Synaptic efficacy and the transmission of complex firing patterns between neurons.
In central neurons, the summation of inputs from presynaptic cells combined with the unreliability of synaptic transmission produces incessant variations of the membrane potential termed synaptic noise (SN). These fluctuations, which depend on both the unpredictable timing of afferent activities and quantal variations of postsynaptic potentials, have defied conventional analysis. We show here that, when applied to SN recorded from the Mauthner (M) cell of teleosts, a simple method of nonlinear analysis reveals previously undetected features of this signal including hidden periodic components. The phase relationship between these components is compatible with the notion that the temporal organization of events comprising this noise is deterministic rather than random and that it is generated by presynaptic interneurons behaving as coupled periodic oscillators. Furthermore a model of the presynaptic network shows how SN is shaped both by activities in incoming inputs and by the distribution of their synaptic weights expressed as mean quantal contents of the activated synapses. In confirmation we found experimentally that long-term tetanic potentiation (LTP), which selectively increases some of these synaptic weights, permits oscillating temporal patterns to be transmitted more effectively to the postsynaptic cell. Thus the probabilistic nature of transmitter release, which governs the strength of synapses, may be critical for the transfer of complex timing information within neuronal assemblies.
Faure
P
P
Biologie Cellulaire et Moléculaire du Neurone (Institut National de la Santé et de la Recherche Médicale U261), Institut Pasteur, 75724 Paris Cedex 15, France.
Kaplan
D
D
Korn
H
H
D000818:Animals; D001683:Biological Clocks / Q000502:physiology; D004924:Escape Reaction / Q000502:physiology; D006054:Goldfish; D007395:Interneurons / Q000502:physiology*; D017774:Long-Term Potentiation / Q000502:physiology; D008564:Membrane Potentials / Q000502:physiology; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010507:Periodicity; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D009435:Synaptic Transmission / Q000502:physiology*; D015027:Zebrafish