A new Mathematical Framework to Understand Single Neuron Computations

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Neurons in the human amygdala and hippocampus are classically thought to encode a person’s identity invariant to visual features. However, it remains largely unknown how visual information from higher visual cortical areas is translated into such a semanti ...

Can spiking neural networks (SNNs) approximate the dynamics of recurrent neural networks? Arguments in classical mean-field theory based on laws of large numbers provide a positive answer when each neuron in the network has many “duplicates”, i.e., other n ...

This paper investigates the fundamental regression task of learning k neurons (a.k.a. teachers) from Gaussian input, using two-layer ReLU neural networks with width m (a.k.a. students) and m, k = O(1), trained via gradient descent under proper initializati ...

Deploying energy-efficient deep neural networks on energy-constrained edge devices is an important research topic in both machine learning and circuit design communities. Both artificial neural networks (ANNs) and spiking neural networks (SNNs) have been p ...

Neural organoids and spheroids are three-dimensional (3D) in vitro brain models that mimic key aspects of human brain cellular composition, organization, and function. These models have become essential for studying brain development and neurological disor ...

Hemispheric asymmetry is a universal property of brain organization with wide implications into brain function and structure, and diseases. This study presents a laterality index for characterizing hemispheric asymmetries that underlie cortical maps using ...

This paper introduces MNE-RT, a Python package designed for real-time neural feature extraction from magne-toencephalography (MEG) and electroencephalography (EEG) signals in Brain-Computer Interface (BCI) systems. The package incorporates efficient algori ...

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 ...

Previous studies have demonstrated that performance under selective attention and divided attention can be enhanced or impaired, depending on whether the stimuli from different modalities are the same or different. However, it remains unclear whether the m ...

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 ...

We present a reinforcement learning framework for constructing assemblies composed of rigid parts, which are commonly seen in many historical masonry buildings and bridges. Traditional construction methods for such structures often depend on dense scaffold ...

Understanding the interplay between human brain structure and function is crucial to discern neural dynamics. This study explores the relation between brain structure and macroscale functional activity using subject-specific structural connectome eigenmode ...

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, ...

“You never forget how to ride a bike”, – but how is that possible? The brain is able to learn complex skills, stop the practice for years, learn other skills in between, and still retrieve the original knowledge when necessary. The mechanisms of this capab ...

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 ...

Neural markers of visual function in age-related macular degeneration (AMD) allow clinicians and researchers to directly evaluate the functional changes in visual processing which occur as a result of the progressive loss of afferent input from the macula. ...

Background :Sensory reafferents are crucial to correct our posture and movements, both reflexively and in a cognitively driven manner. They are also integral to developing and maintaining a sense of agency for our actions. In cases of compromised reafferen ...

Deep neural networks (DNNs) are receiving increasing attention in wind power forecasting due to their ability to effectively capture complex patterns in wind data. However, their forecast errors are severely limited by the local optimal weight issue in opt ...

Social behaviors such as cooperation are crucial for mammals. A deeper knowledge of the neuronal mechanisms underlying cooperation can be beneficial for people suffering from pathologies with impaired social behavior. Our aim was to study the brain activit ...

Plant cells harness osmotic pressures to stiffen their leaves through strong turgor pressures. Key to this osmosisdriven stiffening is the confinement of liquids within semipermeable membranes that can regulate the transport of water molecules and ions. In ...

A new Mathematical Framework to Understand Single Neuron Computations

Chattez avec Graph Search

Feature-based encoding of face identity by single neurons in the human amygdala and hippocampus

Emergent Rate-Based Dynamics in Duplicate-Free Populations of Spiking Neurons

How Gradient Descent Balances Features: A Dynamical Analysis For Two-Layer Neural Networks

Eflop: a Sparsity-aware Metric for Evaluating Computational Cost in Spiking and Non-spiking Neural Networks

Soft microelectrode arrays for 3D in vitro brain models

Multiscale Quantification of Hemispheric Asymmetry in Cortical Maps Using Geometric Eigenmodes

Advances on Real Time M/EEG Neural Feature Extraction

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

The modulation of selective attention and divided attention on cross-modal congruence

Fast adaptation to rule switching using neuronal surprise

Learning to Assemble with Alternative Plans

On Reconstruction of Cortical Functional Maps Using Subject-Specific Geometric and Connectome Eigenmodes

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

Context selectivity with dynamic availability enables lifelong continual learning

Small-scale robotic devices for medical interventions in the brain

Video-evoked neuromarkers of visual function in age-related macular degeneration

Augmenting locomotor perception by remapping tactile foot sensation to the back

Improving the accuracy and interpretability of neural networks for wind power forecasting

Involvement of prelimbic cortex neurons and related circuits in the acquisition of a cooperative learning by pairs of rats

Osmosis-driven stiffening of structured hydrogels