spykes
Getting Started
What is Spykes?
Installing
Vanilla
Bleeding-Edge
Local Version
Datasets
Tutorials
Fitting Tuning Curves with Gradient Descent
Special Case 1: Poisson Generalized Linear Model (GLM)
Special Case 2: Generalized von Mises Model (GVM)
Minimizing Negative Log Likelihood with Gradient Descent
Decoding Feature from Population Activity
Examples Gallery
Neuropop Example
Create a NeuroPop object
Simulate a population of neurons
Split into training and testing sets
Fit the tuning curves with gradient descent
Predict the population activity with the fit tuning curves
Score the prediction
Plot the simulated and fit tuning curves
Decode feature from the population activity
Visualize ground truth vs. decoded estimates
Score decoding performance
PopVis Example
0 Initialization
0.1 Download Data
0.2 Read In Data
0.3 Initialize Variables
1 PopVis
1.1 Initiate all Neurons
1.2 Get Event Times
1.3 Create PopVis Object
CRCNS DataSet Example
0 Overview: Reproduce Figure
0.1 Article
0.2 Dataset
1 Data
1.1 Download Data
1.2 Load Data
2 Get Spike Times
3 Get Event Times
4 Get Features
5 Define Features
6 Plots
6.1 Rasters
6.2 PSTH
6.3 Reproduce Figure
6.4 ggplot
Neuropixels Example
Neuropixels
0 Download Data
1 Read In Data
2 Create Data Frame
3 Start Plotting
3.1 Striatum
3.2 Frontal
3.3 All Neurons
3.4 Striatum vs. Motor Cortex
Neural Coding Reward Example
0 Overview: Reproduce Figure
0.1 Article
0.2 Dataset
0.3 Initialization
1 First Graph of Panel A
1.1 Initiate all Neurons
1.2 Get Event Times
1.3 Match Peak Velocities
1.4 Plot PSTHs
2 First Graph of Panel C
2.1 Normalize PSTHs
2.2 Find Population Average
2.3 Plot PSTH
Reaching Dataset Example
Initialization
Download Reaching Dataset
Part I: NeuroVis
Instantiate Example PMd Neuron
Raster plot and PSTH aligned to target onset
Events
Features
Example 1: Reward vs No Reward
Example 2: according to quadrant of reaching direction
Example 3: Same as Example 2 but for an M1 neuron and aligned at goCueTime
Example 4: sorted by direction only for the trials with reward
Part II: NeuroPop
Extract reach direction x
Extract M1 spike counts Y
Split into train and test sets
Create an instance of NeuroPop
Predict firing rates
Score the prediction
Visualize tuning curves
Decode reach direction from population vector
Visualize decoded reach direction
Score decoding performance
Contributing
Guidelines
Testing
Building Documentation
API
Plotting
NeuroVis
PopVis
Machine Learning
NeuroPop
STRF
Sparse Filtering
Poisson Layers
Input / Output
Datasets
Config
Utils
spykes
Docs
»
Index
Index
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V
C
circ_corr() (in module spykes.utils)
compile() (spykes.ml.tensorflow.sparse_filtering.SparseFiltering method)
convolve_with_temporal_basis() (spykes.ml.strf.STRF method)
D
decode() (spykes.ml.neuropop.NeuroPop method)
design_prior_covariance() (spykes.ml.strf.STRF method)
display() (spykes.ml.neuropop.NeuroPop method)
F
fit() (spykes.ml.neuropop.NeuroPop method)
(spykes.ml.tensorflow.sparse_filtering.SparseFiltering method)
G
get_all_psth() (spykes.plot.popvis.PopVis method)
get_data_directory() (in module spykes.config)
get_home_directory() (in module spykes.config)
get_psth() (spykes.plot.neurovis.NeuroVis method)
get_raster() (spykes.plot.neurovis.NeuroVis method)
get_sort_indices() (in module spykes.utils)
get_spikecounts() (spykes.plot.neurovis.NeuroVis method)
grad_slow_exp() (in module spykes.utils)
L
load_neuropixels_data() (in module spykes.io.datasets)
load_reaching_data() (in module spykes.io.datasets)
load_reaching_xy() (in module spykes.io.datasets)
load_reward_data() (in module spykes.io.datasets)
load_spikefinder_data() (in module spykes.io.datasets)
log_likelihood() (in module spykes.utils)
M
make_2d_gaussian() (spykes.ml.strf.STRF method)
make_cosine_basis() (spykes.ml.strf.STRF method)
make_gaussian_basis() (spykes.ml.strf.STRF method)
make_image_from_spatial_basis() (spykes.ml.strf.STRF method)
make_raised_cosine_temporal_basis() (spykes.ml.strf.STRF method)
N
n_neurons (spykes.plot.popvis.PopVis attribute)
name (spykes.plot.popvis.PopVis attribute)
NeuroPop (class in spykes.ml.neuropop)
NeuroVis (class in spykes.plot.neurovis)
P
plot_heat_map() (spykes.plot.popvis.PopVis method)
plot_population_psth() (spykes.plot.popvis.PopVis method)
plot_psth() (spykes.plot.neurovis.NeuroVis method)
plot_raster() (spykes.plot.neurovis.NeuroVis method)
PoissonLayer (class in spykes.ml.tensorflow.poisson_models)
PopVis (class in spykes.plot.popvis)
predict() (spykes.ml.neuropop.NeuroPop method)
project_to_spatial_basis() (spykes.ml.strf.STRF method)
S
score() (spykes.ml.neuropop.NeuroPop method)
set_matplotlib_defaults() (in module spykes.utils)
set_params() (spykes.ml.neuropop.NeuroPop method)
simulate() (spykes.ml.neuropop.NeuroPop method)
slow_exp() (in module spykes.utils)
sparse_filtering_loss() (in module spykes.ml.tensorflow.sparse_filtering)
SparseFiltering (class in spykes.ml.tensorflow.sparse_filtering)
spykes.config (module)
spykes.io.datasets (module)
spykes.ml.neuropop (module)
spykes.ml.strf (module)
spykes.ml.tensorflow.poisson_models (module)
spykes.ml.tensorflow.sparse_filtering (module)
spykes.plot.neurovis (module)
spykes.plot.popvis (module)
spykes.utils (module)
STRF (class in spykes.ml.strf)
T
train_test_split() (in module spykes.utils)
V
visualize_gaussian_basis() (spykes.ml.strf.STRF method)