Upper bounds on the number of causal pieces in spiking neural networks
In brief
- Two theoretical upper bounds on causal pieces in spiking neural networks are derived.
- The first bound scales with network depth and width, while the second depends on the neuron model used.
- These findings constrain model complexity and approximation power.
Researchers establish two upper bounds on the number of causal pieces in single-spike feedforward neural networks. These results constrain model complexity while shedding light on their approximation capabilities.
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