Topic: NEST

Since the popularization of GPUs for machine learning (ML) workloads, several dedicated accelerator chips have emerged, offering architectures optimized for common ML operations and requirements. The types of tasks targeted by such platforms however - often sparse and highly parallel - are not confined to the realm of traditional layer-based learning. Established simulators for large-scale spiking networks with biologically plausible connectivity and synaptic density have historically targeted CPUs, with GPU support and ports being a relatively recent development[1][2]. Here, we present a work

NEST [1] is a well-established open source simulator providing researchers in computational neuroscience with the ability to perform high-performance simulations of spiking neuronal networks. However, as the simulation kernel is written in C++ for performance reasons, this makes it challenging for researchers without a programming background to customize and extend the built-in neuron and synapse models. In order to satisfy both the need for high-performance simulation codes and a good user experience in terms of easy-to-use modeling of neurons and synapses, NESTML [2] was created as a domain-

Microcircuits are the fundamental building blocks of the neocortex [1]. Single instances have been reconstructed experimentally (e.g., [2]), and their general dynamics and information processing capabilities have been investigated theoretically (e.g., [3, 4, 5, 6, 7]). Their architecture is usually represented in connectivity maps consisting of probabilities that neurons establish connections. These maps reduce the complicated circuitry to simple relations between cell types, allowing for efficient instantiations of neural network models in parallel computers [8]. While this approach neglects

The use of electromyography (EMG) for translating muscle activity into precise movements has become a pivotal technique to control myoelectric prosthetic devices, significantly improving the quality of life for amputees. Traditional EMG control of prosthetics relies on techniques that decode forearm muscle activity into discrete and simple gestures. Although robust and reliable, these methods are far from the dexterity of a real hand, and can hinder the users' acceptance. Recent advancements in neural network-based control offer a more intuitive approach, enabling amputees to achieve more natu

Neural simulations using NEST are typically executed by a Python script that configures the simulator kernel, builds the network, and runs the simulation. The result is a series of files containing the simulated network activity, which can then be analyzed to provide insights into the neural activity. Despite the availability of file headers to identify the origin of the outputs, a user analyzing the data must still interpret the findings with respect to the simulation setup, network connectivity, and parameters of the neuronal and synaptic models. This information is not immediately available