Spatial interactions determine temporal feature integration as revealed by unmasking

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

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 ...

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 ...

Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocampal rep ...

While Spiking Neural Networks (SNNs) have been gaining in popularity, it seems that the algorithms used to train them are not powerful enough to solve the same tasks as those tackled by classical Artificial Neural Networks (ANNs).In this paper, we provide ...

Over the last decade, Low Intensity Focused Ultrasound Stimulation (LIFUS) has emerged as an attractive technology to modulate the activity of deep neural targets without invasive procedures. However, the underlying mechanisms by which ultrasonic waves can ...

For solving tasks such as recognizing a song, answering a question, or inverting a sequence of symbols, cortical microcircuits need to integrate and manipulate information that was dispersed over time during the preceding seconds. Creating biologically rea ...

Neuroscience incorporates knowledge from a range of scales, from single molecules to brain wide neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a ...

The computational load associated with computer vision is often prohibitive, and limits the capacity for on-board image analysis in compact mobile robots. Replicating the kind of feature detection and neural processing that animals excel at remains a chall ...

Brain networks are complex dynamical systems in which directed interactions between different areas evolve at the sub-second scale of sensory, cognitive and motor processes. Due to the highly non-stationary nature of neural signals and their unknown noise ...

While most models of randomly connected neural networks assume single-neuron models with simple dynamics, neurons in the brain exhibit complex intrinsic dynamics over multiple timescales. We analyze how the dynamical properties of single neurons and recurr ...

The way in which cortical microcircuit components -- most importantly neurons -- and their connectivity -- the network -- shape and constrain emergent dynamics is a long-standing question in neuroscience. Experimentally observed dynamical properties can of ...

This paper presents an ultra-low-power voice activity detector (VAD). It uses analog signal processing for acoustic feature extraction (AFE) directly on the microphone output, approximate event-driven analog-to-digital conversion (ED-ADC), and digital deep ...

Training deep neural networks with the error backpropagation algorithm is considered implausible from a biological perspective. Numerous recent publications suggest elaborate models for biologically plausible variants of deep learning, typically defining s ...

There have been many advances in the field of reinforcement learning in continuous control problems. Usually, these approaches use deep learning with artificial neural networks for approximation of policies and value functions. In addition, there have been ...

Interactions between the body and the environment occur within the peripersonal space (PPS), the space immediately surrounding the body. The PPS is encoded by multisensory (audio-tactile, visual-tactile) neurons that possess receptive fields (RFs) anchored ...

Hand grasping is a sophisticated motor task that has received much attention by the neuroscientific community, which demonstrated how grasping activates a network involving parietal, pre-motor and motor cortices using fMRI, ECoG, LFPs and spiking activity. ...

The simplification of neural network models is described. For example, a method for simplifying a neural network model includes providing the neural network model to be simplified, defining a first temporal filter for the conveyance of input from a neuron ...

Uniform random sparse network architectures are ubiquitous in computational neuroscience, but the implicit hypothesis that they are a good representation of real neuronal networks has been met with skepticism. Here we used two experimental data sets, a stu ...

Statistical models of neural activity are integral to modern neuroscience. Recently interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on ne ...

Generalized Linear Models (GLMs) are an increasingly popular framework for modeling neural spike trains. They have been linked to the theory of stochastic point processes and researchers have used this relation to assess goodness-of-fit using methods from ...

Spatial interactions determine temporal feature integration as revealed by unmasking

Chattez avec Graph Search

Subcortical correlates of consciousness with human single neuron recordings

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

Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity

A Time Encoding Approach to Training Spiking Neural Networks

A modeling framework to understand and optimize ultrasound neuromodulation

Spike frequency adaptation supports network computations on temporally dispersed information

A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience

A Neuro-Inspired Computational Model for a Visually Guided Robotic Lamprey Using Frame and Event Based Cameras

Modeling time-varying brain networks with a self-tuning optimized Kalman filter

How single neuron properties shape chaotic dynamics and signal transmission in random neural networks

Untangling emergent cortical dynamics: neurons from networks, noise from chaos

Design of an Always-On Deep Neural Network-Based 1-mu W Voice Activity Detector Aided With a Customized Software Model for Analog Feature Extraction

Biologically plausible deep learning – but how far can we go with shallow networks?

Learning Continuous Muscle Control for a Multi-joint Arm by Extending Proximal Policy Optimization with a Liquid State Machine

Neural adaptation accounts for the dynamic resizing of peripersonal space: evidence from a psychophysical-computational approach

Human EEG reveals distinct neural correlates of power and precision grasping types

Simplification of neural network models

Connection-type-specific biases make uniform random network models consistent with cortical recordings

Applying the Multivariate Time-Rescaling Theorem to Neural Population Models

Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models