NeuroDataReHack 2022

Constructing time-probability-matrices from simultaneously recorded visual areas using state space models

Brock Carlson, Blake Mitchell, David Zoltowski

Contrast sensitivity across layers and cell types within primary visual cortex

Blake Mitchell, Brock Carlson

To analyze V1 responses (2-photon calcium imaging) to full field images with varying image contrast.

Cross-Lab and Cross-Species co-clustering of cortical intracellular patch-clamp data

Sam Mestern

With this project, we aim to derive novel insights regarding cortical neuron differences between species and recording conditions.

Cross-species, cross-modality survey of bursting neurons

Chenggang Chen

Comparing the proportion of burst spiking neurons in the sensory cortex of non-human primates (macaque and marmoset) and rodents (gerbil and mouse).

Decoding history dependent neural activity in primary and higher visual areas

Lan Luo

Investigate how visual adaptation (history dependency of visual signals in the brain) transforms the encoding of stimulus identity using the Allen Institute Visual Behavior 2-Photon Imaging and Neuropixels datasets.

Elucidating Neural Learning Rules from Calcium Imaging Data

Felix Pei, Alessandro Salatiello

We want to analyze data from a mouse learning a task and see if any insight can be found regarding the learning rules used by the brain.

Ensemble computational models of neurons individually tuned with multimodal intracellular electrophysiology data

Krishna Pusuluri

Exploring the network of excitatory neurons with regularized GLMs

Tzu-Chi Yen, Yi-Yun, Josefina

This project explored statistical methods for extracting network structure from point process observations.

Feature-based embeddings of non-stationary ECoG

Brendan Harris

Describe the spatio-temporal dynamical structure of an ECoG recording using an existing pipeline for summarizing non-stationary neural data in a low-dimensional space of time-series features.

Finding neuronal networks based on shared temporal activity across task conditions

Noga Mudrik

A framework for finding interpretable functional neuronal networks presented across various non-stationary task conditions, and for assessing neural encoding uncertainty in the networks' latent space.

Fitting state space models in SSM repo to NWB datasets

David Zoltowski

My goal is to fit state space models in the SSM code package to NWB datasets.

Human iPSC-derived neurons recapitulate phenotypic variation within the human cortex

Michael Zabolocki

Human induced pluripotent stem cells (hiPSCs) offer a model which has the capacity to recapitulate the genetic underlying of the human brain across early neurodevelopment.

Identification of theta states in the prefrontal cortex

John Stout

Neural activity, paced at theta frequency (4-12 Hz) in the medial prefrontal cortex, is linked to memory-guided decision making.

Probing excitatory computations underlying probabilistic learning

Nuttida Rungratsameetaweemana

It is not well understood how excitatory and inhibitory neurons within each cortical region (as well as along the cortical hierarchy) work together to allow efficient information processing in dynamic environments.

Spyglass: data analysis framework for reproducible neuroscience research

Kyu Hyun Lee

Spyglass is a data analysis framework that brings together many open source tools, such as NWB, DataJoint, SpikeInterface, and others.

Sub-seconds Neural Emotion-Coding in Amygdala under Diverse Hippocampal Theta-Gamma States

Lu Zhang

How hippocampal theta-gamma coupling states affect neural emotion-coding in the amygdala, a brain region highly interacting with the hippocampus during emotion memory consolidation.

Using DANDI open datasets in transfer learning for decoding proprioception from neuronal calcium image using artificial neural network

Seungbin Park

Decoding proprioception is necessary for proprioceptive feedback to brain-machine interface to improve its movement performance.

Validating latent variable models in dandisets with rich behavioral data

Ben Lansdell

Tracking behavioral data from freely behaving animals, possibly in conjunction with neural recordings, is an exciting and growing direction in neuroscience.