Multiscale modeling of cortical microcircuits S. Dura-Bernal1, C. Fox2, M. de Kemps3, S. A. Neymotin1, C. C. Kerr1, J. T. Francis1, G. M. G. Shepherd4, W. W. Lytton1

1 SUNY Downstate, Brooklyn, USA; 2 University of Sheffield, UK; 3University of Leeds, UK; 4Northwestern University, Chicago, USA

10-5 m

10-4 s

10-4 m

10-3 s

10-3 m

10-2 s

10-2 m

10-1 s

10-1 m

100 s

100 m

101 s

Muscle excitation

Proprioceptive Information

muscle lengths

Propioceptive (P) layer

Motor (M) layer

Sensory (S) layer

Shoulder flexors: Pectoralis Major, Anterior Deltoid

Elbow flexors: Biceps, Brachialis

Elbow extensor: Triceps Shoulder extensors:

Posterior Deltoid, Infraspinatus

WAMTM Arm User’s Manual

Document: D1001 Version: AH.00

Joint angles

WAM robot arm mirrors motion of

musculoskeletal arm in real time

Reinforcement Learning Critic

(error evaluation)

joint angles

Reward/punisher

global signal

Experimentally-derived connectivity of model of

M1 microcircuitry

Network effects: persistent activity in layer 5  

Intracellular dynamics: calcium interactions support persistent activity associated with working memory.

Reaction-diffusion model of CA wave (sub-cellular)

We develop empirically-derived models to link the different spatial and temporal scale in the brain. These include reaction-diffusion models of molecular dynamics, hybrid networks with multi-compartment and event-driven neurons, neural field models, and high-level probabilistic models.  

Below: Simulated relations of molecular, cellular, and network dynamics in a model of prefrontal cortex.  

Below: Gordon Shepherd’s lab applies multiple tools of quantitative synaptic circuit analysis to elucidate the functional “wiring diagrams” of neocortical neurons.

Below: The spiking neuron model of sensory cortex developed by the Lytton lab is able to accurately reproduce physiological properties observed in vivo, including firing rates, local field potentials, oscillations, traveling waves, and learning of tactile stimulus fields. The model has also been integrated with a neural field model of the basal ganglia to model the effects of Parkinson’s disease.

Below and left: An HMAX-like cortical model of object perception based on hierarchical Bayesian networks and belief propagation. This model includes feedforward selectivity and invariance and top-down feedback.  

Below: A restricted Boltzmann machine model of cortical and hippocampal circuits.  

Below: A hybrid network model (multicompartment and event-driven cells) based on detailed cortical microcircuitry of mouse M1 that reproduces experimental firing rates, physiological oscillations, and coincidence-detection.  

Morphologically realistic

multicompartment cell model of M1 layer 5

Model-generated raster plot reproducing physiological properties of M1

Cross-frequency coupling from LFP of rat (prefrontal cortex): experiment (left) vs. simulation (right)

Below: A spiking network model of neocortex that reproduces physiological measures at different scales: cell voltages, raster plots, LFPs, and cross-frequency coupling.  

Neocortex model connectivity diagram

Below: A “neural blackboard” architecture for combinatorial structures in cognition: models of visual cortex, posterior parietal cortex, and prefrontal cortex solves the binding problem through feedforward and feedback interactions.  

Right: A spiking neural network model of sensorimotor cortex, with STDP and reinforcement learning, learns to control virtual musculoskeletal arm (and robot arm) to perform center-out reaching task. This model links cellular, network and behavioral levels.