@article{10816, abstract = {Pattern separation is a fundamental brain computation that converts small differences in input patterns into large differences in output patterns. Several synaptic mechanisms of pattern separation have been proposed, including code expansion, inhibition and plasticity; however, which of these mechanisms play a role in the entorhinal cortex (EC)–dentate gyrus (DG)–CA3 circuit, a classical pattern separation circuit, remains unclear. Here we show that a biologically realistic, full-scale EC–DG–CA3 circuit model, including granule cells (GCs) and parvalbumin-positive inhibitory interneurons (PV+-INs) in the DG, is an efficient pattern separator. Both external gamma-modulated inhibition and internal lateral inhibition mediated by PV+-INs substantially contributed to pattern separation. Both local connectivity and fast signaling at GC–PV+-IN synapses were important for maximum effectiveness. Similarly, mossy fiber synapses with conditional detonator properties contributed to pattern separation. By contrast, perforant path synapses with Hebbian synaptic plasticity and direct EC–CA3 connection shifted the network towards pattern completion. Our results demonstrate that the specific properties of cells and synapses optimize higher-order computations in biological networks and might be useful to improve the deep learning capabilities of technical networks.}, author = {Guzmán, José and Schlögl, Alois and Espinoza Martinez, Claudia and Zhang, Xiaomin and Suter, Benjamin and Jonas, Peter M}, issn = {2662-8457}, journal = {Nature Computational Science}, keywords = {general medicine}, number = {12}, pages = {830--842}, publisher = {Springer Nature}, title = {{How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network}}, doi = {10.1038/s43588-021-00157-1}, volume = {1}, year = {2021}, } @misc{10110, abstract = {Pattern separation is a fundamental brain computation that converts small differences in input patterns into large differences in output patterns. Several synaptic mechanisms of pattern separation have been proposed, including code expansion, inhibition and plasticity; however, which of these mechanisms play a role in the entorhinal cortex (EC)–dentate gyrus (DG)–CA3 circuit, a classical pattern separation circuit, remains unclear. Here we show that a biologically realistic, full-scale EC–DG–CA3 circuit model, including granule cells (GCs) and parvalbumin-positive inhibitory interneurons (PV+-INs) in the DG, is an efficient pattern separator. Both external gamma-modulated inhibition and internal lateral inhibition mediated by PV+-INs substantially contributed to pattern separation. Both local connectivity and fast signaling at GC–PV+-IN synapses were important for maximum effectiveness. Similarly, mossy fiber synapses with conditional detonator properties contributed to pattern separation. By contrast, perforant path synapses with Hebbian synaptic plasticity and direct EC–CA3 connection shifted the network towards pattern completion. Our results demonstrate that the specific properties of cells and synapses optimize higher-order computations in biological networks and might be useful to improve the deep learning capabilities of technical networks.}, author = {Guzmán, José and Schlögl, Alois and Espinoza Martinez, Claudia and Zhang, Xiaomin and Suter, Benjamin and Jonas, Peter M}, publisher = {IST Austria}, title = {{How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network}}, doi = {10.15479/AT:ISTA:10110}, year = {2021}, } @phdthesis{6363, abstract = {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. }, author = {Espinoza Martinez, Claudia }, isbn = {978-3-99078-000-8}, issn = {2663-337X}, pages = {140}, publisher = {Institute of Science and Technology Austria}, title = {{Parvalbumin+ interneurons enable efficient pattern separation in hippocampal microcircuits}}, doi = {10.15479/AT:ISTA:6363}, year = {2019}, } @article{21, abstract = {Parvalbumin-positive (PV+) GABAergic interneurons in hippocampal microcircuits are thought to play a key role in several higher network functions, such as feedforward and feedback inhibition, network oscillations, and pattern separation. Fast lateral inhibition mediated by GABAergic interneurons may implement a winner-takes-all mechanism in the hippocampal input layer. However, it is not clear whether the functional connectivity rules of granule cells (GCs) and interneurons in the dentate gyrus are consistent with such a mechanism. Using simultaneous patch-clamp recordings from up to seven GCs and up to four PV+ interneurons in the dentate gyrus, we find that connectivity is structured in space, synapse-specific, and enriched in specific disynaptic motifs. In contrast to the neocortex, lateral inhibition in the dentate gyrus (in which a GC inhibits neighboring GCs via a PV+ interneuron) is ~ 10-times more abundant than recurrent inhibition (in which a GC inhibits itself). Thus, unique connectivity rules may enable the dentate gyrus to perform specific higher-order computations}, author = {Espinoza Martinez, Claudia and Guzmán, José and Zhang, Xiaomin and Jonas, Peter M}, journal = {Nature Communications}, number = {1}, publisher = {Nature Publishing Group}, title = {{Parvalbumin+ interneurons obey unique connectivity rules and establish a powerful lateral-inhibition microcircuit in dentate gyrus}}, doi = {10.1038/s41467-018-06899-3}, volume = {9}, year = {2018}, } @article{1845, abstract = {Based on extrapolation from excitatory synapses, it is often assumed that depletion of the releasable pool of synaptic vesicles is the main factor underlying depression at inhibitory synapses. In this issue of Neuron, using subcellular patch-clamp recording from inhibitory presynaptic terminals, Kawaguchi and Sakaba (2015) show that at Purkinje cell-deep cerebellar nuclei neuron synapses, changes in presynaptic action potential waveform substantially contribute to synaptic depression. Based on extrapolation from excitatory synapses, it is often assumed that depletion of the releasable pool of synaptic vesicles is the main factor underlying depression at inhibitory synapses. In this issue of Neuron, using subcellular patch-clamp recording from inhibitory presynaptic terminals, Kawaguchi and Sakaba (2015) show that at Purkinje cell-deep cerebellar nuclei neuron synapses, changes in presynaptic action potential waveform substantially contribute to synaptic depression.}, author = {Vandael, David H and Espinoza Martinez, Claudia and Jonas, Peter M}, journal = {Neuron}, number = {6}, pages = {1149 -- 1151}, publisher = {Elsevier}, title = {{Excitement about inhibitory presynaptic terminals}}, doi = {10.1016/j.neuron.2015.03.006}, volume = {85}, year = {2015}, }