Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons

Abstract

Understanding the brain requires studying its multiscale interactions from molecules to networks. The increasing availability of large-scale datasets detailing brain circuit composition, connectivity, and activity is transforming neuroscience. However, integrating and interpreting this data remains challenging. Concurrently, advances in supercomputing and sophisticated modeling tools now enable the development of highly detailed, large-scale biophysical circuit models. These mechanistic multiscale models offer a method to systematically integrate experimental data, facilitating investigations into brain structure, function, and disease. This review, based on a Society for Neuroscience 2024 MiniSymposium, aims to disseminate recent advances in large-scale mechanistic modeling to the broader community. It highlights (1) examples of current models for various brain regions developed through experimental data integration; (2) their predictive capabilities regarding cellular and circuit mechanisms underlying experimental recordings (e.g., membrane voltage, spikes, local-field potential, electroencephalography/magnetoencephalography) and brain function; and (3) their use in simulating biomarkers for brain diseases like epilepsy, depression, schizophrenia, and Parkinson's, aiding in understanding their biophysical underpinnings and developing novel treatments. The review showcases state-of-the-art models covering hippocampus, somatosensory, visual, motor, auditory cortical, and thalamic circuits across species. These models predict neural activity at multiple scales and provide insights into the biophysical mechanisms underlying sensation, motor behavior, brain signals, neural coding, disease, pharmacological interventions, and neural stimulation. Collaboration with experimental neuroscientists and clinicians is essential for the development and validation of these models, particularly as datasets grow. Hence, this review aims to foster interest in detailed brain circuit models, leading to cross-disciplinary collaborations that accelerate brain research.

Keywords: biophysically detailed models; brain circuits; mechanistic models; modeling; morphologically detailed neurons; multi-compartmental neuron models; network models; simulations.

Figure 1.

Overview of large-scale mechanistic brain circuit models showcased in this review. Clockwise from top-left: 3D spatial representation of neural circuit models of mouse primary visual cortex (V1) column (Billeh et al., 2020); rat full hippocampus CA1 (Gandolfi et al., 2022, 2023; Romani et al., 2024); human cortex Layer 2/3 (Guet-McCreight et al., 2024); rat (Halgren et al., 2023) and human (Marsh et al., 2024) cortical column; macaque primary auditory cortex (A1) column (Dura-Bernal et al., 2023a); full rat nonbarrel somatosensory cortex S1 (Reimann et al., 2024); and mouse primary motor cortex (M1) column (Dura-Bernal et al., 2023b). See details in the main text.

Figure 2.

Example simulation outputs of large-scale mechanistic brain circuit models showcased in this review. A, CA3 theta (8 Hz) oscillatory input entrains CA1 to matched theta oscillation across different scales of circuit. Top-left, Schema showing the in silico experimental setup. Top-right and bottom, Full circuit model LFP recordings from stratum PY neurons and corresponding spectrogram (Romani et al., 2024). B, Layer-wise population responses to single whisker deflection closely match in vivo millisecond dynamics and response amplitudes. Top, Spiking activity for each layer-wise E and I population for a 2.5 s section of the 10 whisker deflection test protocol. Bottom, Spatiotemporal evolution of the trial-averaged stimulus response in flat space (Isbister et al., 2024). C, Neural response in the V1 mouse model to a drifting grating stimulus. Top, The raster plot of neural activity, with neurons grouped by layer and type. Bottom, The firing rate at preferred direction of the grating, by population, compared with the biophysical model, point-neuron (GLIF) model, and experimental in vivo recordings (Billeh et al., 2020). D, M1 cell type- and layer-specific firing dynamics during quiet wakefulness and movement. Left, The raster plot of activity transitioning from quiet to movement; spike count histogram for excitatory populations; and an example model (blue) and experiment (black) PT5B somatic membrane voltage. Top-right, M1 simulated L5 LFP signals during quiet and movement. Bottom-right, Neural manifold (UMAP low-dimensional representation) of the 10 ms binned mean firing rates of the 16 populations during quiet and movement (Dura-Bernal et al., 2023b). E, Left, Simulated EEG from human cortical microcircuits in health and depression (major depressive disorder) and under application of alpha5-PAM pharmacology for depression. Top-right, Power spectral density of simulated EEG in the different conditions. Bottom-right, Simulated alpha (8–12 Hz) power in health and depression and under different doses of the pharmacology (Guet-McCreight et al., 2024). F, Top and bottom-left, Probability of spiking as a function of horizontal distance from the center of the electrode array for each cell type and cortical layer in the rat cortical column. Average (solid line) cell spiking probability and 95% confidence intervals (shaded region) for each cell reconstruction were calculated for soma locations across the entire X-Z plane of the corresponding cortical layer. Activation probabilities were calculated for 150 mA anodal and 75 mA cathodal stimulation currents over a 200 ms stimulation period. Bottom-right, A raster plot displaying network behavior during and after stimulation in one trial of the microcircuit simulation across all rat cortical columns at maximum applied current. Each cell within the microcircuit has its own coordinate on the y axis. Each dot is an action potential. Green dots indicate spikes that are directly triggered by electrical stimulation (occurs during first 5 ms). Blue dots indicate spikes triggered via synaptic input (Halgren et al., 2023).