[HTML][HTML] The scientific case for brain simulations
A key element of the European Union's Human Brain Project (HBP) and other large-scale
brain research projects is the simulation of large-scale model networks of neurons. Here, we …
brain research projects is the simulation of large-scale model networks of neurons. Here, we …
Emerging materials for neuromorphic devices and systems
Neuromorphic devices and systems have attracted attention as next-generation computing
due to their high efficiency in processing complex data. So far, they have been demonstrated …
due to their high efficiency in processing complex data. So far, they have been demonstrated …
A solution to the learning dilemma for recurrent networks of spiking neurons
Recurrently connected networks of spiking neurons underlie the astounding information
processing capabilities of the brain. Yet in spite of extensive research, how they can learn …
processing capabilities of the brain. Yet in spite of extensive research, how they can learn …
Long short-term memory and learning-to-learn in networks of spiking neurons
Recurrent networks of spiking neurons (RSNNs) underlie the astounding computing and
learning capabilities of the brain. But computing and learning capabilities of RSNN models …
learning capabilities of the brain. But computing and learning capabilities of RSNN models …
Training deep neural density estimators to identify mechanistic models of neural dynamics
Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of
underlying causes. However, determining which model parameters agree with complex and …
underlying causes. However, determining which model parameters agree with complex and …
An adaptive threshold neuron for recurrent spiking neural networks with nanodevice hardware implementation
Abstract We propose a Double EXponential Adaptive Threshold (DEXAT) neuron model that
improves the performance of neuromorphic Recurrent Spiking Neural Networks (RSNNs) by …
improves the performance of neuromorphic Recurrent Spiking Neural Networks (RSNNs) by …
Generalized leaky integrate-and-fire models classify multiple neuron types
There is a high diversity of neuronal types in the mammalian neocortex. To facilitate
construction of system models with multiple cell types, we generate a database of point …
construction of system models with multiple cell types, we generate a database of point …
Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size
Neural population equations such as neural mass or field models are widely used to study
brain activity on a large scale. However, the relation of these models to the properties of …
brain activity on a large scale. However, the relation of these models to the properties of …
Neuronal circuits in barrel cortex for whisker sensory perception
The array of whiskers on the snout provides rodents with tactile sensory information relating
to the size, shape and texture of objects in their immediate environment. Rodents can use …
to the size, shape and texture of objects in their immediate environment. Rodents can use …
BluePyOpt: leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience
At many scales in neuroscience, appropriate mathematical models take the form of complex
dynamical systems. Parameterizing such models to conform to the multitude of available …
dynamical systems. Parameterizing such models to conform to the multitude of available …