![]() ![]() ![]() Moreover, SNNs have recently gained much attention for their value in low-power neuromorphic computing and practical machine learning applications 4, 5. Simulations of spiking neural networks (SNNs) are widely used to understand how brain functions arise from area-specific network dynamics 1, 2, 3. Such models strike a good balance between flexibility, performance, and biological accuracy, allowing us to explore dendritic contributions to network-level functions while paving the way for developing more powerful neuromorphic systems. Dendrify, through simple commands, automatically generates reduced compartmental neuron models with simplified yet biologically relevant dendritic and synaptic integrative properties. To bridge the gap between these two extremes and facilitate the adoption of dendritic features in spiking neural networks, we introduce Dendrify, an open-source Python package based on Brian 2. Conversely, circuit models with morphologically detailed neuron models are computationally costly, thus impractical for large-network simulations. Current spiking neural networks, although efficient, are usually quite simplistic, overlooking essential dendritic properties. This is partly because existing tools do not allow the development of realistic and efficient network models that account for dendrites. However, the role of dendritic computations in network-level operations remains largely unexplored. Computational modeling has been indispensable for understanding how subcellular neuronal features influence circuit processing. ![]()
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