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We study analytically a model of long-term synaptic plasticity where synaptic changes are triggered by presynaptic spikes, postsynaptic spikes, and the time differences between presynaptic and postsynaptic spikes. The changes due to correlated input and output spikes are quantified by means of a learning window. We show that plasticity can lead to an(More)
How does a neuron vary its mean output firing rate if the input changes from random to oscillatory coherent but noisy activity? What are the critical parameters of the neuronal dynamics and input statistics? To answer these questions, we investigate the coincidence-detection properties of an integrate-and-fire neuron. We derive an expression indicating how(More)
Neuronal oscillations allow for temporal segmentation of neuronal spikes. Interdependent oscillators can integrate multiple layers of information. We examined phase-phase coupling of theta and gamma oscillators in the CA1 region of rat hippocampus during maze exploration and rapid eye movement sleep. Hippocampal theta waves were asymmetric, and estimation(More)
Sharp wave-associated ∼200-Hz ripple oscillations in the hippocampus have been implicated in the consolidation of memories. However, knowledge on mechanisms underlying ripples is still scarce, in particular with respect to synaptic involvement of specific cell types. Here, we used cell-attached and whole-cell recordings in vitro to study activity of(More)
High-frequency hippocampal network oscillations, or "ripples," are thought to be involved in episodic memory. According to current theories, memory traces are represented by assemblies of principal neurons that are activated during ripple-associated network states. Here we performed in vivo and in vitro experiments to investigate the synaptic mechanisms(More)
Synaptic changes impair previously acquired memory traces. The smaller this impairment the larger is the longevity of memories. Two strategies have been suggested to keep memories from being overwritten too rapidly while preserving receptiveness to new contents: either introducing synaptic meta levels that store the history of synaptic state changes or(More)
Hebbian learning refers to an unsupervised correlation-based adaptation mechanism and is usually formulated in terms of mean firing rates. In this Chapter we study learning at the spike level. The learning process is driven by the temporal correlations between presynaptic spike arrival and postsynaptic firing. To explore the effect of learning on pulse(More)
When a rat moves, grid cells in its entorhinal cortex become active in multiple regions of the external world that form a hexagonal lattice. As the animal traverses one such "firing field," spikes tend to occur at successively earlier theta phases of the local field potential. This phenomenon is called phase precession. Here, we show that spike phases(More)