KInNeSS: A modular framework for computational neuroscience

Massimiliano Versace, Heather Ames, Jasmin Léveillé, Bret Fortenberry, and Anatoli Gorchetchnikov

Department of Cognitive and Neural Systems
and
Center of Excellence for Learning In Education, Science, and Technology
Boston University
677 Beacon Street
Boston, MA 02215
Phone: 617-353-6174
Fax: 617-353-7755
Email:versace@cns.bu.edu

Abstract: Making use of very detailed neurophysiological, anatomical, and behavioral data to build biologically-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. We present KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior.  This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalability, an XML based network description, and multiple levels of granularity within a modern object oriented programming design.  KInNeSS is best suited to simulate networks of hundreds to thousands of branched multi-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions or ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity.  KInNeSS also includes quadratic integrate-and-fire models that can be used to replace Hodgkin-Huxley currents in simulating spiking dynamics. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent.  An explanation of the modeling philosophy and plug-in development is also presented. Further development of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effectively collaborate using a modern neural simulation platform. Supported in part by AFOSR, NSF and ONR.