A new class of evolutionary computation processes is presented, called Learnable Evolution
Model or LEM. In contrast to Darwinian-type evolution that relies on mutation, recombination …

Hardware implementations of Spiking Neural Networks are numerous because they are well
suited for implementation in digital and analog hardware, and outperform classic neural …

Complexity is commonly summarized as 'the actions of the whole are more than the sum of
the actions of the parts'. Understanding how the coherence emerges from these natural and …

This article introduces the “CAM-Brain Machine”(CBM), an FPGA-based piece of hardware
that implements a genetic algorithm (GA) to evolve a cellular automata (CA)-based neural …

Two-dimensional Ising lattices are simulated on a field programmable gate array (FPGA)
based system. Multiple spins are updated at each FPGA clock, leading to a linear increase …

Spike encoding is the initial yet crucial step for any application domain of Artificial Spiking
Neural Networks (ASNN). However, current encoding methods are not suitable to process …

Previous work on learning regular languages from exemplary training sequences showed
that Long Short-Term Memory (LSTM) outperforms traditional recurrent neural networks …

This article introduces the “CAM-Brain Machine”(CBM), an FPGA based piece of hardware
which implements a genetic algorithm (GA) to evolve a cellular automata (CA) based neural …

This article introduces ATR's “CAM-Brain Machine”(CBM), an FPGA based piece of
hardware which implements a genetic algorithm (GA) to evolve a cellular automata (CA) …

This paper reports on efforts to evolve an optimum de/convolution function to be used to
convert analog to binary signals (spike trains) and vice versa for the binary input/output …