A Brief History of Simulation Neuroscience

Our knowledge of the brain has evolved over millennia in philosophical, experimental and theoretical phases. We suggest that the next phase is simulation neuroscience. The main drivers of simulation neuroscience are big data generated at multiple levels of brain organization and the need to integrate these data to trace the causal chain of interactions within and across all these levels. Simulation neuroscience is currently the only methodology for systematically approaching the multiscale brain. In this review, we attempt to reconstruct the deep historical paths leading to simulation neuroscience, from the first observations of the nerve cell to modern efforts to digitally reconstruct and simulate the brain. Neuroscience began with the identification of the neuron as the fundamental unit of brain structure and function and has evolved towards understanding the role of each cell type in the brain, how brain cells are connected to each other, and how the seemingly infinite networks they form give rise to the vast diversity of brain functions. Neuronal mapping is evolving from subjective descriptions of cell types towards objective classes, subclasses and types. Connectivity mapping is evolving from loose topographic maps between brain regions towards dense anatomical and physiological maps of connections between individual genetically distinct neurons. Functional mapping is evolving from psychological and behavioral stereotypes towards a map of behaviors emerging from structural and functional connectomes. We show how industrialization of neuroscience and the resulting large disconnected datasets are generating demand for integrative neuroscience, how the scale of neuronal and connectivity maps is driving digital atlasing and digital reconstruction to piece together the multiple levels of brain organization, and how the complexity of the interactions between molecules, neurons, microcircuits and brain regions is driving brain simulation to understand the interactions in the multiscale brain.

Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON

Michael Lee Hines, Felix Schürmann

Large multiscale neuronal network simulations are of increasing value as more big data are gathered about brain wiring and organization under the auspices of a current major research initiative, such as Brain Research through Advancing Innovative Neurotechnologies. The development of these models requires new simulation technologies. We describe here the current use of the NEURON simulator with message passing interface (MPI) for simulation in the domain of moderately large networks on commonly available high-performance computers (HPCs). We discuss the basic layout of such simulations, including the methods of simulation setup, the run-time spike-passing paradigm, and postsimulation data storage and data management approaches. Using the Neuroscience Gateway, a portal for computational neuroscience that provides access to large HPCs, we benchmark simulations of neuronal networks of different sizes (500-100,000 cells), and using different numbers of nodes (1-256). We compare three types of networks, composed of either Izhikevich integrate-and-fire neurons (I&F), single-compartment Hodgkin-Huxley (HH) cells, or a hybrid network with half of each. Results show simulation run time increased approximately linearly with network size and decreased almost linearly with the number of nodes. Networks with I&F neurons were faster than HH networks, although differences were small since all tested cells were point neurons with a single compartment.

Mit Press2016

Simple Neuron Models and Adaptation

Skander Mensi

Predicting activity of single neuron is an important part of the computational neuroscience and a great challenge. Several mathematical models exist, from the simple (one compartment and few parameters, like the SRM or the IF-type models), to the more complex (biophysical model like the Hodgkin-Huxley model). All these models have their own advantages and limitations, but no one is able to reproduce the exact behavior of real neurons. Multiple projects try to simulate complex neural networks, or even the whole brain (i.e. the blue brain project or other big network simulation). To achieve this goal it is very important that simple neuron models simulate, for a low computational cost, the precise activities of all neuron classes. It is well-known that neurons exhibit a lot of different activity patterns (from an electro-physiological point of view). Here we have focused on pyramidal neurons from the layer 5 of the neocortex. They are classified as regular spiking-cells. This cell type shows adaptation and like other neurons, refractoriness. Adaptation and refractoriness are very common neuronal activity in the brain, and so it is important to have a simple model which can reproduce this kind of activity. This project deals with two classical simple neuron models: the adaptive exponential integrate-and-fire model (AdEx) and the spike response model (SRM). We deter- mined the parameters of these two models using data generated with a detailed model, the Destexhe's model which is a HH-like model for cortical pyramidal cells, stimulated with different current injection scenarios. In a second time the model parameters have been set using data from in-vitro recordings of 4 layer 5 pyramidal rat neurons, stimulated with a sinusoid in vivo-like protocol, injected somatically in current-clamp configuration. Then we show that this type of model can capture adaptation and can reproduce the activity of neuron with a high reliability

2008

Computational characteristics and hardware implications of brain tissue simulations

Francesco Cremonesi

Understanding the link between the brain's anatomy and its function through computer simulations of neural tissue models is a widely used approach in computational neuroscience. This technique enables rapid prototyping and testing of hypotheses, allowing researchers to bridge the scales of biological phenomena. Until recently, the constant trend of improvement in computational power has supported an exponential growth in the scale and level of detail of in silico experiments. However, a systematic characterization of the performance landscape has not yet been carried out. In this work we intend to capture intrinsic computational properties of the existing mod- elling abstractions and answer questions about the intricate relationship between simulation algorithms and modern hardware architecture. Our first contribution is a novel set of hardware- agnostic metrics that enables us to bring focus to the heterogeneous landscape of brain tissue models. We develop a methodology able to capture subtle differences between cell-based models and quantify their impact on performance based on hardware features. We show that lumping simulation experiments together by referring to numbers of neurons and synapses without further detail hides fundamental differences in computational and hardware require- ments across models. In addition to analysing different neuron representations, we investigate the impact of biological heterogeneity on the performance of a cortical microcircuit model. Our analysis indicates that while general-purpose computers have until now sustained high- performance simulations of all brain tissue models, the next generation of in silico models will require hardware tailored to the underlying abstraction. We find that all formalisms saturate the memory bandwidth with a fairly small number of shared memory threads, but the reasons behind this are quite different: conductance-based models are dominated by the large memory traffic of clock-driven kernels, while current-based models are most affected by event-driven execution and memory latency. In distributed simulations the latency of the interconnect fabric is the root cause for a significant degradation in performance. We argue that performance analyses such as ours are required to enable the next generation of brain tissue simulations - or else scientific progress risks being hindered by the presence of severe hardware bottlenecks. Our methodology provides a common tool to facilitate the communication between modellers, developers and hardware designers in order to sustain the larger memory and performance requirements of future brain tissue simulations.