KInNeSS: A Modular Framework for Computational Neuroscience

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. This paper presents 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 schema, 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 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. 3 IntroductionAdvances in functional, anatomical, and behavioral neuroscience techniques have led to an increase in the data available for modeling complex dynamics of biologically inspired neural networks at many levels of abstraction, from in-depth descriptions and analyses of individual membrane channels to large-scale investigations of whole brain activity. This wealth of data is essential for creating realistic neural models and increases our understanding of animal and human behavior. Furthermore, it has pushed the modeling community towards the design of increasingly complex models, incorporating unprecedented amount of biophysical and anatomical constraints. These large-scale neural models are often non-linear dynamical systems which can be analytically intractable and require numerical simulation to gain insight into their behavior. Emergent properties of large-scale neural networks often remain unnoticed until the whole system is simulated and components are allowed to interact (Cannon et al., 2002).An additional level of complexity is finding a neural simulator and simulation environment that would enable the large variety of researchers from neurophysiology, psychology and computational modeling to share data and work collaboratively (an excellent review can be found in Brette et al., 2007). Most available software packages are specialized in different applications.

Spikes, synchrony, and attentive learning by laminar thalamocortical circuits

“…The minimal numbers of compartments and currents needed to produce the desired network properties is used in each neuron's unbranched cable sections (Rall, 1962). The model implements online spike-timing-dependent plasticity (STDP) learning (Gorchetchnikov et al, 2005a), and the plastic synaptic weights, as well as each neuron's compartmental currents, are recorded to allow off-line local-field potentials (LFP), current-source densities (CSD), and oscillation frequency/synchrony analysis (Versace et al, 2007). Stimuli are horizontally or vertically oriented bars that enable testing of model hypotheses about match/mismatch dynamics and learning.…”

“…4.12 Simulation environment. The model is implemented in KInNeSS (KDE Integrated NeuroSimulation Software, www.kinness.net), a software package that allows the simulation of single neurons with multiple compartments as well as large networks of such elements (Versace et al, 2007). All off-line data analysis is implemented in Matlab (Mathworks Inc.).…”

Running as fast as it can: How spiking dynamics form object groupings in the laminar circuits of visual cortex

“…Mathematical equations and parameters, as well as details pertaining to the simulation protocol, are included in the Appendix. All simulations were conducted using KDE Integrated Neurosimulation software (KInNeSS; Versace et al 2008). …”

“…In particular, Tallon et al (1995) reported a 30Hz component over areas covering visual cortices in response to a Kanizsa triangle stimulus. In order to probe the synchronous dynamics of the network measured at a larger scale, cell activity across the 1D array of layer 2/3 cells obtained during simulations of an illusory contour (8 units wide flankers separated by a 5 units wide gap) was combined into a single estimate of the local field potential (LFP) according to the methodology described in Versace et al (2008). The resulting signal was Fourier transformed (FFT) to obtain the power spectrum displayed in Figure 12.…”

“…Local field potentials are calculated in the KinNeSS software package according to the methodology described in Versace et al (2008). Emplacement of the electrode is determined by choosing a random location within µm of the middle spatial location of the array of pyramidal layer 2/3 cells and aligning the electrode shank with the orientation of the cells.…”