@article{9329,
  abstract     = {Background: To understand information coding in single neurons, it is necessary to analyze subthreshold synaptic events, action potentials (APs), and their interrelation in different behavioral states. However, detecting excitatory postsynaptic potentials (EPSPs) or currents (EPSCs) in behaving animals remains challenging, because of unfavorable signal-to-noise ratio, high frequency, fluctuating amplitude, and variable time course of synaptic events.
New method: We developed a method for synaptic event detection, termed MOD (Machine-learning Optimal-filtering Detection-procedure), which combines concepts of supervised machine learning and optimal Wiener filtering. Experts were asked to manually score short epochs of data. The algorithm was trained to obtain the optimal filter coefficients of a Wiener filter and the optimal detection threshold. Scored and unscored data were then processed with the optimal filter, and events were detected as peaks above threshold.
Results: We challenged MOD with EPSP traces in vivo in mice during spatial navigation and EPSC traces in vitro in slices under conditions of enhanced transmitter release. The area under the curve (AUC) of the receiver operating characteristics (ROC) curve was, on average, 0.894 for in vivo and 0.969 for in vitro data sets, indicating high detection accuracy and efficiency.
Comparison with existing methods: When benchmarked using a (1 − AUC)−1 metric, MOD outperformed previous methods (template-fit, deconvolution, and Bayesian methods) by an average factor of 3.13 for in vivo data sets, but showed comparable (template-fit, deconvolution) or higher (Bayesian) computational efficacy.
Conclusions: MOD may become an important new tool for large-scale, real-time analysis of synaptic activity.},
  author       = {Zhang, Xiaomin and Schlögl, Alois and Vandael, David H and Jonas, Peter M},
  issn         = {1872-678X},
  journal      = {Journal of Neuroscience Methods},
  number       = {6},
  publisher    = {Elsevier},
  title        = {{MOD: A novel machine-learning optimal-filtering method for accurate and efficient detection of subthreshold synaptic events in vivo}},
  doi          = {10.1016/j.jneumeth.2021.109125},
  volume       = {357},
  year         = {2021},
}

@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},
}

@article{8001,
  abstract     = {Post-tetanic potentiation (PTP) is an attractive candidate mechanism for hippocampus-dependent short-term memory. Although PTP has a uniquely large magnitude at hippocampal mossy fiber-CA3 pyramidal neuron synapses, it is unclear whether it can be induced by natural activity and whether its lifetime is sufficient to support short-term memory. We combined in vivo recordings from granule cells (GCs), in vitro paired recordings from mossy fiber terminals and postsynaptic CA3 neurons, and “flash and freeze” electron microscopy. PTP was induced at single synapses and showed a low induction threshold adapted to sparse GC activity in vivo. PTP was mainly generated by enlargement of the readily releasable pool of synaptic vesicles, allowing multiplicative interaction with other plasticity forms. PTP was associated with an increase in the docked vesicle pool, suggesting formation of structural “pool engrams.” Absence of presynaptic activity extended the lifetime of the potentiation, enabling prolonged information storage in the hippocampal network.},
  author       = {Vandael, David H and Borges Merjane, Carolina and Zhang, Xiaomin and Jonas, Peter M},
  issn         = {10974199},
  journal      = {Neuron},
  number       = {3},
  pages        = {509--521},
  publisher    = {Elsevier},
  title        = {{Short-term plasticity at hippocampal mossy fiber synapses is induced by natural activity patterns and associated with vesicle pool engram formation}},
  doi          = {10.1016/j.neuron.2020.05.013},
  volume       = {107},
  year         = {2020},
}

@article{8261,
  abstract     = {Dentate gyrus granule cells (GCs) connect the entorhinal cortex to the hippocampal CA3 region, but how they process spatial information remains enigmatic. To examine the role of GCs in spatial coding, we measured excitatory postsynaptic potentials (EPSPs) and action potentials (APs) in head-fixed mice running on a linear belt. Intracellular recording from morphologically identified GCs revealed that most cells were active, but activity level varied over a wide range. Whereas only ∼5% of GCs showed spatially tuned spiking, ∼50% received spatially tuned input. Thus, the GC population broadly encodes spatial information, but only a subset relays this information to the CA3 network. Fourier analysis indicated that GCs received conjunctive place-grid-like synaptic input, suggesting code conversion in single neurons. GC firing was correlated with dendritic complexity and intrinsic excitability, but not extrinsic excitatory input or dendritic cable properties. Thus, functional maturation may control input-output transformation and spatial code conversion.},
  author       = {Zhang, Xiaomin and Schlögl, Alois and Jonas, Peter M},
  issn         = {0896-6273},
  journal      = {Neuron},
  number       = {6},
  pages        = {1212--1225},
  publisher    = {Elsevier},
  title        = {{Selective routing of spatial information flow from input to output in hippocampal granule cells}},
  doi          = {10.1016/j.neuron.2020.07.006},
  volume       = {107},
  year         = {2020},
}

@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},
}