v1.0.5

🚀 Release v1.0.5

In this release, we added two new scripts, short_term_synaptic_plasticity.py and short_term_synaptic_plasticity_with_NEST.py, that implement short-term synaptic plasticity (STP) mechanisms, including short-term depression (STD) and short-term facilitation (STF). These scripts provide examples of how STP can be modeled in both standalone Python and using the NEST Simulator, illustrating the dynamic changes in synaptic strength that occur on short timescales in response to presynaptic activity.

v1.0.4

🚀 Release v1.0.4

In this release, we added two new scripts clopath_spike_pairing.py and clopath_biderectional_connections.py that implement the Clopath plasticity rule for synaptic learning in spiking neural networks. These scripts provide examples of how the Clopath rule can be applied to spike pairing and bidirectional connections, respectively, illustrating the dynamics of synaptic plasticity under different conditions.

v1.0.3

🚀 Release v1.0.3

In this release, we added a new script urbanczik_senn_plasticity.py that implements the Urbanczik-Senn plasticity rule for synaptic learning in spiking neural networks.

v1.0.2

🚀 Release v1.0.2

In this release, we added a new script nervos_snn_mnist.py that implements a spiking neural network (SNN) for MNIST pattern recognition using the nervos library. This script provides an end-to-end workflow for configuring experiment parameters, training the SNN, and analyzing the results with additional utilities for diagnostics and plots.

v1.0.1

🚀 Release v1.0.1

In this release, we added two new scripts stdp_weight_plot.py and stdp_simple_network_example.py to the repository that implement a simple spiking neural network example using spike-timing-dependent plasticity (STDP). These scripts are designed to illustrate the basic principles of STDP and how it can lead to learning in a neural network.

v1.0.0

🚀 Release v1.0.0

This is the initial release of the Neural Dynamics repository, featuring a comprehensive collection of educational Python scripts for computational neuroscience. The release includes tutorials on using the NEST Simulator, as well as standalone scripts implementing various neuron models and network dynamics.

📦 Scope and content

This release includes educational Python scripts covering a broad range of core topics in neural dynamics, such as:

• Single neuron models (Integrate-and-Fire, Izhikevich, AdEx, EIF)
• Spiking neural networks (Brunel network, oscillatory dynamics)
• Synaptic plasticity rules (BCM rule)
• Gap junctions and their role in neural modeling
• Rate models for collective neural activity

The repository structure reflects this thematic organization and mirrors the progression of the blog series.

🧠 Conceptual focus

The scripts in this repository are designed as didactic and conceptual examples. Emphasis is placed on:

• Clarity and interpretability of models
• Step-by-step explanations accompanying each script
• Reproducibility of classic results in theoretical neuroscience
• Encouraging experimentation and exploration of neural dynamics concepts
• Bridging theoretical neuroscience with practical implementation

Many models deliberately rely on reduced geometries, simplified boundary conditions, or idealized assumptions to keep the underlying mechanisms explicit.

🔬 Reproducibility and usage

All scripts are compatible with a lightweight Python environment based on NumPy, SciPy, and Matplotlib, along with the NEST Simulator for spiking neural network simulations. Instructions for setting up the environment and running the scripts are provided in the README file. The scripts are written to support both direct execution and interactive, cell-by-cell exploration in development environments such as VS Code or Jupyter.

This release provides a stable baseline for reuse in:

• teaching and coursework
• self-study
• illustrative figures and animations
• methodological extensions

Backward compatibility across future releases is not guaranteed, but changes will primarily serve conceptual clarification rather than feature expansion.

📝 License

All code is released under the GPL-3.0 License.

✨ Outlook

Future releases may expand individual examples, refine numerical implementations, or add complementary scripts aligned with new blog posts. Any such extensions will build on the conceptual baseline established with this release.