Field: Neuroscience › Cognitive NeuroscienceType: Couldn't replicate a published resultArea: computational neuroscienceTarget: Chaotic rate dynamics in spiking neuron networksStage: replication
Hypothesis
Networks of spiking neurons would show the same chaotic instability as matched rate networks, as an earlier study proposed.
What failed
The rate networks showed the expected signs of chaos, such as autocorrelation times that diverge near the transition. In the spiking networks, the autocorrelation time did not depend on coupling strength.
Broken assumption
Spiking neuron networks become chaotic in the same way as rate-based network models.
Why
The mathematical analogy between rate networks and spiking networks did not hold in the spiking simulations.
From the published paper: Engelken et al. F1000Research, 2016. ↗CC BY