Hippocampal Deletion of BDNF Gene Attenuates Gamma Oscillations in Area CA1 by Up-Regulating 5-HT3 Receptor

Background: Pyramidal neurons in the hippocampal area CA3 express high levels of BDNF, but how this BDNF contributes to oscillatory properties of hippocampus is unknown. Methodology/Principal Findings: Here we examined carbachol-induced gamma oscillations in hippocampal slices lacking BDNF gene in the area CA3. The power of oscillations was reduced in the hippocampal area CA1, which coincided with increases in the expression and activity of 5-HT3 receptor. Pharmacological block of this receptor partially restored power of gamma oscillations in slices from KO mice, but had no effect in slices from WT mice. Conclusion/Significance: These data suggest that BDNF facilitates gamma oscillations in the hippocampus by attenuating signaling through 5-HT3 receptor. Thus, BDNF modulates hippocampal oscillations through serotonergic system

Sparse Gamma Rhythms Arising through Clustering in Adapting Neuronal Networks

Gamma rhythms (30–100 Hz) are an extensively studied synchronous brain state responsible for a number of sensory, memory, and motor processes. Experimental evidence suggests that fast-spiking interneurons are responsible for carrying the high frequency components of the rhythm, while regular-spiking pyramidal neurons fire sparsely. We propose that a combination of spike frequency adaptation and global inhibition may be responsible for this behavior. Excitatory neurons form several clusters that fire every few cycles of the fast oscillation. This is first shown in a detailed biophysical network model and then analyzed thoroughly in an idealized model. We exploit the fact that the timescale of adaptation is much slower than that of the other variables. Singular perturbation theory is used to derive an approximate periodic solution for a single spiking unit. This is then used to predict the relationship between the number of clusters arising spontaneously in the network as it relates to the adaptation time constant. We compare this to a complementary analysis that employs a weak coupling assumption to predict the first Fourier mode to destabilize from the incoherent state of an associated phase model as the external noise is reduced. Both approaches predict the same scaling of cluster number with respect to the adaptation time constant, which is corroborated in numerical simulations of the full system. Thus, we develop several testable predictions regarding the formation and characteristics of gamma rhythms with sparsely firing excitatory neurons