Computational Cognitive Neuroscience, Fourth Edition
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Updated
Dec 19, 2023 - HTML
Computational Cognitive Neuroscience, Fourth Edition
A python package for modelling locomotion in complex environments and spatially/velocity selective cell activity.
3D-UCaps: 3D Capsules Unet for Volumetric Image Segmentation (MICCAI 2021)
BIDS App for Hippunfold (automated hippocampal unfolding and subfield segmentation)
State space models for decoding hippocampal trajectories and determining their type using sorted or clusterless data
AICONSlab's hippocampal segmentation algorithm using CNNs
In this project I develop a deep learning CNN model to predict Alzheimer's disease using 3D MRI medical images of the Hippocampus region of the brain.
Built an end-to-end AI system which features a deep learning algorithm that integrates into a clinical-grade viewer and automatically measures hippocampal volumes of new patients, as their studies are committed to the clinical imaging archive.
Deep spectral-based shape features for Alzheimer’s Disease classification
Application of AI in 3D Medical Imaging in quantifying Alzheimer's disease progression through automated measurement of hippocampal volume
Partial analyses to accompany "Uncovering temporal structure in hippocampal output patterns".
Hippocampal Segmentation Factory (HSF). A one-liner Deep Learning tool to segment raw T2w (and T1w) MRIs into hippocampal subfields in few seconds. Trained on 700+ manually segmented hippocampi.
State space models for categorization of replay content from multiunit spiking activity. Deng et al. 2016
The LCN-HippoModel is a biophysically realistic model of CA1 pyramidal cells aimed to get novel insights on firing dynamics in deep and superficial populations during the theta rhythm.
A semi-latent state-space model that combines movement, LFP, and ensemble spiking information to identify periods of replay and decode its content in real time. Long Tao et al., unpublished.
Python script & library to read hippocampal volumes computed by Freesurfer & ASHS
[Software for Master's thesis] ∘ Real-time signal detection for closed-loop, in-vivo neuroscience
Hippocampal transcriptomic responses to enzyme‐mediated cellular dissociation
Pytorch implementation of a G-CNN with a U-Net architecture, using discrete 3D roto-translational equivariant convolutions, to segment the hippocampal subfields on 3D MRIs.
Code for analysis on data from Jadhav et al. 2016
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