AIMC Topic: Visual Cortex
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A visual encoding model based on deep neural networks and transfer learning for brain activity measured by functional magnetic resonance imaging.
Journal of neuroscience methods
Jun 27, 2019
BACKGROUND: Building visual encoding models to accurately predict visual responses is a central challenge for current vision-based brain-machine interface techniques. To achieve high prediction accuracy on neural signals, visual encoding models shoul...
Human visual cortical gamma reflects natural image structure.
NeuroImage
Jun 24, 2019
Many studies have reported visual cortical gamma-band activity related to stimulus processing and cognition. Most respective studies used artificial stimuli, and the few studies that used natural stimuli disagree. Electrocorticographic (ECoG) recordi...
Exploring spatiotemporal neural dynamics of the human visual cortex.
Human brain mapping
Jun 23, 2019
The human visual cortex is organized in a hierarchical manner. Although previous evidence supporting this hypothesis has been accumulated, specific details regarding the spatiotemporal information flow remain open. Here we present detailed spatiotemp...
The Ventral Visual Pathway Represents Animal Appearance over Animacy, Unlike Human Behavior and Deep Neural Networks.
The Journal of neuroscience : the official journal of the Society for Neuroscience
Jun 13, 2019
Recent studies showed agreement between how the human brain and neural networks represent objects, suggesting that we might start to understand the underlying computations. However, we know that the human brain is prone to biases at many perceptual a...
Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex.
NeuroImage
May 16, 2019
Goal-driven and feedforward-only convolutional neural networks (CNN) have been shown to be able to predict and decode cortical responses to natural images or videos. Here, we explored an alternative deep neural network, variational auto-encoder (VAE)...
Discrimination of Motion Direction in a Robot Using a Phenomenological Model of Synaptic Plasticity.
Computational intelligence and neuroscience
May 2, 2019
Recognizing and tracking the direction of moving stimuli is crucial to the control of much animal behaviour. In this study, we examine whether a bio-inspired model of synaptic plasticity implemented in a robotic agent may allow the discrimination of ...
Deep convolutional models improve predictions of macaque V1 responses to natural images.
PLoS computational biology
Apr 23, 2019
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 understanding of the nonlinear computations in V1. Recent...
Fast and robust active neuron segmentation in two-photon calcium imaging using spatiotemporal deep learning.
Proceedings of the National Academy of Sciences of the United States of America
Apr 11, 2019
Calcium imaging records large-scale neuronal activity with cellular resolution in vivo. Automated, fast, and reliable active neuron segmentation is a critical step in the analysis workflow of utilizing neuronal signals in real-time behavioral studies...
Autonomous patch-clamp robot for functional characterization of neurons in vivo: development and application to mouse visual cortex.
Journal of neurophysiology
Apr 10, 2019
Patch clamping is the gold standard measurement technique for cell-type characterization in vivo, but it has low throughput, is difficult to scale, and requires highly skilled operation. We developed an autonomous robot that can acquire multiple cons...
Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network.
Scientific reports
Mar 7, 2019
A comprehensive understanding of the stimulus-response properties of individual neurons is necessary to crack the neural code of sensory cortices. However, a barrier to achieving this goal is the difficulty of analysing the nonlinearity of neuronal r...
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