Topic: Spiking neuronal networks

All-optical techniques play a pivotal role in the direct interrogation of biological neural networks and the investigation of their computational properties. Such techniques combine spatiotemporally precise optogenetic stimulation at cellular resolution to manipulate neuronal states in the network with optical imaging of fluorescent genetically-encoded indicators for simultaneous readout of neuronal activity [1, 2]. The biophysical models of various optogenetic tools have been studied computationally in single neurons, accurately fitting experimental data [3, 4]. However, the effects of optoge

Artificial neural networks (ANNs) achieve remarkable results on various tasks, but understanding the computational mechanisms underlying their performance remains difficult. Furthermore, traditionally employed artificial networks have little in common with real biological networks. Overcoming this difference, machine-learning-based training methods for spiking neuronal networks (SNNs) have been developed to create functional networks, enabling the investigation of these neural networks with neuroscientific analysis methods. Here we analyze one such SNN trained with backpropagation based on a

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-