The Neuromorphics Lab is currently working on two main software project: the Neural Algebra project, and KinnNeSS. Both project are aimed at facilitating the development of large-scale neural simulations and their deployment in low-power, high-density neural chips to be embedded in virtual and robotic agents.

Neural Algebra

neural algebra One of the goal of the Lab is to develop a high level neural model development framework that supports design and implementation of neural models targeted for the CogExMachina OS (Cog), developed by HP in collaboration with our Lab for the DARPA SyNAPSE project. The framework would support creation, connection, interaction, and customization of modules based on Cog primitives. The framework would support an algebraic syntax for modules communications allowing a neural model to be described in algebraic statements.The framework will

provide a toolbox supporting a graphic design of neural models that use a predefined set of modeling blocks and adaptive synaptic connections with the ability to set properties of model's elements.


KInNeSS, the KDE Integrated NeuroSimulation Software, has been developed by the Lab member Anatoli Gorchetchnikov. This neurosimulation software is unique in that it allows to tightly link sophisticated model development with behavior.

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 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. 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.

Go to the KInNeSS site.


KInNeSS: A modular framework for computational neuroscience. Massimiliano Versace, Heather Ames, Jasmin Leveille, Bret Fortenberry, Himanshu Mhatre, and Anatoli Gorchetchnikov. Neuroinformatics, In press. Download the PDF version. [2007]