PymoNNto: A Flexible Modular Toolbox for Designing Brain-Inspired Neural Networks

Stabilizing sequence learning in stochastic spiking networks with GABA-Modulated STDP

Cortical networks are capable of unsupervised learning and spontaneous replay of complex temporal sequences. Endowing artificial spiking neural networks with similar learning abilities remains a challenge. In particular, it is unresolved how different plasticity rules can contribute to both learning and the maintenance of network stability during learning. Here we introduce a biologically inspired form of GABA-Modulated Spike Timing-Dependent Plasticity (GMS) and demonstrate its ability to permit stable learning of complex temporal sequences including natural language in recurrent spiking neural networks. Motivated by biological findings, GMS utilizes the momentary level of inhibition onto excitatory cells to adjust both the magnitude and sign of Spike Timing-Dependent Plasticity (STDP) of connections between excitatory cells. In particular, high levels of inhibition in the network cause depression of excitatory-to-excitatory connections. We demonstrate the effectiveness of this mechanism during several sequence learning experiments with character- and token-based text inputs as well as visual input sequences. We show that GMS maintains stability during learning and spontaneous replay and permits the network to form a clustered hierarchical representation of its input sequences. Overall, we provide a biologically inspired model of unsupervised learning of complex sequences in recurrent spiking neural networks.

Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch

Spiking neural network simulations are a central tool in Computational Neuroscience, Artificial Intelligence, and Neuromorphic Engineering research. A broad range of simulators and software frameworks for such simulations exist with different target application areas. Among these, PymoNNto is a recent Python-based toolbox for spiking neural network simulations that emphasizes the embedding of custom code in a modular and flexible way. While PymoNNto already supports GPU implementations, its backend relies on NumPy operations. Here we introduce PymoNNtorch, which is natively implemented with PyTorch while retaining PymoNNto's modular design. Furthermore, we demonstrate how changes to the implementations of common network operations in combination with PymoNNtorch's native GPU support can offer speed-up over conventional simulators like NEST, ANNarchy, and Brian 2 in certain situations. Overall, we show how PymoNNto's modular and flexible design in combination with PymoNNtorch's GPU acceleration and optimized indexing operations facilitate research and development of spiking neural networks in the Python programming language.