Biology subjects

Migliore, R.

Publications and source records attributed to Migliore, R..

4 recordsLinked to original sources

bioRxiv2023

Community-based Reconstruction and Simulation of a Full-scale Model of Region CA1 of Rat Hippocampus

The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, it has been challenging to reconcile information obtained from diverse experimental approaches. To address this challenge, we present a community-driven, full-scale in silico model of the rat CA1 that integrates a broad range of experimental data, from synapse to network, including the reconstruction of its principal afferents, the Schaffer collaterals, and a model of the effects that acetylcholine has on the system. We tested and validated each model component and the final network model, and made input data, assumptions, and strategies explicit and transparent. The unique flexibility of the model allows scientists to address a range of scientific questions. In this article, we describe the methods used to set up simulations that reproduce and extend in vitro and in vivo experiments. Among several applications in the article, we focus on theta rhythm, a prominent hippocampal oscillation associated with various behavioral correlates and use our computer model to reproduce and reconcile experimental findings. Finally, we make data, code and model available through the hippocampushub.eu portal, which also provides an extensive set of analyses of the model and a user-friendly interface to facilitate adoption and usage. This neuroscience community-driven model represents a valuable tool for integrating diverse experimental data and provides a foundation for further research into the complex workings of the hippocampal CA1 region.

neuroscience↗

bioRxiv2020

Systematic comparison and automated validation of detailed models of hippocampal neurons

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. In addition, there is currently no simple way of quantitatively comparing different models regarding how closely they match specific experimental observations. This limits the evaluation, re-use and further development of the existing models. Further, the development of new models could also be significantly facilitated by the ability to rapidly test the behavior of model candidates against the relevant collection of experimental data. We address these problems for the representative case of the CA1 pyramidal cell of the rat hippocampus by developing an open-source Python test suite, which makes it possible to automatically and systematically test multiple properties of models by making quantitative comparisons between the models and electrophysiological data. The tests cover various aspects of somatic behavior, and signal propagation and integration in apical dendrites. To demonstrate the utility of our approach, we applied our tests to compare the behavior of several different hippocampal CA1 pyramidal cell models from the ModelDB database against electrophysiological data available in the literature, and concluded that each of these models provides a good match to experimental results in some domains but not in others. We also show how we employed the test suite to aid the development of models within the European Human Brain Project (HBP), and describe the integration of the tests into the validation framework developed in the HBP, with the aim of facilitating more reproducible and transparent model building in the neuroscience community. Author summary Anatomically and biophysically detailed neuronal models are useful tools in neuroscience because they allow the prediction of the behavior and the function of the studied cell type under circumstances that are hard to investigate experimentally. However, most detailed biophysical models have been built to capture a few selected properties of the real neuron, and it is often unknown how they would behave under different circumstances, or whether they can be used to successfully answer different scientific questions. To help the modeling community develop better neural models, and make the process of model building more reproducible and transparent, we developed a test suite that enables the comparison of the behavior of models of neurons in the rat hippocampus and their evaluation against experimental data. Applying our tests to several models available in the literature, we show that each model is able to capture some of the important properties of the real neuron but fails to match experimental data in other domains. We also use the test suite in the model development workflow of the European Human Brain Project to aid the construction of better models of hippocampal neurons and networks.

neuroscience↗

bioRxiv2019

Beyond the suprachiasmatic nucleus: circadian rhythmicity shapes astrocyte morphology and neuronal function in hippocampal area CA1

Most animal species operate according to a 24-hour period set by the suprachiasmatic nucleus (SCN) of the hypothalamus. The rhythmic activity of the SCN is known to modulate hippocampal-dependent memory processes, but the molecular and cellular mechanisms that account for this effect remain largely unknown. Here, we show that there are cell-type specific structural and functional changes that occur with circadian rhythmicity in neurons and astrocytes in hippocampal area CA1. Pyramidal neurons change the surface expression of NMDA receptors, whereas astrocytes change their proximity to synapses. Together, these phenomena alter glutamate clearance, receptor activation and integration of temporally clustered excitatory synaptic inputs, ultimately shaping hippocampal-dependent learning in vivo. We identify corticosterone as a key contributor to changes in synaptic strength. These findings identify important mechanisms through which neurons and astrocytes modify the molecular composition and structure of the synaptic environment, contribute to the local storage of information in the hippocampus and alter the temporal dynamics of cognitive processing.

neuroscience↗

bioRxiv2017

When a rose is not a rose: Pyramidal neurons respond differently in healthy and epileptic human neocortex

Epilepsy affects a huge number of patients by severe disruption of brain functions and is characterised by recurrent seizures, sometimes hard to be treated by medications. Seizure induced cellular consequences in ionic gradient and homeostasis are expected to result in electrophysiological differences between epileptic and non-epileptic neurons.\n\nIn the following work we demonstrate these differences in layer III cortical pyramidal neurons sourced from epileptic and non-epileptic human patient tissue. Although visually indistinguishable and featuring similar membrane potentials and latency to first spikes upon whole cell patch stimulation, epileptic pyramidal neurons display a larger rheobase and a smaller membrane resistance, responding less efficiently to electrical stimulation than their peri-tumorous equivalents. This decreased excitability contradicts results in comparable animal models of epilepsy and was further corroborated by detailed analysis of spiking characteristics and phase plot analysis of these events. Both point to an overexpression of K+ channels trying to compensate for the hyperexcited, epileptic network state. A computational model of a pyramidal neuron was utilized to give an estimate of the needful relative changes in K+ and Na+ conductances.

neuroscience↗