Title: Executable Symbolic Modeling of Neural Processes
Authors: 
  Sriram M Iyengar (School of HEalth Information Sciences, University of Texas),
  Riccardo Mozzachiodi, Enrico Cataldo, and  Douglas A Baxter 
    (Medical School, Univeristy of Texas, Houston)
 Carolyn Talcott (SRI International)

Abstract: 
Neuroscience is currently experiencing explosive growth in detailed high-quality
experimental information on neural processes underlying learning, memory and
behavior. Consequently there is a need for computational models that can manage
this outpouring of information, derive knowledge from information, and to generate
novel, testable hypotheses. In this paper we describe an application of Pathway
Logic, using the rewriting-logic specification system Maude, to model the behavior
of a neural circuit involved in feeding behavior of a marine mollusk. This
approach, intended to augment existing modeling techniques in neuroscience, has
potential advantages of scalability, and robustness with regard to system
parameters. It yields expressive models capable of simulating known neural circuit
behaviors and performing in silico experiments including knock-outs, 'what-if's and
others.