Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices

Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses but also how long it takes to instantiate the network model in computer memory. On the hardware side, acceleration via highly parallel GPUs is being increasingly utilized. On the software side, code generation approaches ensure highly optimized code at the expense of repeated code regeneration and recompilation after modifications to the network model. Aiming for a greater flexibility with respect to iterative model changes, here we propose a new method for creating network connections interactively, dynamically, and directly in GPU memory through a set of commonly used high-level connection rules. We validate the simulation performance with both consumer and data center GPUs on two neuroscientifically relevant models: a cortical microcircuit of about 77,000 leaky-integrate-and-fire neuron models and 300 million static synapses, and a two-population network recurrently connected using a variety of connection rules. With our proposed ad hoc network instantiation, both network construction and simulation times are comparable or shorter than those obtained with other state-of-the-art simulation technologies while still meeting the flexibility demands of explorative network modeling

Thermodynamic properties of the Shastry-Sutherland model from quantum Monte Carlo simulations

We investigate the minus-sign problem that afflicts quantum Monte Carlo (QMC) simulations of frustrated quantum spin systems, focusing on spin S=1/2, two spatial dimensions, and the extended Shastry-Sutherland model. We show that formulating the Hamiltonian in the diagonal dimer basis leads to a sign problem that becomes negligible at low temperatures for small and intermediate values of the ratio of the inter- and intradimer couplings. This is a consequence of the fact that the product state of dimer singlets is the exact ground state both of the extended Shastry-Sutherland model and of a corresponding "sign-problem-free" model, obtained by changing the signs of all positive off-diagonal matrix elements in the dimer basis. By exploiting this insight, we map the sign problem throughout the extended parameter space from the Shastry-Sutherland to the fully frustrated bilayer model and compare it with the phase diagram computed by tensor-network methods. We use QMC to compute with high accuracy the temperature dependence of the magnetic specific heat and susceptibility of the Shastry-Sutherland model for large systems up to a coupling ratio of 0.526(1) and down to zero temperature. For larger coupling ratios, our QMC results assist us in benchmarking the evolution of the thermodynamic properties by systematic comparison with exact diagonalization calculations and interpolated high-temperature series expansions.Comment: 13 pages including 10 figures; published version with minor changes and correction

Phenomenological modeling of diverse and heterogeneous synaptic dynamics at natural density

This chapter sheds light on the synaptic organization of the brain from the perspective of computational neuroscience. It provides an introductory overview on how to account for empirical data in mathematical models, implement them in software, and perform simulations reflecting experiments. This path is demonstrated with respect to four key aspects of synaptic signaling: the connectivity of brain networks, synaptic transmission, synaptic plasticity, and the heterogeneity across synapses. Each step and aspect of the modeling and simulation workflow comes with its own challenges and pitfalls, which are highlighted and addressed in detail.Comment: 35 pages, 3 figure

Modeling spiking networks with neuron-glia interactions in NEST

Recent experimental evidence suggests an active roles of astrocytes in a number of brain functions and demon-strates coordinated neuronal and astrocytic activity in vivo [1]. In the cortex, astrocytes form non-overlappingdomains, each containing several hundreds of neurons and ~100,000 synapses [2]. Astrocytic processes arein close contact with synaptic terminals and affect synaptic transmission, plasticity, and neuronal excitability[3, 4]. Understanding the role of astrocytic mechanisms in brain functions and dysfunctions requires open-access tools for model implementation, simulation, and analysis. In the past decade, hundreds of new modelswith some form of neuron-astrocyte interaction dynamics have been proposed. However, their implementa-tion is rarely shared and not sufficiently documented to reproduce the findings [4, 5]. We developed a newmodule in the NEST simulator that allows efficient implementation and simulation of large neuron-astrocytepopulations. This includes an astrocyte model with internal calcium dynamics, a synapse model to commu-nicate between astrocytes and postsynaptic neurons, and user-friendly and efficient high-level connectivityfunctions, which allow probabilistic or deterministic pairing of neurons and astrocytes. This new module willimprove the convenience, reliability, and reproducibility of computational studies involving neuron-astrocyteinteractions

Computational modeling of neuron-astrocyte interactions in large neural populations using the NEST simulator

Astrocytes,the most abundant glial type in the cortex, interact with neighboring synapses,neurons and glia through complex cellular machinery (Bazargani et al., 2016).Astrocytes form mostly nonoverlapping microdomains, and a single suchmicrodomain can be reached by several hundreds of neurons and as many as~100,000 synapses (Zisis et al., 2021). Experimental studies have demonstratedcoordinated neuronal and astrocytic activity invivo (Lines et al., 2020). Computational methods can help to integratethe data on cellular mechanisms and structural organization of the corticaltissue, and to explore how neuron-astrocyte interactions modulatepopulation-level activity. In the past two decades, the number of publishedcomputational models that include some form of neuron-astrocyte interaction hasbeen steadily increasing (Manninen et al., 2018; Manninen, Aćimović etal., 2018). The majority of the published models was implemented in custom madecode that is often not publicly available. Implementing these models in wellestablished open source simulation tools improves reproducibility of theresults and sharing of the models (Manninen et al., 2018; Manninen, Acimovic etal., 2018). Two earlier efforts to develop open source tools for simulation ofneuronal and glial networks include Arachne (Aleksin et al., 2017), and animplementation in the Brian simulator (Stimberg et al., 2019). We developed a new solution for efficient simulationof large heterogeneous populations of neurons and astrocytes implemented as a module in theNEST simulator (https://www.nest-simulator.org/). We first extended theconcept of a synapse in NEST to include interaction between three compartments,pre- and postsynaptic neurons and the neighboring astrocytic compartment. Next,we developed new method to establish efficiently interactions within a largeheterogeneous cellular population of neurons and astrocytes. Finally, we testedthe new tool by analyzing spontaneous activity regimes in medium-size networkscomposed of several hundreds of cells. In summary, we present a new module for NEST simulator that supports reproducible, open access and efficient development of computational models for large heterogeneous populations of neurons and astrocytes