Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub.

To build biophysically detailed models of brain cells, circuits, and regions, a data-driven approach is increasingly being adopted. This helps to obtain a simulated activity that reproduces the experimentally recorded neural dynamics as faithfully as possible, and to turn the model into a useful framework for making predictions based on the principles governing the nature of neural cells. In such a context, the access to existing neural models and data outstandingly facilitates the work of computational neuroscientists and fosters its novelty, as the scientific community grows wider and neural models progressively increase in type, size, and number.

The EBRAINS Hodgkin-Huxley Neuron Builder: An online resource for building data-driven neuron models.

In the last decades, brain modeling has been established as a fundamental tool for understanding neural mechanisms and information processing in individual cells and circuits at different scales of observation. Building data-driven brain models requires the availability of experimental data and analysis tools as well as neural simulation environments and, often, large scale computing facilities. All these components are rarely found in a comprehensive framework and usually require programming.

EBRAINS Live Papers - Interactive Resource Sheets for Computational Studies in Neuroscience.

We present here an online platform for sharing resources underlying publications in neuroscience. It enables authors to easily upload and distribute digital resources, such as data, code, and notebooks, in a structured and systematic way. Interactivity is a prominent feature of the Live Papers, with features to download, visualise or simulate data, models and results presented in the corresponding publications.

The EBRAINS NeuroFeatureExtract: An Online Resource for the Extraction of Neural Activity Features From Electrophysiological Data.

The description of neural dynamics, in terms of precise characterizations of action potential timings and shape and voltage related measures, is fundamental for a deeper understanding of the neural code and its information content. Not only such measures serve the scientific questions posed by experimentalists but are increasingly being used by computational neuroscientists for the construction of biophysically detailed data-driven models. Nonetheless, online resources enabling users to perform such feature extraction operation are lacking.

HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data.

Anatomically and biophysically detailed data-driven neuronal models have become widely used tools for understanding and predicting the behavior and function of neurons. Due to the increasing availability of experimental data from anatomical and electrophysiological measurements as well as the growing number of computational and software tools that enable accurate neuronal modeling, there are now a large number of different models of many cell types available in the literature. These models were usually built to capture a few important or interesting properties of the given neuron type, and it is often unknown how they would behave outside their original context.

Age-related preference for geometric spatial cues during real-world navigation.

Ageing effects on spatial navigation are characterized mainly in terms of impaired allocentric strategies. However, an alternative hypothesis is that navigation difficulties in aged people are associated with deficits in processing and encoding spatial cues. We tested this hypothesis by studying how geometry and landmark cues control navigation in young and older adults in a real, ecological environment.

The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow.

Every neuron is part of a network, exerting its function by transforming multiple spatiotemporal synaptic input patterns into a single spiking output. This function is specified by the particular shape and passive electrical properties of the neuronal membrane, and the composition and spatial distribution of ion channels across its processes. For a variety of physiological or pathological reasons, the intrinsic input/output function may change during a neuron's lifetime.

Modulation of network activity and induction of homeostatic synaptic plasticity by enzymatic removal of heparan sulfates.

Heparan sulfates (HSs) are complex and highly active molecules that are required for synaptogenesis and long-term potentiation. A deficit in HSs leads to autistic phenotype in mice. Here, we investigated the long-term effect of heparinase I, which digests highly sulfated HSs, on the spontaneous bioelectrical activity of neuronal networks in developing primary hippocampal cultures.

Encoding/decoding of first and second order tactile afferents in a neurorobotic application.

We present a neurorobotic framework to investigate tactile information processing at the early stages of the somatosensory pathway. We focus on spatiotemporal coding of first and second order responses to Braille stimulation, which offers a suitable protocol to investigate the neural bases of fine touch discrimination. First, we model Slow Adaptive type I fingertip mechanoreceptor responses to Braille characters sensed both statically and dynamically.

Investigating neuronal activity by SPYCODE multi-channel data analyzer.

Multi-channel acquisition from neuronal networks, either in vivo or in vitro, is becoming a standard in modern neuroscience in order to infer how cell assemblies communicate. In spite of the large diffusion of micro-electrode-array-based systems, researchers usually find it difficult to manage the huge quantity of data routinely recorded during the experimental sessions. In fact, many of the available open-source toolboxes still lack two fundamental requirements for treating multi-channel recordings: (i) a rich repertoire of algorithms for extracting information both at a single channel and at the whole network level; (ii) the capability of autonomously repeating the same set of computational operations to 'multiple' recording streams (also from different experiments) and without a manual intervention.