GIN G-Node Repositories
Layer-and-frequency-specific-task-engagement
This repository is about the classification of local field potentials data across different laminar layers
and spectral frequency bands for different task engagement conditions during a change detection task.
GIN | HTTPS | SSH | DOI: 10.12751/g-node.sdxr1v | @56Fe
This repository is about the classification of local field potentials data across different laminar layers
and spectral frequency bands for different task engagement conditions during a change detection task.
GIN | HTTPS | SSH | DOI: 10.12751/g-node.sdxr1v | @56Fe
Perceived-wealth-and-difficulty-modulation
This repository is about the analysis of behavioral and neural data about perceived wealth and difficulty in decision-making.
GIN | HTTPS | SSH | DOI: 10.12751/g-node.1kkrw6 | @56Fe
This repository is about the analysis of behavioral and neural data about perceived wealth and difficulty in decision-making.
GIN | HTTPS | SSH | DOI: 10.12751/g-node.1kkrw6 | @56Fe
Looking-to-cue-sides-and-modulation-of-value-in-OFC
Data and code for: Ferro et al., 2024, Gaze-centered gating, reactivation, and reevaluation of economic value in orbitofrontal cortex. Nature Communications 15, 6163. Data contains eye gaze and spiking data during risky decision-making tasks. Code runs characterization of psychometric, gaze behavioral and neural encoding of economic value gaze-centered analyses of neural encoding at task-relevant epochs, including task delays for value reactivation and reevaluation.
GIN | HTTPS | SSH | DOI: 10.12751/g-node.evlnq5 | @56Fe
Data and code for: Ferro et al., 2024, Gaze-centered gating, reactivation, and reevaluation of economic value in orbitofrontal cortex. Nature Communications 15, 6163. Data contains eye gaze and spiking data during risky decision-making tasks. Code runs characterization of psychometric, gaze behavioral and neural encoding of economic value gaze-centered analyses of neural encoding at task-relevant epochs, including task delays for value reactivation and reevaluation.
GIN | HTTPS | SSH | DOI: 10.12751/g-node.evlnq5 | @56Fe
V1-V4-LFPs-and-Visual-Attention
Data and code for: Ferro et al., 2021, Directed information exchange between cortical layers in macaque V1 and V4 and its modulation by selective attention. Proceedings of the National Academy of Sciences, 118(12), p.e2022097118. Data contains laminar local field potentials in V1 and V4. Code runs spectral power, coherence and Conditional Granger Causality (cGC) based on Mutual Information for interactions between and within V1-V4 laminar depths.
GIN | HTTPS | SSH | DOI: 10.12751/g-node.824cgx | @56Fe
Data and code for: Ferro et al., 2021, Directed information exchange between cortical layers in macaque V1 and V4 and its modulation by selective attention. Proceedings of the National Academy of Sciences, 118(12), p.e2022097118. Data contains laminar local field potentials in V1 and V4. Code runs spectral power, coherence and Conditional Granger Causality (cGC) based on Mutual Information for interactions between and within V1-V4 laminar depths.
GIN | HTTPS | SSH | DOI: 10.12751/g-node.824cgx | @56Fe
Analysis-of-Current-Source-Density
The code computes Currsent Source Densities via the iCSD Cubic Splines method published in (Pettersen et al., J Neurosci Methods, 2006) developed for Ferro et al., 2021, Directed information exchange between cortical layers in macaque V1 and V4 and its modulation by selective attention. Proceedings of the National Academy of Sciences, 118(12), p.e2022097118.
GIN | HTTPS | SSH | @56Fe
The code computes Currsent Source Densities via the iCSD Cubic Splines method published in (Pettersen et al., J Neurosci Methods, 2006) developed for Ferro et al., 2021, Directed information exchange between cortical layers in macaque V1 and V4 and its modulation by selective attention. Proceedings of the National Academy of Sciences, 118(12), p.e2022097118.
GIN | HTTPS | SSH | @56Fe
Analysis-of-MUAe-Latency
The function computes the Latency of MUAes by fitting 5 cumulative Gaussian distribution parameters (mu, sigma, alpha, c, d) following (Roelfsema, Tolboom, Khayat, Neuron 2007), developed for Ferro et al., 2021, Directed information exchange between cortical layers in macaque V1 and V4 and its modulation by selective attention. Proceedings of the National Academy of Sciences, 118(12), p.e2022097118.
GIN | HTTPS | SSH | @56Fe
The function computes the Latency of MUAes by fitting 5 cumulative Gaussian distribution parameters (mu, sigma, alpha, c, d) following (Roelfsema, Tolboom, Khayat, Neuron 2007), developed for Ferro et al., 2021, Directed information exchange between cortical layers in macaque V1 and V4 and its modulation by selective attention. Proceedings of the National Academy of Sciences, 118(12), p.e2022097118.
GIN | HTTPS | SSH | @56Fe
Folded-Normal-Distributions
The code allows to retrieve the analytical distribution and to fit data to folded normal distribution, including the distribution of sum and difference of pairs of random variables, developed for Ferro et al., 2021, Directed information exchange between cortical layers in macaque V1 and V4 and its modulation by selective attention. Proceedings of the National Academy of Sciences, 118(12), p.e2022097118.
GIN | HTTPS | SSH | @56Fe
The code allows to retrieve the analytical distribution and to fit data to folded normal distribution, including the distribution of sum and difference of pairs of random variables, developed for Ferro et al., 2021, Directed information exchange between cortical layers in macaque V1 and V4 and its modulation by selective attention. Proceedings of the National Academy of Sciences, 118(12), p.e2022097118.
GIN | HTTPS | SSH | @56Fe
Nearest-Neighbour-Search-Neural-Networks-Product-Quantization
Code for: Ferro et al., 2016, Nearest neighbour search using binary neural networks. In 2016 International Joint Conference on Neural Networks (IJCNN) (pp. 5106-5112). IEEE. The code finds nearest neighbours in terms of Euclidean distance, and uses it for classification. The search is optimized by combining Product Quantization (PQ) and binary neural associative memories (Willshaw Neural Networks).
GIN | HTTPS | SSH | @56Fe
Code for: Ferro et al., 2016, Nearest neighbour search using binary neural networks. In 2016 International Joint Conference on Neural Networks (IJCNN) (pp. 5106-5112). IEEE. The code finds nearest neighbours in terms of Euclidean distance, and uses it for classification. The search is optimized by combining Product Quantization (PQ) and binary neural associative memories (Willshaw Neural Networks).
GIN | HTTPS | SSH | @56Fe