Inception loops discover what excites neurons most using deep predictive models
Finding sensory stimuli that drive neurons optimally is central to understanding information
processing in the brain. However, optimizing sensory input is difficult due to the …
processing in the brain. However, optimizing sensory input is difficult due to the …
The sensorium competition on predicting large-scale mouse primary visual cortex activity
The neural underpinning of the biological visual system is challenging to study
experimentally, in particular as the neuronal activity becomes increasingly nonlinear with …
experimentally, in particular as the neuronal activity becomes increasingly nonlinear with …
Using goal-driven deep learning models to understand sensory cortex
DLK Yamins, JJ DiCarlo - Nature neuroscience, 2016 - nature.com
Fueled by innovation in the computer vision and artificial intelligence communities, recent
developments in computational neuroscience have used goal-driven hierarchical …
developments in computational neuroscience have used goal-driven hierarchical …
A neural network trained for prediction mimics diverse features of biological neurons and perception
Recent work has shown that convolutional neural networks (CNNs) trained on image
recognition tasks can serve as valuable models for predicting neural responses in primate …
recognition tasks can serve as valuable models for predicting neural responses in primate …
Deep convolutional models improve predictions of macaque V1 responses to natural images
Despite great efforts over several decades, our best models of primary visual cortex (V1) still
predict spiking activity quite poorly when probed with natural stimuli, highlighting our limited …
predict spiking activity quite poorly when probed with natural stimuli, highlighting our limited …
Deep neural network models of sensory systems: windows onto the role of task constraints
AJE Kell, JH McDermott - Current opinion in neurobiology, 2019 - Elsevier
Highlights•Deep neural networks (DNNs) now reach human-level performance on some
perceptual tasks.•They show human-like error patterns and predict sensory cortical …
perceptual tasks.•They show human-like error patterns and predict sensory cortical …
Deep neural networks in computational neuroscience
The goal of computational neuroscience is to find mechanistic explanations of how the
nervous system processes information to support cognitive function and behaviour. At the …
nervous system processes information to support cognitive function and behaviour. At the …
Inferring hidden structure in multilayered neural circuits
N Maheswaranathan, DB Kastner… - PLoS computational …, 2018 - journals.plos.org
A central challenge in sensory neuroscience involves understanding how neural circuits
shape computations across cascaded cell layers. Here we attempt to reconstruct the …
shape computations across cascaded cell layers. Here we attempt to reconstruct the …
Inferring nonlinear neuronal computation based on physiologically plausible inputs
The computation represented by a sensory neuron's response to stimuli is constructed from
an array of physiological processes both belonging to that neuron and inherited from its …
an array of physiological processes both belonging to that neuron and inherited from its …
Deep learning models of the retinal response to natural scenes
L McIntosh, N Maheswaranathan… - Advances in neural …, 2016 - proceedings.neurips.cc
A central challenge in sensory neuroscience is to understand neural computations and
circuit mechanisms that underlie the encoding of ethologically relevant, natural stimuli. In …
circuit mechanisms that underlie the encoding of ethologically relevant, natural stimuli. In …