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 layes 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. Layer 5 pyramids form two distinct groups: corticostriatal cells that project to striatum and to other cortical area, and corticospinal cells that project downwards to brainstem and spinal cord. Our dual-output hypothesis conceptualizes these as partially separable subcircuits anchored by the two massive cell types. We suggest that the one-way projection (corticostriatal to corticospinal) between these subcircuits effects a major code transformation: dominant corticocortical temporal coding (corticostriatal subcircuit) to dominant rate coding (corticospinal subcircuit). Importantly for ths hypothesis, the corticospinal pyramidal cells show linear activation properties with little adaptation.
Our Aims proceed from low to high through sets of tightly-linked experiment and simulation, with predictions leading to experiments leading to modified simulations: 1. Simulate integration of synaptic signals in dendrites, based on densities of Ih and IA measured with cell activation at different dendritic locations (subcellular scale: 1-10?m); 2. Model cell firing based on current clamp experiments (cell scale: 30-800?m); 3. Create circuit-level models based on projection strength measurements (microcircuit scale: 2-5mm); 4. Use information theoretic and dynamical measures to evaluate SPI-STR code transformation hypothesis through input/output analysis (projection scale: 10-100mm). We predict that the neocortical circuit can either combine or multiplex signals, depending on tags based on location, frequency, phase, and amplitude of inputs.
We are developing a detailed computer model of the motor cortex, the circuit of the brain responsible for producing movement, using a combination of computer simulation and experiment to look at scales from the microscopic to the visible. 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.
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| Li, Xiaojian; Yamawaki, Naoki; Barrett, John M et al. (2018) Scaling of Optogenetically Evoked Signaling in a Higher-Order Corticocortical Pathway in the Anesthetized Mouse. Front Syst Neurosci 12:16 |
| Antic, Srdjan D; Hines, Michael; Lytton, William W (2018) Embedded ensemble encoding hypothesis: The role of the ""Prepared"" cell. J Neurosci Res 96:1543-1559 |
| Dura-Bernal, S; Neymotin, S A; Kerr, C C et al. (2017) Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis. IBM J Res Dev 61:6.1-6.14 |
| Neymotin, Samuel A; Talbot, Zoe N; Jung, Jeeyune Q et al. (2017) Tracking recurrence of correlation structure in neuronal recordings. J Neurosci Methods 275:1-9 |
| Neymotin, Samuel A; Suter, Benjamin A; Dura-Bernal, Salvador et al. (2017) Optimizing computer models of corticospinal neurons to replicate in vitro dynamics. J Neurophysiol 117:148-162 |
| Lytton, William W; Arle, Jeff; Bobashev, Georgiy et al. (2017) Multiscale modeling in the clinic: diseases of the brain and nervous system. Brain Inform 4:219-230 |
| McDougal, Robert A; Bulanova, Anna S; Lytton, William W (2016) Reproducibility in Computational Neuroscience Models and Simulations. IEEE Trans Biomed Eng 63:2021-35 |
| Lytton, William W; Seidenstein, Alexandra H; Dura-Bernal, Salvador et al. (2016) Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON. Neural Comput 28:2063-90 |
| Lytton, William W (2016) Computer modeling of epilepsy: opportunities for drug discovery. Drug Discov Today Dis Models 19:27-30 |
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