NeuroGPU: Accelerating multi-compartment, biophysically detailed neuron simulations on GPUs

Abstract

Background: The membrane potential of individual neurons depends on a large number of interacting biophysical processes operating on spatial-temporal scales spanning several orders of magnitude. The multi-scale nature of these processes dictates that accurate prediction of membrane potentials in specific neurons requires the utilization of detailed simulations. Unfortunately, constraining parameters within biologically detailed neuron models can be difficult, leading to poor model fits. This obstacle can be overcome partially by numerical optimization or detailed exploration of parameter space. However, these processes, which currently rely on central processing unit (CPU) computation, often incur orders of magnitude increases in computing time for marginal improvements in model behavior. As a result, model quality is often compromised to accommodate compute resources.

New method: Here, we present a simulation environment, NeuroGPU, that takes advantage of the inherent parallelized structure of the graphics processing unit (GPU) to accelerate neuronal simulation.

Results & comparison with existing methods: NeuroGPU can simulate most biologically detailed models 10-200 times faster than NEURON simulation running on a single core and 5 times faster than GPU simulators (CoreNEURON). NeuroGPU is designed for model parameter tuning and best performs when the GPU is fully utilized by running multiple (> 100) instances of the same model with different parameters. When using multiple GPUs, NeuroGPU can reach to a speed-up of 800 fold compared to single core simulations, especially when simulating the same model morphology with different parameters. We demonstrate the power of NeuoGPU through large-scale parameter exploration to reveal the response landscape of a neuron. Finally, we accelerate numerical optimization of biophysically detailed neuron models to achieve highly accurate fitting of models to simulation and experimental data.

Conclusions: Thus, NeuroGPU is the fastest available platform that enables rapid simulation of multi-compartment, biophysically detailed neuron models on commonly used computing systems accessible by many scientists.

Keywords: Biophysical simulations; Compartmental models; Conductance-based models; Electrophysiology; Graphical Processing Unit.

Fig. 1.

Optimizing biophysical neuronal models increase with their complexity. Biophysical neuronal model complexity depends on three factors (A–C) which determine computational costs required to simulate and fit models to empirical data (optimization) (D and E). A: Morphological complexity: The abstraction of the neuronal morphology ranges from a point neuron, to a simplified morphology, and ultimately toward different methods to reconstruct neuronal morphology with high resolution. B: Compartmental complexity: Compartmental models vary in complexity both in number of compartments and the content of each compartment. Top: Single compartment with basic, conductances required for spiking and a resting membrane potential. Middle: Multi-compartmental model where ion-channels are aggregated under several conductances e.g. all different voltage gated potassium channels are represented in a slow and fast inactivating channel (Korngreen and Sakmann, 2000). Bottom: High resolution morphology with detailed representation to all channels sub-types in each compartment. C: Single-channel complexity: The overall conductance in compartmental models is formalized either with Hodgkin-Huxley equations or with Markov-based models. When fitting a model to empirical data, several parameters can be varied. Top: only the maximal conductance in a Hodgkin-Huxley formulation. Middle: coefficients added to Hodgkin-Huxley equations (time constants, voltage dependence). Bottom: In Markov-based channels the transition coefficients can be varied. D: Optimizing a model (fitting model to data) depends on the complexity of the model and number of free parameters. When complexity and number of free parameters require compute times that exceed a few days, it becomes impractical to use high resolution models. E: Developing tools to accelerate simulation and optimization time will enable us to use more complicated and biophysical relevant models.

Fig. 2.

NeuroGPU reduces simulation run-time of complex neurons by orders of magnitude without compromising accuracy.NeuroGPU reduces simulation run-time of complex neurons by orders of magnitude without compromising accuracy. A: Morphology of a BBP portal layer 5 neocortical pyramidal cell (Ramaswamy et al., 2015). Dendrite in black, axon in red. B: Top: injected current at the soma. Middle: NEURON voltage response as recorded at the soma. Cyan: NeuroGPU response as recorded at the soma. Bottom: difference in voltage between NEURON and NeuroGPU. C: Top: APs generated per current injection intensity in the soma. Middle, bottom: Peak and average voltage difference between the voltage response in NEURON and NeuroGPU. Red circles denote examples in B. D: Top: Comparing PC model runtimes for the different simulators: black – NEURON, grey MPI-Neuron (32 processors) green – CoreNeuron, blue – NeuroGPU. X-axis in log2 scale, Y-axis in log10 scale. Bottom: Speedup compared to NEURON. Note that the run time for 2¹⁴ neurons using CoreNeuron is extrapolated (see methods). E: Top: Runtime for the model on 1–4 GPUs (Tesla V100) on the same node. Bottom: Speedup compared to NEURON. F–J: Same as A-D, but for the chandelier cell model.

Fig. 3.

NeuroGPU enables rapid exploration of parameter space in complex pyramidal neuron model. A: Each point in the grid represents the number of APs in the relevant model. Points on the axis represent the varied conductances of Na_v and K_v at the axon in the range of [0,10] and [0,20] S/cm², respectively. B: Example voltage responses for chosen models from A. Colors match to the corresponding model location.

Fig. 4.

NeuroGPU accelerates evolutionary optimization for fitting models to neuronal data. A: Voltage traces obtained from optimization (worst case from population of 100: red; best case from population of 10,000: cyan) compared to ground truth (black). B: Optimizations examples using DEAP with different sizes of populations. Four Optimizations with different random starting population over 50 generations. Y axis is the error from the target voltage as described in the methods section. Lower values denote less error from target data. C: Comparing runtimes for optimizations using NeuroGPU and NEURON (linearly extrapolated from 5 generations). Circles are color coded for population size as in A, and represent mean ± SEM. D: Best score in each optimization in A. Circles and error bars as in C.

Fig. 5.

NeuroGPU fits BBP PC model to empirical data. A: Left: morphology of L5 thick-tufted pyramidal neuron from somatosensory cortex (Ramaswamy et al., 2015). Right: NeuroGPU fits (green traces) the L5 PC model to empirical data recorded from an L5 prefrontal cortex pyramidal neuron of a mouse (Black Traces) with different stimulus intensity 200–340pA (Spratt et al., 2019). B: Left: Prefrontal-cortex layer 5 pyramidal neuron morphology (Ascoli et al., 2007; Yin et al., 2018). The morphology of BBP PC model was modified to that of the prefrontal neuron and fitted to the same data as in A. C: Number of APs per 300 ms of step stimulation for different models of layer 5 pyramidal neuron. Note that both revised models overlap target data.