A universal workflow for creation, validation and generalization of detailed neuronal models

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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 possi ...

The outstanding information processing capacity of the brain relies on numerous molecular mechanisms. It is an extremely energy-expensive function, which involves specialization and collaboration of different cell types. A common framework to study the com ...

Neurons primarily communicate through release of neurotransmitter from presynaptic specialisations along their axonal arborisations. In order to understand the functional role of a specific neuron, it is therefore of great interest to know the structure of ...

CEBRA is a machine-learning method that can be used to compress time series in a way that reveals otherwise hidden structures in the variability of the data. It excels at processing behavioural and neural data recorded simultaneously, and it can decode act ...

The design of an oil free turbocharger supported on herringbone grooved gas bearing was formulated as a multi-objective problem, which was solved by coupling a reduced order parametric model for gas bearing supported rotors with an evolutionary algorithm. ...

Synaptic plasticity underlies the brain’s ability to learn and adapt. While experiments in brain slices have revealed mechanisms and protocols for the induction of plasticity between pairs of neurons, how these synaptic changes are coordinated in biologica ...

The convergence of human and artificial intelligence is currently receiving considerable scholarly attention. Much debate about the resulting Hybrid Minds focuses on the integration of artificial intelligence into the human brain through intelligent brain- ...

Empirical evidence shows that memories that are frequently revisited are easy to recall, and that familiar items involve larger hippocampal representations than less familiar ones. In line with these observations, here we develop a modelling approach to pr ...

Synaptic plasticity underlies our ability to learn and adapt to the constantly changing environment. The phenomenon of synapses changing their efficacy in an activity-dependent manner is often studied in small groups of neurons in vitro or indirectly throu ...

Oscillatory activity in the cerebellum and linked networks is an important aspect of neuronal processing and functional implementation of behavior. So far, it was challenging to quantify and study cerebellar oscillatory signatures in human neuroscience due ...

The relation between brain functional activity and the underlying structure is complex and varies depending on the specific brain region. Recently, we used graph signal processing to introduce the structural-decoupling index (SDI), a novel metric quantifyi ...

A universal workflow for creation, validation and generalization of detailed neuronal models

Chattez avec Graph Search

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

Molecular reconstruction and simulation of the Neuron-Glia-Vasculature system

Axonal projections from mouse whisker-related sensory cortex

Brain dynamics uncovered using a machine-learning algorithm

Design and experimental investigation of a herringbone grooved gas bearing supported turbocharger

Assemblies, synapse clustering and network topology interact with plasticity to explain structure-function relationships of the cortical connectome

Playing Brains: The Ethical Challenges Posed by Silicon Sentience and Hybrid Intelligence in DishBrain

A dynamic attractor network model of memory formation, reinforcement and forgetting

Principles of Network Plasticity in Neocortical Microcircuits

Mini-review: Transcranial Alternating Current Stimulation and the Cerebellum

Brain structure-function coupling is unique to individuals across multiple frequency bands: a graph signal processing study