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Neuronal Dynamics - Computational Neuroscience of Single Neurons
Related publications (1,000)
Fear learning induces synaptic potentiation between engram neurons in the rat lateral amygdala
Henry Markram, Rodrigo de Campos Perin
The lateral amygdala (LA) encodes fear memories by potentiating sensory inputs associated with threats and, in the process, recruits 10-30% of its neurons per fear memory engram. However, how the local network within the LA processes this information and w ...
Nature Portfolio2024Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights
Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi
Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...
EPFL Infoscience2024Fast adaptation to rule switching using neuronal surprise
In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signa ...
2024Subcortical correlates of consciousness with human single neuron recordings
Olaf Blanke, Fosco Bernasconi, Nathan Quentin Faivre, Michael Eric Anthony Pereira
Subcortical brain structures such as the basal ganglia or the thalamus are involved in regulating motor and cognitive behavior. However, their contribution to perceptual consciousness is still unclear, due to the inherent difficulties of recording subcorti ...
2024Cortical cell assemblies and their underlying connectivity: An in silico study
Michael Reimann, András Ecker, Sirio Bolaños Puchet, James Bryden Isbister, Daniela Egas Santander
Recent developments in experimental techniques have enabled simultaneous recordings from thousands of neurons, enabling the study of functional cell assemblies. However, determining the patterns of synaptic connectivity giving rise to these assemblies rema ...
2024Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse
Silvestro Micera, Daniela De Luca
Neural decoding of the visual system is a subject of research interest, both to understand how the visual system works and to be able to use this knowledge in areas, such as computer vision or brain-computer interfaces. Spike-based decoding is often used, ...
Ieee-Inst Electrical Electronics Engineers Inc2024Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain
Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...
EPFL2024Unveiling the complexity of learning and decision-making
Reinforcement learning (RL) is crucial for learning to adapt to new environments. In RL, the prediction error is an important component that compares the expected and actual rewards. Dopamine plays a critical role in encoding these prediction errors. In my ...
EPFL2024Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning
The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...
EPFL2024Small-scale robotic devices for medical interventions in the brain
Mahmut Selman Sakar, Lorenzo Francesco John Noseda
This article summarizes the recent advancements in the design, fabrication, and control of microrobotic devices for the diagnosis and treatment of brain disorders. With a focus on diverse actuation methods, we discuss how advancements in materials science ...
2024