semvec/pypi-packages huggingface dataset:
miind 1.0.21
MIIND
Requires Python: >=3.6

# MIIND: a population level simulator.

MIIND is a simulator that allows the creation, simulation and analysis of large-scale neural networks. It does not model individual neurons, but models populations
directly, similarly to a neural mass model, except that we use population density techniques. Population density techniques are based on point model neurons, such as
leaky-integrate-and-fire (LIF), quadratic-integrate-and-fire neurons (QIF), or more complex ones, such as adaptive-exponential-integrate-and-fire (AdExp), Izhikevich,
Fitzhugh-Nagumo (FN). MIIND is able to model populations of 1D neural models (like LIF, QIF), 2D models (AdExp, Izhikevich, FN, others), or 3D models (Hindmarsh-Rose, Tsodyks-Markram Synapse). It does so by using
statistical techniques to answer the question: "If I'd run a NEST or BRIAN simulation (to name some point model-based simulators), where in state space would my neurons be?"
We calculate this distribution in terms of a density function, and from this density function we can infer many properties of the population, including its own firing rate.
By modeling large-scale networks as homogeneous populations that exchange firing rate statistics, rather than spikes, remarkable efficiency can be achieved, whilst retaining
a connection to spiking neurons that is not present in neural mass models.

## Documentation and Installation Instructions

MIIND is available through pypi and can be installed on most Linux and Windows systems (Mac version in development) with the command:

<strong>$ python -m pip install miind</strong>

For building from source and further documentation:

https://miind.readthedocs.io/en/latest/

## Three dimensional population density methods!
They said it could not be done, but we have created an efficient version of the Hindmarsh rose model,
a neural model with three state variables.
<img src="https://github.com/dekamps/miind/blob/master/images/hindmarsh.gif" alt="drawing" width="400"/>

## Gallery
### Single Population: Fitzhugh-Nagumo (Mesh Method)
[![](http://img.youtube.com/vi/vv9lgntZhYQ/0.jpg)](http://www.youtube.com/watch?v=vv9lgntZhYQ "MIIND : Fitzhugh-Nagumo example")

### Izhikevich
[![](http://img.youtube.com/vi/8p7jEz-qWTY/0.jpg)](http://www.youtube.com/watch?v=8p7jEz-qWTY "MIIND : Izhikevich example")

### Adaptive Exponential Integrate and Fire
<img src="https://github.com/dekamps/miind/blob/master/images/AdExp.gif" alt="drawing" width="400"/>

### Replication of Half Center Central Pattern Generator
[![](http://img.youtube.com/vi/9pC4MOWQ-Ho/0.jpg)](http://www.youtube.com/watch?v=9pC4MOWQ-Ho "MIIND : Persistent Sodium Half Centre example")
humanchain 0.0.1
LangChain for humans
Requires Python: >=3.7

# humanchain

[![PyPI - Version](https://img.shields.io/pypi/v/humanchain.svg)](https://pypi.org/project/humanchain)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/humanchain.svg)](https://pypi.org/project/humanchain)

-----

**Table of Contents**

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install humanchain
```

## License

`humanchain` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.