@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{7406,
  abstract     = {Background
Synaptic vesicles (SVs) are an integral part of the neurotransmission machinery, and isolation of SVs from their host neuron is necessary to reveal their most fundamental biochemical and functional properties in in vitro assays. Isolated SVs from neurons that have been genetically engineered, e.g. to introduce genetically encoded indicators, are not readily available but would permit new insights into SV structure and function. Furthermore, it is unclear if cultured neurons can provide sufficient starting material for SV isolation procedures.

New method
Here, we demonstrate an efficient ex vivo procedure to obtain functional SVs from cultured rat cortical neurons after genetic engineering with a lentivirus.

Results
We show that ∼108 plated cortical neurons allow isolation of suitable SV amounts for functional analysis and imaging. We found that SVs isolated from cultured neurons have neurotransmitter uptake comparable to that of SVs isolated from intact cortex. Using total internal reflection fluorescence (TIRF) microscopy, we visualized an exogenous SV-targeted marker protein and demonstrated the high efficiency of SV modification.

Comparison with existing methods
Obtaining SVs from genetically engineered neurons currently generally requires the availability of transgenic animals, which is constrained by technical (e.g. cost and time) and biological (e.g. developmental defects and lethality) limitations.

Conclusions
These results demonstrate the modification and isolation of functional SVs using cultured neurons and viral transduction. The ability to readily obtain SVs from genetically engineered neurons will permit linking in situ studies to in vitro experiments in a variety of genetic contexts.},
  author       = {Mckenzie, Catherine and Spanova, Miroslava and Johnson, Alexander J and Kainrath, Stephanie and Zheden, Vanessa and Sitte, Harald H. and Janovjak, Harald L},
  issn         = {0165-0270},
  journal      = {Journal of Neuroscience Methods},
  pages        = {114--121},
  publisher    = {Elsevier},
  title        = {{Isolation of synaptic vesicles from genetically engineered cultured neurons}},
  doi          = {10.1016/j.jneumeth.2018.11.018},
  volume       = {312},
  year         = {2019},
}

@article{3517,
  abstract     = {A modular multichannel microdrive ('hyperdrive') is described. The microdrive uses printed circuit board technology and flexible fused silica capillaries. The modular design allows for the fabrication of 4-32 independently movable electrodes or `tetrodes'. The drives are re-usable and re-loading the drive with electrodes is simple. },
  author       = {Szabo, Imre and Czurkó, András and Csicsvari, Jozsef L and Hirase, Hajima and Leinekugel, Xavier and Buzsáki, György},
  issn         = {0165-0270},
  journal      = {Journal of Neuroscience Methods},
  number       = {1},
  pages        = {105 -- 110},
  publisher    = {Elsevier},
  title        = {{The application of printed circuit board technology for fabrication of multi-channel micro-drives}},
  doi          = {10.1016/S0165-0270(00)00362-9},
  volume       = {105},
  year         = {2001},
}