Interpreting encoding and decoding models
N Kriegeskorte, PK Douglas - Current opinion in neurobiology, 2019 - Elsevier
Encoding and decoding models are widely used in systems, cognitive, and computational
neuroscience to make sense of brain-activity data. However, the interpretation of their results …
neuroscience to make sense of brain-activity data. However, the interpretation of their results …
Seeing beyond the brain: Conditional diffusion model with sparse masked modeling for vision decoding
Decoding visual stimuli from brain recordings aims to deepen our understanding of the
human visual system and build a solid foundation for bridging human and computer vision …
human visual system and build a solid foundation for bridging human and computer vision …
Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience
L Paninski, JP Cunningham - Current opinion in neurobiology, 2018 - Elsevier
Highlights•Modern recording technologies are creating data at a scale and complexity that
demand rigorous data analytical approaches.•Neural data science is an essential bridge …
demand rigorous data analytical approaches.•Neural data science is an essential bridge …
Generative adversarial networks for reconstructing natural images from brain activity
We explore a method for reconstructing visual stimuli from brain activity. Using large
databases of natural images we trained a deep convolutional generative adversarial …
databases of natural images we trained a deep convolutional generative adversarial …
Psychometry: An omnifit model for image reconstruction from human brain activity
Reconstructing the viewed images from human brain activity bridges human and computer
vision through the Brain-Computer Interface. The inherent variability in brain function …
vision through the Brain-Computer Interface. The inherent variability in brain function …
Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training
We present an automated method to track and identify neurons in C. elegans, called 'fast
Deep Neural Correspondence'or fDNC, based on the transformer network architecture. The …
Deep Neural Correspondence'or fDNC, based on the transformer network architecture. The …
Deep learning approaches for neural decoding across architectures and recording modalities
JA Livezey, JI Glaser - Briefings in bioinformatics, 2021 - academic.oup.com
Decoding behavior, perception or cognitive state directly from neural signals is critical for
brain–computer interface research and an important tool for systems neuroscience. In the …
brain–computer interface research and an important tool for systems neuroscience. In the …
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos
A fundamental goal of systems neuroscience is to understand the relationship between
neural activity and behavior. Behavior has traditionally been characterized by low …
neural activity and behavior. Behavior has traditionally been characterized by low …
Retinal encoding of natural scenes
D Karamanlis, HM Schreyer… - Annual Review of Vision …, 2022 - annualreviews.org
An ultimate goal in retina science is to understand how the neural circuit of the retina
processes natural visual scenes. Yet most studies in laboratories have long been performed …
processes natural visual scenes. Yet most studies in laboratories have long been performed …
YASS: Yet Another Spike Sorter applied to large-scale multi-electrode array recordings in primate retina
Spike sorting is a critical first step in extracting neural signals from large-scale multi-
electrode array (MEA) data. This manuscript presents several new techniques that make …
electrode array (MEA) data. This manuscript presents several new techniques that make …