Supervised learning
Can spiking neural networks learn precise timing with hidden layers?
Open access · cc by · source: Europe PMC
A surrogate-gradient rule lets multilayer spiking networks learn precisely timed outputs, but on hard tasks random feedback does much worse than backprop-like symmetric feedback.
Study at a glance
- Design
- Computational / modelling — Simulations of leaky integrate-and-fire networks trained with the SuperSpike rule under three feedback schemes on synthetic spike-pattern tasks
- N
- No dataset N; tasks are synthetic (single neuron with 100 inputs, a spiking XOR task, and a 100-output pattern task)
- Population
- Simulated spiking neural networks
- Outcome
- Van Rossum distance to target spike trains and classification accuracy
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Key findings
A single neuron that started completely silent learned a five-spike target after 500 trials, without injecting noise. On the spiking XOR task, networks with hidden layers learned it perfectly while a network without hidden units could not; uniform feedback was worse on average. On a harder task with 100 output neurons, symmetric feedback with 32 or more hidden units matched the target visually, while random feedback performed worse than even a network with no hidden layer, partly improved by an activity regulariser.
Methodology
The authors derived SuperSpike, a learning rule that replaces the non-differentiable spike with a smooth function of membrane voltage, giving a local three-factor Hebbian update with eligibility traces. They trained simulated leaky integrate-and-fire networks to produce target spike trains and to solve a spiking exclusive-or task. Hidden units received error signals via symmetric (backprop-like), random (feedback alignment) or uniform (global) feedback.
Limitations
All tasks are small synthetic spike-pattern problems, not real-world benchmarks, so it says little about scaling to large datasets. The eligibility traces are computed per synapse, so cost grows with the square of neuron count, which the authors flag. Biological interpretations (calcium transients, neuromodulators) are suggestions, not tested against neural data. Only shallow three-layer feedforward networks were studied, not deep or recurrent ones.
How this study connects
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