac- tion-potential-like waveform when inputs to the cell sum to reach a pre-determined threshold. It was shown that by connecting a number of these digital neuron models in a network configuration with ap- propriate input weighting, it was possible to emulate the function of a simple feed-forward neural net- work. This finding is important, because prev ious work has suggested that artificial neurones that are more biological in their function have the potential to form neuron networks that are more robust and noise-tolerant than conventional artificial neural networks. INTRODUCTION This project aimed to produce a model of a neuron ultimately implementable in silicon. The motiva- tion for this is twofold. Firstly, a neuron chip has the potential to provide a more realistic and faster method for simulating the physiological properties of neurones than traditional modelling methods. Secondly, the development of such a model could be a useful new development in the field of artifi- cial neural networks and neural computi ng. Thus, we need to have substantial grounding in two very broad areas, both of which are detailed and in- depth disciplines in their own right. The first is the field of neural modelling – that is the quest to pro- duce an accurate and useful model of a single neu- ron or a group of neurones in order to further un- derstand th e working of the nervous system. The second is the field of Artificial Neural Networks (ANNs) which are used in computation, often in research into artificial intelligenc e. The style ofcomputing is based not on a sequential, algorithmic paradigm, but rather on a dynamic network of units that can be trained (more like a physiological sys- tem of neurones). THE BIOLOGICAL NEURON Figure 1 The neuron as depicted in Figure 1, receives inputs from other cells at the dendrites in the form of exci- tatory or inhibitory post-synaptic potentials. These small, transient membrane depolarisations propagate passively to the cell body, where if they sum to reach threshold, an action potential as shown in Figure 2, is generated [1]. This all-or-nothing electrical pulse is conducted at constant speed and amplitude down the axon, to the terminal endings (synapses) that pass the signal to the target cells. Figure 2. Action Potential example. A typical action potential waveform recorded at a particular point on the axon over time. The initial membrane voltage of –70mV is the resting membrane potential. Excitation c auses the membran e potential to reach threshold of –55mV, resulting in sudden, massive depolarisation of the cell. Rapid repolarisa- tion and a period of hyperpolarisaion follow this, where the membrane voltage is actually more nega- tive than resting membrane potential. Over a period of a few milliseconds, membrane voltage returns to its resting level. Such a pulse propagates along t he axon, each depolarisation causing the neighbouring
