Model for a robust neural integrator.
Integrator circuits in the brain show persistent firing that reflects the sum of previous excitatory and inhibitory inputs from external sources. Integrator circuits have been implicated in parametric working memory, decision making and motor control. Previous work has shown that stable integrator function can be achieved by an excitatory recurrent neural circuit, provided synaptic strengths are tuned with extreme precision (better than 1% accuracy). Here we show that integrator circuits can function without fine tuning if the neuronal units have bistable properties. Two specific mechanisms of bistability are analyzed, one based on local recurrent excitation, and the other on the voltage-dependence of the NMDA (N-methyl-D-aspartate) channel. Neither circuit requires fine tuning to perform robust integration, and the latter actually exploits the variability of neuronal conductances.
Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, California 92037, USA. akula@physics.utah.edu
Alexei A
AA
Koulakov
Sridhar
S
Raghavachari
Adam
A
Kepecs
John E
JE
Lisman
D003198:Computer Simulation; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D016194:Receptors, N-Methyl-D-Aspartate / Q000502:physiology*; D015203:Reproducibility of Results
D016194:Receptors, N-Methyl-D-Aspartate