LIF and LIF-matched rate networks exhibit correlated neural bases when driven but are chaotic without input.

Networks of 2000 LIF and LIF-matched rate neurons were simulated for 1s across different reservoir strengths (G) and feedback strengths (Q), both with and without driving input. In the absence of input, both networks exhibited chaotic dynamics and low c...

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Bibliographische Detailangaben
1. Verfasser:	Thomas Robert Newton (21756023) (author)
Weitere Verfasser:	Wilten Nicola (4951927) (author)
Veröffentlicht:	2025
Schlagworte:	Cell Biology Neuroscience Science Policy Biological Sciences not elsewhere classified supervised learning tasks slower learning rates slow learning rates longer fully interchangeable fast learning rates force hyperparameters identical force effectively act trial firing rate spiking network could matched instantaneous rate applied force training fire spiking networks spiking networks approximate force trained spiking decoding solutions learned rate network matched rate force training spiking networks rate networks training networks weights trained spiking neurons weight solutions weight matrices vice versa successful convergence selectively stabilizing results indicate principal components pass filter neural bases little effect leaky integrate inherent spike increasing instability findings suggest analytic derivation
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LIF and LIF-matched rate networks exhibit correlated neural bases when driven but are chaotic without input.

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