Memory formation and recall in recurrent spiking neural networks

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

How does the brain generate behavioral responses from sensory stimuli? This sensor-to-motor transformation is a fundamental question in systems neuroscience. While recent experiments have highlighted the complexity of neural representations across the cere ...

The human brain is a complex and dynamic physiological system that can be considered as a network of reciprocally interconnected systems localized in different brain regions. To fully characterize the brain's complex dynamics, it is essential to investigat ...

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 ...

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 ...

Information is transmitted between brain regions through the release of neurotransmitters from long-range projecting axons. Understanding how the activity of such long-range connections contributes to behavior requires efficient methods for reversibly mani ...

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, ...

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

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 ...

In the last few years, stroke ranked as the second most common cause of death and is the third most significant condition affecting disability-adjusted life years (DALYs) worldwide. Being the most prevalent and quality of life impacting post-stroke symptom ...

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, ...

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 ...

Topographical disorientation refers to the selective inability to orient oneself in familiar surroundings. However, to date its neural correlates remain poorly understood. Here we use quantitative lesion analysis and a lesion network mapping approach in or ...

Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

Electrical stimulation of the nervous system has emerged as a promising assistive technology in case of many injuries and illnesses across various parts of the nervous system. In particular, the invasive neuromodulation of the peripheral nervous system see ...

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

Memory formation and recall in recurrent spiking neural networks

Chat with Graph Search

Capturing behavioral and neural variability in multi-area spiking neuronal network models

A Spatiotemporal Analysis of Brain Dynamics, a New Feature for Sleep and Seizure State Discrimination

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

Fast adaptation to rule switching using neuronal surprise

A bistable inhibitory optoGPCR for multiplexed optogenetic control of neural circuits

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

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Subcortical correlates of consciousness with human single neuron recordings

Probing and modulating inter-areal coupling in the cortical visual motion processing pathway with non-invasive brain stimulation

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

The neural correlates of topographical disorientation-a lesion analysis study

Enabling Uncertainty Estimation in Iterative Neural Networks

Sparse autoregressive neural networks for classical spin systems

Driving and suppressing the human language network using large language models

Biophysically accurate and machine learning-based surrogate models to optimize neuroprosthesis design and operation

Safe Deep Neural Networks

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks