Learning precisely timed spikes.
To signal the onset of salient sensory features or execute well-timed motor sequences, neuronal circuits must transform streams of incoming spike trains into precisely timed firing. To address the efficiency and fidelity with which neurons can perform such computations, we developed a theory to characterize the capacity of feedforward networks to generate desired spike sequences. We find the maximum number of desired output spikes a neuron can implement to be 0.1-0.3 per synapse. We further present a biologically plausible learning rule that allows feedforward and recurrent networks to learn multiple mappings between inputs and desired spike sequences. We apply this framework to reconstruct synaptic weights from spiking activity and study the precision with which the temporal structure of ongoing behavior can be inferred from the spiking of premotor neurons. This work provides a powerful approach for characterizing the computational and learning capacities of single neurons and neuronal circuits.
Donders Institute, Radboud University, Nijmegen 6525, the Netherlands; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
Raoul-Martin
RM
Memmesheimer
Racah Institute of Physics, Hebrew University, Jerusalem 91904, Israel; The Edmond and Lily Safra Center for Brain Sciences, Hebrew University, Jerusalem 91904, Israel.
Ran
R
Rubin
Center for Brain Science, Harvard University, Cambridge, MA 02138, USA; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA.
Bence P
BP
Olveczky
Center for Brain Science, Harvard University, Cambridge, MA 02138, USA; Racah Institute of Physics, Hebrew University, Jerusalem 91904, Israel; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA. Electronic address: haim@fiz.huji.ac.il.
Haim
H
Sompolinsky
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001921:Brain / Q000166:cytology*; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D013569:Synapses / Q000502:physiology; D013997:Time Factors