Microconnectomics of primary motor cortex: a multiscale computer model

Summary:  We will develop a multi-scale model of primary motor cortex (area M1) based on a rich experimental dataset obtained in ongoing studies. The model will range from the level of ion channels in dendrites, up to the level of the inputs from and outputs to other areas of cortex, a range of microns to centimeters, with a temporal range of milliseconds to 10 sec. We will evaluate dynamical interactions across scale, made more complicated by a structure that features long apical dendrites of Layer 5 pyramidal cells that reach across layers of cortex and thereby across scales. This feature produces complex structure-function relations: apical dendrites directly process inputs from different cortical layers for export from the local microcircuit (direct input/output). They also act within the scale hierarchy, forming a component of the local network which provides a parallel processing of inputs to produce outputs via the entire Layer 5 pyramidal cell ensemble. The model will help us better understand a variety of diseases, including autism and Parkinson’s disease. In addition, the model will assist us in understanding the codes of the brain, which will allow us to later develop more sophisticated prosthetic limbs for the wounded: prosthetics that not only move, but also feel.


Start Date:  09/15/2014

End Date:  05/31/2018

Funding Source:  NIH NIBIB (U01 award)

Principal Investigator(s):  William W. Lytton (SUNY Downstate)

Co-Investigator(s): Gordon M.G. Shepeherd (Northwestern University)

Contact(s):  William W. Lytton (



Extension of NEURON simulator for simulation of reaction-diffusion in neurons

Summary: Multiscale modeling using computer simulation is increasingly recognized as a major method, along with data-mining, for assimilating the vast and ever-growing knowledge base in systems biology. This will improve understanding of the links between molecules and disease manifestation for translational research to the clinic. The bridging of chemophysiology (chemical signaling in neurons and astrocytes) with electrophysiology provides a fundamental connection that will necessarily underpin higher organizational scales. Multiscale models are particularly difficult to simulate in neurobiology due to the elongated nature of neural cells (compared to compact cells for many other cell types), and to multiple overlapping of embedded scales (eg pyramidal apical dendrite domains at the same temporal and spatial scale as local networks).

We are developing the widely used NEURON simulator to accommodate simulation of these complex second-messenger signal interactions that contribute to information processing. In the prior funding period, we added the reaction-diffusion module to NEURON, providing 3D deterministic diffusion linked to reactions situated in cytosol, on or within internal organelles, or on plasma membrane. We also added 1D deterministic diffusion to reduce high computational loads that limited the scope of simulations, noting that the detail of full 3D diffusion is not always needed. As part of these improvements, we extended NEURON's Python interface to include a new set of classes devoted to reaction-diffusion modeling. Additionally, we prepared connectors for interfacing with SBML (Systems Biology Markup Language).

In the current proposal, we will build on these advances in order to allow development of "mosaic" simulations involving combinations of stochastic and deterministic simulation in both 3D and 1D. This will involve the ability to readily switch among these different levels of approximation so that different modeling approaches can be compared. These objectives will be achieved through the following Specific Aims: Aim 1. Multiple multigrid methods: 1D and 3D grids with different sized grids at different locations. Aim 2. Parallelization using multisplit methods that allow the simulation of a single neuron to be run across multiple processors or across multiple threads on a single processor. Aim 3. Stochastic simulation using an extended Gillespie method. This will complement additional stochastic methods that will also be made available in NEURON. Aim 4. Dissemination: new Graphical User Interface for front-end specifications for viewing results, model development, model importation and merging, method comparison and multiprocessor deployment. Making the tool accessible to the community via courses, tutorials, example programs, documentation and online help.


Start Date:  06/01/2016 (continuation of R01 MH086638 which originally started in 06/01/2010)

End Date:  02/28/2021

Funding Source:  NIMH R01

Principal Investigator(s):  William W. Lytton (SUNY Downstate), Robert A. McDougal (Yale University)


Contact(s):  William W. Lytton (






Creating the synthetic brain through hybrid computational and biological systems: repairing and replacing neural networks (REPAIR)

Summary:  The purpose of this effort is to fuse computational and biological principles to develop realistic computational models of the sensorimotor system, which can be used in a silico/biological coadaptive symbiotic system of rehabilitation. This effort shall focus on the creation of new technologies for use in neuroprosthetic rehabilitative solutions and the development of a test bed which that can be used to further expand integrative medical devices for repair and enhancement of biological systems.

To achieve this goal this effort shall utilize reinforcement learning (RL) as a means to link in vivo physiology with computational modeling. The RL paradigm shall be analyzed in multiple time scales in a co-adaptive symbiotic system interfacing with both in vivo neural decoders and a modeled in silico brain (ISB). These systems will be investigated to facilitate consolidation of sensorimotor programs, to analyze how perturbations of multiscale neural signals affect performance, and to describe how they allow for the compensation of deficits in the real brain and vice versa. In addition, co-adaptive interfaces shall also be considered to determine interactions that influence the net benefits of behavioral, computational, and physiological strategies between the user and models. This effort shall also employ optimized spatiotemporal microstimulation (OSTMS) to determine how neural networks process information in a manner directly applicable to simulations; the use of pharmacological agents to erase long term potentiation (LTP) to allow for realistic assessment of model and signal compensation ability; and the development of a Cyber-Workstation (CW) to study complex interactions between large-scale brain subsystems during behavioral experiments.The ultimate goal of this effort is to develop a transformative experimental and computational approach which is capable of translating a user’s intention into commands to control machines and facilitating the fusion of artificial neural substrates into biologic systems which have the capability of adaptation in a rich real-time environment for dexterous tasks.  


Start Date:  03/01/2010

End Date:  05/31/2015

Funding Source:  DOD DARPA

Principal Investigator(s):  Joseph T. Francis (SUNY Downstate), William W. Lytton (SUNY Downstate)

Co-Investigator(s): Jose C. Principe (University of Florida), Jose AB Fortes (University of Florida), Jose Carmena (Berkeley University), Justin C. Sanchez (University of Miami)

Contact(s):  William W. Lytton (






Multiscale Modeling of Neurological and Psychiatric Diseases

Summary: Much of our modeling has looked at a variety of diseases, most prominently epilepsy and schizophrenia, but also Alzheimer's disease, Parkinson's disease, and stroke.  For example, our recent studies of schizophrenia have emphasized the need for multiple scale analysis for evaluating the connections between molecular and synaptic alterations at the lower scales with the transmission of information leading to disruptions of thought at the higher scales.  


Funding Sources:  NIH including NINDS, NIMH, NIA; Veteran's Administration.

Start Date:  1992

End Date:  ongoing

Principal Investigator(s):  William W. Lytton (SUNY Downstate)

Contact(s):  William W. Lytton (