Abstract
The hippocampus is a brain region crucially involved in various cognitive functions including learning and memory processes. The hippocampal functions are performed by specific computations of its intrinsic neural circuitry in combination with interaction of the hippocampus with other brain regions. Therefore, the hippocampus has been conceived as a key network to studying and understanding the fundamental neural computations that supports higher brain functions. The hippocampus-involving functions are segregated along the longitudinal axis of the hippocampus. Importantly, it has been recently revealed that the local hippocampal circuit presents significant specializations between the two opposite poles or segments of the structure, namely between the dorsal and the ventral hippocampus suggesting that distinct neural processing may support the different functions performed by the hippocampus segments. The signal processing by neural networks crucially involves synaptic computations. In this study, we examined the synaptic dynamics of the dorsal and ventral synapses under conditions of different activation frequencies. We found that under consecutive activation the dorsal synapses display strong facilitation at a wide range of frequencies (1-40 Hz) while in ventral synapses the facilitation is restricted only to low activation frequency (1 Hz) and it lasted very shortly during activation. Thus, ventral synapses are mostly depressing. This evidence suggests that the dorsal hippocampal synaptic circuit presents wide-band filtering characteristics while the ventral are depressing low-pass synapses. The differing synaptic properties of the dorsal and the ventral hippocampus may underlie the higher ability for long-term plasticity of the dorsal hippocampus and the initiation of basic endogenous network oscillation in the ventral hippocampus.
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Papatheodoropoulos, C. (2017). Different Frequency-Dependent Properties Between Dorsal and Ventral Hippocampal Synapses. In: Frasson, C., Kostopoulos, G. (eds) Brain Function Assessment in Learning. BFAL 2017. Lecture Notes in Computer Science(), vol 10512. Springer, Cham. https://doi.org/10.1007/978-3-319-67615-9_17
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DOI: https://doi.org/10.1007/978-3-319-67615-9_17
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