Python Codes - Neuron Analysis

Structure

Python Codes/
├── requirements.txt                    # Python dependencies
├── Neuron Analysis/                    # Main analysis package
│   ├── __init__.py
│   ├── Neurons/                       # Core neuron analysis functions
│   │   ├── __init__.py
│   │   ├── spike_analysis.py          # Spike analysis and PSTH
│   │   ├── psth_analysis.py           # PSTH plotting and visualization
│   │   └── tdt_utils.py               # TDT data utilities
│   ├── TDT/                           # TDT data processing
│   │   ├── __init__.py
│   │   ├── tdt_reader.py              # TDT data reading functions
│   │   └── tdt_processing.py          # TDT data processing and filtering
│   ├── dPCA-master/                   # Demixed PCA implementation
│   │   ├── __init__.py
│   │   └── dpca.py                    # Main dPCA algorithm
│   ├── tSNE_matlab/                   # tSNE dimensionality reduction
│   │   ├── __init__.py
│   │   └── tsne.py                    # tSNE implementation
│   └── NeurometricExample/            # Neurometric analysis examples
│       ├── __init__.py
│       └── neurometric_example.py     # ROC analysis examples
└── demo.py                            # Demo script

Installation

1. Install Python dependencies:

pip install -r requirements.txt

2. For TDT data processing, you may need the TDT Python SDK:

pip install tdt

Key Features

Spike Analysis (Neurons/spike_analysis.py)

• align_spikes_to(): Align spikes to events and create spike matrices
• get_psth(): Calculate Peri-Stimulus Time Histograms with smoothing
• get_roc(): Calculate ROC curves and AUC
• get_selectivity(): Calculate selectivity indices
• get_tuning(): Calculate tuning curves

PSTH Analysis (Neurons/psth_analysis.py)

• plot_spike_psth(): Plot PSTH with raster plots
• plot_spike_raster(): Plot spike rasters
• plot_population_response(): Plot population responses
• plot_tuning_curve(): Plot tuning curves
• plot_roc_curve(): Plot ROC curves

TDT Data Processing (TDT/)

• tdt2mat(): Read TDT tank data
• sev2mat(): Read SEV files
• tdt_filter(): Filter TDT data
• ecog_tdt_data2matlab(): Process ECoG data

dPCA (dPCA-master/dpca.py)

• dpca(): Main dPCA algorithm
• dpca_explained_variance(): Calculate explained variance
• dpca_plot(): Plot dPCA results

tSNE (tSNE_matlab/tsne.py)

• tsne(): tSNE dimensionality reduction
• x2p(): Convert distances to probabilities
• tsne_d() and tsne_p(): Simplified tSNE variants

Neurometric Analysis (NeurometricExample/)

• run_neurometric_analysis(): Complete neurometric analysis
• calculate_roc_curve(): Calculate ROC curves
• plot_neurometric_function(): Plot neurometric functions

Usage Examples

Basic Spike Analysis

import numpy as np
from Neuron_Analysis.Neurons.spike_analysis import align_spikes_to, get_psth

# Example data
spike_times = np.random.exponential(100, 1000)
event_times = np.arange(0, 10000, 1000)

# Align spikes to events
spike_matrix, aligned_spikes = align_spikes_to(
    spike_times, event_times, pre=-500, post=1000, bin_size=10
)

# Calculate PSTH
psth, sem = get_psth(spike_matrix, bin_size=10, w=50, kernel='gauss')

PSTH Plotting

from Neuron_Analysis.Neurons.psth_analysis import plot_spike_psth

# Plot PSTH with raster
fig, axes = plot_spike_psth(
    spike_times, event_times, 
    pre=-500, post=1000, 
    title="Example PSTH"
)

TDT Data Processing

from Neuron_Analysis.TDT.tdt_reader import tdt2mat
from Neuron_Analysis.TDT.tdt_processing import tdt_filter

# Read TDT data
tdt_data = tdt2mat("MyTank", "Block-1")

# Filter data
filtered_data = tdt_filter(
    tdt_data['streams']['LFP1']['data'],
    fs=1000,
    low_freq=1,
    high_freq=100,
    filter_type='bandpass'
)

dPCA Analysis

from Neuron_Analysis.dPCA_master.dpca import dpca, dpca_plot

# Example data (neurons x conditions x time)
X = np.random.randn(50, 8, 100)

# Run dPCA
W, V, which_marg = dpca(X, num_comps=5)

# Plot results
dpca_plot(X, W, V, which_marg, plot_type='traces')

tSNE Dimensionality Reduction

from Neuron_Analysis.tSNE_matlab.tsne import tsne

# Example data
X = np.random.randn(1000, 100)

# Run tSNE
Y = tsne(X, no_dims=2, perplexity=30)

Neurometric Analysis

from Neuron_Analysis.NeurometricExample.neurometric_example import run_neurometric_analysis

# Run complete analysis
run_neurometric_analysis()

Contributing

When adding new functions or modifying existing ones:

1. Follow the existing code style
2. Add comprehensive docstrings
3. Include type hints
4. Test with example data

License

This code is based on the original implementation by Nader Nikbakht.