574 citations · 578 across the 9 of their papers we have counts for
9 papers
Evolutionary Selective Imitation: Interpretable Agents by Imitation Learning Without a Demonstrator
Roy Eliya, J. Michael Herrmann
We propose a new method for training an agent via an evolutionary strategy (ES), in which we iteratively improve a set of samples to imitate: Starting with a random set, in every i…
Statistical models of neural activity, criticality, and Zipf's law
Martino Sorbaro, J. Michael Herrmann, Matthias H. Hennig
In this overview, we discuss the connections between the observations of critical dynamics in neuronal networks and the maximum entropy models that are often used as statistical mo…
The Abelian distribution
Anna Levina, J. Michael Herrmann
We define the Abelian distribution and study its basic properties. Abelian distributions arise in the context of neural modeling and describe the size of neural avalanches in fully…
Critical Parameters in Particle Swarm Optimisation
J. Michael Herrmann, Adam Erskine, Thomas Joyce
Particle swarm optimisation is a metaheuristic algorithm which finds reasonable solutions in a wide range of applied problems if suitable parameters are used. We study the properti…
The success of complex networks at criticality
Victor Hernandez-Urbina, Tom L. Underwood, J. Michael Herrmann
In spiking neural networks an action potential could in principle trigger subsequent spikes in the neighbourhood of the initial neuron. A successful spike is that which trigger sub…
Small-world structure induced by spike-timing-dependent plasticity in networks with critical dynamics
Victor Hernandez-Urbina, J. Michael Herrmann
The small-world property in the context of complex networks implies structural benefits to the processes taking place within a network, such as optimal information transmission and…