Ankur Sinha

University College London | UCL · Department of Neuroscience, Physiology, and Pharmacology

Data-driven models of neurons and circuits are important for understanding how the properties of membrane conductances, synapses, dendrites, and the anatomical connectivity between neurons generate the complex dynamical behaviors of brain circuits in health and disease. However, the inherent complexity of these biological processes makes the constr...

Data-driven models of neurons and circuits are important for understanding how the properties of membrane conductances, synapses, dendrites and the anatomical connectivity between neurons generate the complex dynamical behaviors of brain circuits in health and disease. However, the inherent complexity of these biological processes make the construc...

We are pleased to announce that the presentations and posters of the Annual Computational Neuroscience Meeting (CNS*2023) have become available. Discover the detailed program on the official website https://cns2023.sched.com ... Join us at Annual Computational Neuroscience Meeting.

Data-driven models of neurons and circuits are important for understanding how the properties of membrane conductances, synapses, dendrites and the anatomical connectivity between neurons generate the complex dynamical behaviors of brain circuits in health and disease. However, the inherent complexity of these biological processes make the construc...

Data-driven models of neurons and circuits are important for understanding how the properties of membrane conductances, synapses, dendrites and the anatomical connectivity between neurons generate the complex dynamical behaviors of brain circuits in health and disease. However, the inherent complexity of these biological processes make the construc...

Data-driven models of neurons and circuits are important for understanding how the properties of membrane conductances, synapses, dendrites and the anatomical connectivity between neurons generate the complex dynamical behaviors of brain circuits in health and disease. However, the inherent complexity of these biological processes make the construc...

We are delighted to present you the Proceedings of the 2022 CNS meeting. The CNS meeting encourages approaches that combine theoretical, computational, and experimental work in the neurosciences, and provides an opportunity for participants to share their views. The abstracts corresponding to speakers' talks and posters are what you find collected...

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed throug...

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed throug...

Foreword from the editors. We hosted four keynote speakers: Wolf Singer, Bill Bialek, Danielle Bassett, and Sonja Gruen. They enlightened us about computations in the cerebral cortex, the reduction of high-dimensional data, the emerging field of computational psychiatry, and the significance of spike patterns in motor cortex. From the submissions,...

NEST is a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural systems rather than on the exact morphology of individual neurons. For further information, visit http://www.nest-simulator.org. The release notes for this release are available at: https://github.com/nest/nest-simulator/releases/tag/v3...

Several homeostatic mechanisms enable the brain to maintain desired levels of neuronal activity. One of these, homeostatic structural plasticity, has been reported to restore activity in networks disrupted by peripheral lesions by altering their neuronal connectivity. While multiple lesion experiments have studied the changes in neurite morphology...

Several homeostatic mechanisms enable the brain to maintain desired levels of neuronal activity. One of these, homeostatic structural plasticity, has been reported to restore activity in networks disrupted by peripheral lesions by altering their neuronal connectivity. While multiple lesion experiments have studied the changes in neurite morphology...

NEST is a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural systems rather than on the exact morphology of individual neurons. For further information, visit http://www.nest-simulator.org. The release notes for this release are available at https://github.com/nest/nest-simulator/releases/tag/v2.1...

NEST is a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural systems rather than on the exact morphology of individual neurons. For further information, visit http://www.nest-simulator.org. The release notes for this release are available at https://github.com/nest/nest-simulator/releases/tag/v2.1...

Using an extracellular medium with high potassium/low magnesium concentration with the addition of 4-AP we induced epileptiform activity in combined hippocampus/entorhinal cortex slices of the rat brain [1]. In this in vitro model of temporal lobe epilepsy, we observed the repeating sequences of interictal discharge (IID) regimes and seizure-like e...

NEST is a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural systems rather than on the exact morphology of individual neurons. For further information, visit http://www.nest-simulator.org. The release notes for this release are available at: https://github.com/nest/nest-simulator/releases/tag/v2....

In recent work, Vogels and collaborators demonstrated the ability of spike-time dependent inhibitory plasticity to stabilise recurrent spiking neural networks by balancing out the excitatory input received by neurons in the network with the required amount of inhibition [1]. Further, as an application of this unsupervised balance, they showed that...

Grid cells in the dorsocaudal medial entorhinal cortex (dMEC) of the rat provide a metric representation of the animal's local environment. The collective firing patterns in a network of grid cells forms a triangular mesh that accurately tracks the location of the animal. The activity of a grid cell network, similar to head direction cells, display...

Head direction cells are thought to be an integral part of the neural navigation system. These cells track the agent's current head direction irrespective of the host's location. In doing so, they process a combination of inputs: angular velocity and visual inputs are major effectors; to correctly encode the agent's current heading. There are close...