Distinguishing between similar experiences is achieved by the brain in a process called pattern separation. In the hippocampus, pattern separation reduces the interference of memories and increases the storage capacity by decorrelating similar inputs patterns of neuronal activity into non-overlapping output firing patterns. Winners-take-all (WTA) mechanism is a theoretical model for pattern separation in which a "winner" cell suppresses the activity of the neighboring neurons through feedback inhibition. However, if the network properties of the dentate gyrus support WTA as a biologically conceivable model remains unknown. Here, we showed that the connectivity rules of PV+interneurons and their synaptic properties are optimizedfor efficient pattern separation. We found using multiple whole-cell in vitrorecordings that PV+interneurons mainly connect to granule cells (GC) through lateral inhibition, a form of feedback inhibition in which a GC inhibits other GCs but not itself through the activation of PV+interneurons. Thus, lateral inhibition between GC–PV+interneurons was ~10 times more abundant than recurrent connections. Furthermore, the GC–PV+interneuron connectivity was more spatially confined but less abundant than PV+interneurons–GC connectivity, leading to an asymmetrical distribution of excitatory and inhibitory connectivity. Our network model of the dentate gyrus with incorporated real connectivity rules efficiently decorrelates neuronal activity patterns using WTA as the primary mechanism. This process relied on lateral inhibition, fast-signaling properties of PV+interneurons and the asymmetrical distribution of excitatory and inhibitory connectivity. Finally, we found that silencing the activity of PV+interneurons in vivoleads to acute deficits in discrimination between similar environments, suggesting that PV+interneuron networks are necessary for behavioral relevant computations. Our results demonstrate that PV+interneurons possess unique connectivity and fast signaling properties that confer to the dentate gyrus network properties that allow the emergence of pattern separation. Thus, our results contribute to the knowledge of how specific forms of network organization underlie sophisticated types of information processing.
Espinoza Martinez C. Parvalbumin+ interneurons enable efficient pattern separation in hippocampal microcircuits. 2019. doi:10.15479/AT:ISTA:6363
Espinoza Martinez, C. (2019). Parvalbumin+ interneurons enable efficient pattern separation in hippocampal microcircuits. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:6363
Espinoza Martinez, Claudia . “Parvalbumin+ Interneurons Enable Efficient Pattern Separation in Hippocampal Microcircuits.” Institute of Science and Technology Austria, 2019. https://doi.org/10.15479/AT:ISTA:6363.
C. Espinoza Martinez, “Parvalbumin+ interneurons enable efficient pattern separation in hippocampal microcircuits,” Institute of Science and Technology Austria, 2019.
Espinoza Martinez C. 2019. Parvalbumin+ interneurons enable efficient pattern separation in hippocampal microcircuits. Institute of Science and Technology Austria.
Espinoza Martinez, Claudia. Parvalbumin+ Interneurons Enable Efficient Pattern Separation in Hippocampal Microcircuits. Institute of Science and Technology Austria, 2019, doi:10.15479/AT:ISTA:6363.
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