Experimental data show that biological synapses behave quite differently from the symbolic synapses in common artificial neural network models. Biological synapses are dynamic, i....
We shed light on the key ingredients of reservoir computing and analyze the contribution of the network dynamics to the spatial encoding of inputs. Therefore, we introduce attracto...
In the paper we recover a Hammerstein system nonlinearity. Hammerstein systems, incorporating nonlinearity and dynamics, play an important role in various applications, and e¤ecti...
Zygmunt Hasiewicz, Grzegorz Mzyk, Przemyslaw Sliwi...
— In this paper, we propose a new type of neural adaptive control via dynamic neural networks. For a class of unknown nonlinear systems, a neural identifierFbased feedback linea...
— A single biological neuron is able to perform complex computations that are highly nonlinear in nature, adaptive, and superior to the perceptron model. A neuron is essentially ...