Evolving an optimal de/convolution function for the neural net modules of ATR's artificial brain project

This paper reports on efforts to evolve an optimum de/convo-lution function to be used to convert analog to binary signals (spike trains) and vice versa for the binary input/output signals of the neural net circuit modules evolved at electronic speeds by the so-called "CAM-Brain Machine (CBM)" of ATR’s Artificial Brain Project [1, 2, 3]. The CBM is an FPGA based piece of hardware which will be used to evolve tens of thousands of cellular automata based neural network circuits or modules at electronic speeds in about a second each, which are then downloaded into humanly architected artificial brains in a large RAM space [2, 3]. Since state-of-the-art programmable FPGAs constrained us to use 1 bit binary signaling in our neural model (the "CoDi-1Bit" model [4]), an efficient de/convolution technique is needed to convert digital signals to analog and vice versa, so that "evolutionary engineers" (EEs) who evolve the many modules, can think it terms of analog signals when they need to, rather than in terms of the abstract incomprehensible spike trains (where the information in the signal is contained in the spacing between spikes). An earlier convolution function, digitized from a text book figure [7] (shown in Fig. 1), gave only moderate accuracy between actual and desired output results. By applying a genetic algorithm to the evolution of the de/convolution function we were able to make it nearly twice as accurate. Accuracy is important so as to reduce cumulative errors when the output of one neural net module becomes the input of another, in long sequential chains of modules in artificial brain architectures consisting of 10,000s of modules. The CBM can handle up to 32000 modules (of maximum about 1000 artificial neurons each) and will be delivered to ATR by the spring of 1999.