PyNN | Python package for simulator-independent specification

by NeuralEnsemble Python Version: 0.12.3 License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | PyNN Summary

PyNN is a Python library typically used in Simulation applications. PyNN has no vulnerabilities, it has build file available and it has low support. However PyNN has 12 bugs and it has a Non-SPDX License. You can install using 'pip install PyNN' or download it from GitHub, PyPI.

A Python package for simulator-independent specification of neuronal network models.

Support

Quality

Security

License

Reuse

Support

PyNN has a low active ecosystem.

It has 212 star(s) with 113 fork(s). There are 25 watchers for this library.

There were 2 major release(s) in the last 12 months.

There are 113 open issues and 442 have been closed. On average issues are closed in 1103 days. There are 10 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of PyNN is 0.12.3

Quality

PyNN has 12 bugs (7 blocker, 0 critical, 5 major, 0 minor) and 1072 code smells.

Security

PyNN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

PyNN code analysis shows 0 unresolved vulnerabilities.

There are 12 security hotspots that need review.

License

PyNN has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

PyNN releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

PyNN saves you 13707 person hours of effort in developing the same functionality from scratch.

It has 27497 lines of code, 2438 functions and 240 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed PyNN and discovered the below as its top functions. This is intended to give you an instant insight into PyNN implemented functionality, and help decide if they suit your requirements.

Performs a Convegent connection
Return an iterator over the parameters
Remove a parameter from the schema
Returns True if the object is a listlike object
Create cortical populations
Connects a fixed number product to a fixed number
Create a weight matrix
Adjusts the weight of the weights to the desired weight
Sets attributes from the given parameter space
Load mechanisms from path
Connects to the given projection
Get the data for a given variable
Run build
Connect the given projection
Build the post - synapses
Generate the next sample
Creates the nest of the model
Connect source to the given projection
Write spikes to disk
Plot raster bar charts
Run the simulation
Plot the current source
Builds the connected connections
Setup the simulator
Build a run
Plot a list of blocks

Get all kandi verified functions for this library.

PyNN Key Features

No Key Features are available at this moment for PyNN.

PyNN Examples and Code Snippets

No Code Snippets are available at this moment for PyNN.

Community Discussions

Trending Discussions on PyNN

Error while sending a spike using PyNN - SpiNNaker

Unable to remove TypeError: unhashable type: 'numpy.ndarray'

QUESTION

Error while sending a spike using PyNN - SpiNNaker

Asked 2020-Jul-22 at 12:31

I am trying to send a single spike to neuron 18 of the population I created using connection.send_spike(label, 18). The code I am using is the same as the example on Jypiter which runs correctly there.

...

ANSWER

Answered 2020-Jul-22 at 12:31

I am posting the answer I found in case someone else has the same problem

The output didn’t write the database; this can be forced by adding “create_database = True” to the “[Database]” section of your .spynnaker.cfg file.

Source https://stackoverflow.com/questions/62959896

QUESTION

Unable to remove TypeError: unhashable type: 'numpy.ndarray'

Asked 2018-Feb-25 at 12:03

I have just started learning tensorflow coding and I am stuck with very basic code for creating a neural network. Below is the the code:

...

ANSWER

Answered 2018-Feb-25 at 12:03

You are assigning the result of session.run() to a. This means a is a numpy array after the first run, overwriting the original definition as a placeholder. Then you are using this array as a key in the dictionary for the second run, which obviously doesn't work.

Renaming the a and b variables within the session context should fix it, e.g.

Source https://stackoverflow.com/questions/48972752

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install PyNN

You can install using 'pip install PyNN' or download it from GitHub, PyPI.
You can use PyNN like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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