Synaptic learning rules and sparse coding in a model sensory system.
Neural circuits exploit numerous strategies for encoding information. Although the functional significance of individual coding mechanisms has been investigated, ways in which multiple mechanisms interact and integrate are not well understood. The locust olfactory system, in which dense, transiently synchronized spike trains across ensembles of antenna lobe (AL) neurons are transformed into a sparse representation in the mushroom body (MB; a region associated with memory), provides a well-studied preparation for investigating the interaction of multiple coding mechanisms. Recordings made in vivo from the insect MB demonstrated highly specific responses to odors in Kenyon cells (KCs). Typically, only a few KCs from the recorded population of neurons responded reliably when a specific odor was presented. Different odors induced responses in different KCs. Here, we explored with a biologically plausible model the possibility that a form of plasticity may control and tune synaptic weights of inputs to the mushroom body to ensure the specificity of KCs' responses to familiar or meaningful odors. We found that plasticity at the synapses between the AL and the MB efficiently regulated the delicate tuning necessary to selectively filter the intense AL oscillatory output and condense it to a sparse representation in the MB. Activity-dependent plasticity drove the observed specificity, reliability, and expected persistence of odor representations, suggesting a role for plasticity in information processing and making a testable prediction about synaptic plasticity at AL-MB synapses.
Finelli
Luca A
LA
Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California, United States of America. lfinelli@salk.edu
Haney
Seth
S
Bazhenov
Maxim
M
Stopfer
Mark
M
Sejnowski
Terrence J
TJ
D000200:Action Potentials / Q000502:physiology; D000222:Adaptation, Physiological / Q000502:physiology; D000818:Animals; D001683:Biological Clocks / Q000502:physiology; D003198:Computer Simulation; D006110:Grasshoppers / Q000502:physiology; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology*; D018034:Olfactory Receptor Neurons / Q000502:physiology*; D012903:Smell / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*