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Found 12 papers, 1 papers with code
Hidden Traveling Waves bind Working Memory Variables in Recurrent Neural Networks
Traveling waves are a fundamental phenomenon in the brain, playing a crucial role in short-term information storage.
Transformers and Cortical Waves: Encoders for Pulling In Context Across Time
The capabilities of transformer networks such as ChatGPT and other Large Language Models (LLMs) have captured the world's attention.
Internal feedback in the cortical perception-action loop enables fast and accurate behavior
We show here that these delays can be compensated by internal feedback signals that flow backwards, from motor towards sensory areas.
Replay in Deep Learning: Current Approaches and Missing Biological Elements
Replay is the reactivation of one or more neural patterns, which are similar to the activation patterns experienced during past waking experiences.
The Unreasonable Effectiveness of Deep Learning in Artificial Intelligence
Deep learning networks have been trained to recognize speech, caption photographs and translate text between languages at high levels of performance.
Spiking Recurrent Networks as a Model to Probe Neuronal Timescales Specific to Working Memory
Cortical neurons process and integrate information on multiple timescales.
Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing
The parallel trend of deep-learning has led to a huge leap in performance towards solving various vision-based research problems such as object detection.
MCell-R: A particle-resolution network-free spatial modeling framework
Spatial heterogeneity can have dramatic effects on the biochemical networks that drive cell regulation and decision-making.
Multi-modal Approach for Affective Computing
Using multi-modal AMIGOS dataset, this study compares the performance of human emotion classification using multiple computational approaches applied to face videos and various bio-sensing modalities.
Human-Computer Interaction
Gradient Descent for Spiking Neural Networks
Much of studies on neural computation are based on network models of static neurons that produce analog output, despite the fact that information processing in the brain is predominantly carried out by dynamic neurons that produce discrete pulses called spikes.
Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings
In all of our simulated data, the differential covariance-based methods achieved better or similar performance to the GLM method and required fewer data samples.
The Wilson Machine for Image Modeling
We turn this representation into a directed probabilistic graphical model, transforming the learning problem into the unsupervised learning of the distribution of the critical bitplane and the supervised learning of the conditional distributions for the remaining bitplanes.
