Simulations, models and simplicity

Simplicity and Efficiency of Integrate-and-Fire Neuron Models

Lovelace and Cios (2008) recently proposed a very simple spiking neuron (VSSN) model for simulations of large neuronal networks as an efficient replacement for the integrate-and-fire neuron model. We argue that the VSSN model falls behind key advances in neuronal network modeling over the past 20 years, in particular, techniques that permit simulators to compute the state of the neuron without ...

Simplicity and closure constraints in spin foam models of gravity

We revise imposition of various constraints in spin foam models of 4-dimensional general relativity. We argue that the usual simplicity constraint must be supplemented by a constraint on holonomies and together they must be inserted explicitly into the discretized path integral. At the same time, the closure constraint must be relaxed so that the new constraint expresses covariance of intertwin...

Further thoughts on simplicity and complexity in population projection models.

"This article is a review of--and response to--a special issue of Mathematical Population Studies that focused on the relative performance of simpler vs. more complex population projection models. I do not attempt to summarize or comment on each of the articles in the special issue, but rather present an additional perspective on several points: definitions of simplicity and complexity, empiric...

Models, Simulations, and Experiments

I discuss the difference between models, simulations, and experiments from an epistemological and an ontological perspective. I first distinguish between ‘static’ models (like a map) and ‘dynamic’ models endowed with the capacity to generate processes. Only the latter can be used to simulate. I then criticise the view according to which the difference between models/simulations and experiments ...

Environmental Models and Simulations