Abstract
The Python Modular Neural Network Toolbox (PymoNNto) provides a versatile and adaptable Python-based framework to develop and investigate brain-inspired neural networks. In contrast to other commonly used simulators such as Brian2 and NEST, PymoNNto imposes only minimal restrictions for implementation and execution. The basic structure of PymoNNto consists of one network class with several neuron- and synapse-groups. The behaviour of each group can be flexibly defined by exchangeable modules. The implementation of these modules is up to the user and only limited by Python itself. Behaviours can be implemented in Python, Numpy, Tensorflow, and other libraries to perform computations on CPUs and GPUs. PymoNNto comes with convenient high level behaviour modules, allowing differential equation-based implementations similar to Brian2, and an adaptable modular Graphical User Interface for real-time observation and modification of the simulated network and its parameters.
Highlights
Simulating neural networks has become an indispensable part of brain research, allowing neuroscientists to efficiently develop, explore, and evaluate hypotheses
PymoNNto allows for efficient implementation and analysis via a multitude of features, such as a powerful and extendable graphical user interface, a storage manager, and several pre-implemented neuronal/synaptic mechanisms and network models
We briefly summarize the most useful ones: Graphical User Interface (GUI) PymoNNto’s GUI is a powerful tool to interactively explore the behaviour of a network simulation
Summary
Simulating neural networks has become an indispensable part of brain research, allowing neuroscientists to efficiently develop, explore, and evaluate hypotheses Working with such models is facilitated by various simulation environments, which typically provide high level classes and functions for convenient model generation, simulation, and analysis. While for example, Neuron (Hines and Carnevale, 1997) excels at simulating neurons with a high degree of biological detail, NEST (Fardet et al, 2020) is optimized to simulate large networks of rather simplified spiking neurons on distributed computing clusters (Jordan et al, 2018) Another simulator, Brian/Brian (Goodman and Brette, 2009; Stimberg et al, 2019) prioritizes concise model definition over scaling to large computing environments. PymoNNto allows for efficient implementation and analysis via a multitude of features, such as a powerful and extendable graphical user interface, a storage manager, and several pre-implemented neuronal/synaptic mechanisms and network models (compare Table 1)
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