isoCycle 0.1.16

isolating single cycles of oscillatory activity in neuronal spiking

isoCycle

A Deep Network-Based Decoder for Isolating Single Cycles of Neural Oscillations in Spiking Activity

Neural oscillations are prominent features of brain activity, observable through frequency-specific power changes in electroencephalograms (EEG) and local field potentials (LFP), as well as coherence across brain regions, modulated by modes of activity. While identifying oscillations has primarily relied on EEG and LFP, the intrinsic relation between neural oscillations and neuronal spiking remains noteworthy. We investigate the possibility of detecting single cycles of neural rhythms solely through the spiking activity of neurons, using recent advancements in densely recording large populations of neurons within a local network. Many spikes from neurons in a local network provide an estimation of the network activity across time, which we can use to investigate the existence of cyclic patterns. Here, we employ recurrent neural networks to robustly isolate individual cycles of neural oscillations from the spiking of a densely recorded population of neurons. This isolation occurs in the time domain, where cycles from different time scales may combine in various ways to shape the network's spiking probability.

GPU acceleration

isoCycle employs a decoder developed with TensorFlow. Leveraging a GPU highly reduces execution time, a factor particularly crucial for long recordings. However, configuring your GPU to work with TensorFlow requires additional steps post-package installation. For quick guidance, refer to Setting up TensorFlow with GPU Support.

Installation

quick: install isoCycle with pip, consider a new environment as conflicts are likely

pip install isoCycle

Steps for installation from scratch using Anaconda:

1. First, ensure that you have Anaconda installed on your computer. If you don't have Anaconda, you can download it from the official Anaconda website. Anaconda is a popular distribution of Python that comes with many pre-installed packages and a package manager called conda, making it convenient for data analysis and scientific computing tasks.

2. Once you have Anaconda installed, open a terminal or command prompt on your computer, and create a new conda environment by executing the following command:

conda create --name myenv

Replace myenv with the desired name for your environment.

3. Activate the newly created environment with the following command:

conda activate myenv

Again, replace myenv with the name of your environment.

4. Install isoCycle and its dependencies by running the command:

conda install isoCycle

5. After the installation is complete, you can import isoCycle into your Python scripts or notebooks using the statement

import isoCycle

Now, with Anaconda installed, a new environment created, and isoCycle successfully installed, you are ready to analyze your data using isoCycle. One option is to use Jupyter Notebook. Here is an example Notebook: isoCycle_example.ipynb

Setting up TensorFlow with GPU Support

To utilize the power of GPU acceleration with TensorFlow, ensure that you have the correct hardware and software setup.

You will need a CUDA-capable GPU and you'll need to install the CUDA Toolkit and the cuDNN library. Once these are installed, you can install TensorFlow with built-in GPU support.

To check if TensorFlow is using the GPU, you can use the following Python code:

import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))

You can also log device placements with:

tf.debugging.set_log_device_placement(True)

For more detailed information, please refer to the following guides:

1. Using a GPU with TensorFlow.
2. CUDA Toolkit and cuDNN installation guides on the NVIDIA website. Please follow the respective instructions carefully when installing these components.