@article{20099,
  abstract     = {The hippocampus, critical for learning and memory, is dogmatically described as a trisynaptic circuit where dentate gyrus granule cells (GCs), CA3 pyramidal neurons (PNs), and CA1 PNs are serially connected. However, CA3 also forms an autoassociative network, and its PNs have diverse morphologies, intrinsic properties, and GC input levels. How PN subtypes compose this recurrent network is unknown. To determine the synaptic arrangement of identified CA3 PNs, we combine multicellular patch-clamp recording and post hoc morphological analysis in mouse hippocampal slices. PNs can be divided into distinct “superficial” and “deep” subclasses, the latter including previously reported “athorny” cells. Subclasses have distinct input-output transformations and asymmetric connectivity, which is more abundant from superficial to deep PNs, splitting CA3 locally into two parallel recurrent networks. Coincident spontaneous inhibition occurs frequently within but not between subclasses, implying subclass-specific inhibitory innervation. Our results suggest two separately controlled sublayers for parallel information processing in hippocampal CA3.},
  author       = {Watson, Jake and Vargas Barroso, Victor M and Jonas, Peter M},
  issn         = {2211-1247},
  journal      = {Cell Reports},
  number       = {8},
  publisher    = {Elsevier},
  title        = {{Cell-specific wiring routes information flow through hippocampal CA3}},
  doi          = {10.1016/j.celrep.2025.116080},
  volume       = {44},
  year         = {2025},
}

@article{20457,
  abstract     = {Patch-clamp recording of miniature postsynaptic currents (mPSCs, or ‘minis’) is used extensively to investigate the functional properties of synapses. With this approach, spontaneous synaptic transmission events are recorded in an attempt to determine quantal synaptic parameters or the effect of synaptic manipulations. However, at the majority of brain synapses these events are small, with many undetectable due to recording noise. The effects of incomplete detection were well appreciated in the early years of synaptic physiology analysis, but appear to be increasingly forgotten. Here we sought to characterise the consequences of incomplete detection on the interpretability of mini analysis, using simulated mPSC data to give full control over event parameters. We demonstrate that commonly reported measures such as mean event amplitude and frequency, are misrepresented by the loss of undetected events. Probabilistic loss of small events results in detected event amplitude distributions that appear biologically complete, yet do not reflect the underlying synaptic properties. With both simulated and experimental datasets, we demonstrate that specific changes in event amplitude are primarily detected as changes in frequency, compromising classical biological interpretations. To facilitate more robust data analysis and interpretation, we detail a means for experimental estimation of the event detection limit and provide practical recommendations for data analysis. Together, our study highlights how mini analysis is prone to falsely reporting synaptic changes, raising awareness of these considerations, and provides a framework for more robust data analysis and interpretation.},
  author       = {Greger, Ingo H. and Watson, Jake},
  issn         = {1469-7793},
  journal      = {Journal of Physiology},
  number       = {22},
  pages        = {7189--7205},
  publisher    = {Wiley},
  title        = {{‘Mini analysis’ misrepresents changes in synaptic properties due to incomplete event detection}},
  doi          = {10.1113/JP288183},
  volume       = {603},
  year         = {2025},
}

@article{18879,
  abstract     = {Our brain has remarkable computational power, generating sophisticated behaviors, storing memories over an individual’s lifetime, and producing higher cognitive functions. However, little of our neuroscience knowledge covers the human brain. Is this organ truly unique, or is it a scaled version of the extensively studied rodent brain? Combining multicellular patch-clamp recording with expansion-based superresolution microscopy and full-scale modeling, we determined the cellular and microcircuit properties of the human hippocampal CA3 region, a fundamental circuit for memory storage. In contrast to neocortical networks, human hippocampal CA3 displayed sparse connectivity, providing a circuit architecture that maximizes associational power. Human synapses showed unique reliability, high precision, and long integration times, exhibiting both species- and circuit-specific properties. Together with expanded neuronal numbers, these circuit characteristics greatly enhanced the memory storage capacity of CA3. Our results reveal distinct microcircuit properties of the human hippocampus and begin to unravel the inner workings of our most complex organ. },
  author       = {Watson, Jake and Vargas Barroso, Victor M and Morse, Rebecca and Navas Olivé, Andrea C and Tavakoli, Mojtaba and Danzl, Johann G and Tomschik, Matthias and Rössler, Karl and Jonas, Peter M},
  issn         = {1097-4172},
  journal      = {Cell},
  number       = {2},
  pages        = {501--514.e18},
  publisher    = {Elsevier},
  title        = {{Human hippocampal CA3 uses specific functional connectivity rules for efficient associative memory}},
  doi          = {10.1016/j.cell.2024.11.022},
  volume       = {188},
  year         = {2025},
}

@unpublished{18688,
  abstract     = {The human brain has remarkable computational power. It generates sophisticated behavioral sequences, stores engrams over an individual’s lifetime, and produces higher cognitive functions up to the level of consciousness. However, so little of our neuroscience knowledge covers the human brain, and it remains unknown whether this organ is truly unique, or is a scaled version of the extensively studied rodent brain. To address this fundamental question, we determined the cellular, synaptic, and connectivity rules of the hippocampal CA3 recurrent circuit using multicellular patch clamp-recording. This circuit is the largest autoassociative network in the brain, and plays a key role in memory and higher-order computations such as pattern separation and pattern completion. We demonstrate that human hippocampal CA3 employs sparse connectivity, in stark contrast to neocortical recurrent networks. Connectivity sparsifies from rodents to humans, providing a circuit architecture that maximizes associational power. Unitary synaptic events at human CA3–CA3 synapses showed both distinct species-specific and circuit-dependent properties, with high reliability, unique amplitude precision, and long integration times. We also identify differential scaling rules between hippocampal pathways from rodents to humans, with a moderate increase in the convergence of CA3 inputs per cell, but a marked increase in human mossy fiber innervation. Anatomically guided full-scale modeling suggests that the human brain’s sparse connectivity, expanded neuronal number, and reliable synaptic signaling combine to enhance the associative memory storage capacity of CA3. Together, our results reveal unique rules of connectivity and synaptic signaling in the human hippocampus, demonstrating the absolute necessity of human brain research and beginning to unravel the remarkable performance of our autoassociative memory circuits.},
  author       = {Watson, Jake F. and Vargas-Barroso, Victor and Morse-Mora, Rebecca J. and Navas-Olive, Andrea and Tavakoli, Mojtaba and Danzl, Johann G and Tomschik, Matthias and Rössler, Karl and Jonas, Peter M},
  booktitle    = {bioRxiv},
  title        = {{Human hippocampal CA3 uses specific functional connectivity rules for efficient associative memory}},
  doi          = {10.1101/2024.05.02.592169},
  year         = {2024},
}

@article{14257,
  abstract     = {Mapping the complex and dense arrangement of cells and their connectivity in brain tissue demands nanoscale spatial resolution imaging. Super-resolution optical microscopy excels at visualizing specific molecules and individual cells but fails to provide tissue context. Here we developed Comprehensive Analysis of Tissues across Scales (CATS), a technology to densely map brain tissue architecture from millimeter regional to nanometer synaptic scales in diverse chemically fixed brain preparations, including rodent and human. CATS uses fixation-compatible extracellular labeling and optical imaging, including stimulated emission depletion or expansion microscopy, to comprehensively delineate cellular structures. It enables three-dimensional reconstruction of single synapses and mapping of synaptic connectivity by identification and analysis of putative synaptic cleft regions. Applying CATS to the mouse hippocampal mossy fiber circuitry, we reconstructed and quantified the synaptic input and output structure of identified neurons. We furthermore demonstrate applicability to clinically derived human tissue samples, including formalin-fixed paraffin-embedded routine diagnostic specimens, for visualizing the cellular architecture of brain tissue in health and disease.},
  author       = {Michalska, Julia M and Lyudchik, Julia and Velicky, Philipp and Korinkova, Hana and Watson, Jake and Cenameri, Alban and Sommer, Christoph M and Amberg, Nicole and Venturino, Alessandro and Roessler, Karl and Czech, Thomas and Höftberger, Romana and Siegert, Sandra and Novarino, Gaia and Jonas, Peter M and Danzl, Johann G},
  issn         = {1546-1696},
  journal      = {Nature Biotechnology},
  pages        = {1051--1064},
  publisher    = {Springer Nature},
  title        = {{Imaging brain tissue architecture across millimeter to nanometer scales}},
  doi          = {10.1038/s41587-023-01911-8},
  volume       = {42},
  year         = {2024},
}

@misc{13126,
  abstract     = {Mapping the complex and dense arrangement of cells and their connectivity in brain tissue demands nanoscale spatial resolution imaging. Super-resolution optical microscopy excels at visualizing specific molecules and individual cells but fails to provide tissue context. Here, we developed Comprehensive Analysis of Tissues across Scales (CATS), a technology to densely map brain tissue architecture from millimeter regional to nanometer synaptic scales in diverse chemically fixed brain preparations, including rodent and human. CATS uses fixation-compatible extracellular labeling and optical imaging, including stimulated emission depletion or expansion microscopy, to comprehensively delineate cellular structures. It enables three-dimensional reconstruction of single synapses and mapping of synaptic connectivity by identification and analysis of putative synaptic cleft regions. Applying CATS to the mouse hippocampal mossy fiber circuitry, we reconstructed and quantified the synaptic input and output structure of identified neurons. We furthermore demonstrate applicability to clinically derived human tissue samples, including formalin-fixed paraffin-embedded routine diagnostic specimens, for visualizing the cellular architecture of brain tissue in health and disease.},
  author       = {Danzl, Johann G},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Research data for the publication "Imaging brain tissue architecture across millimeter to nanometer scales"}},
  doi          = {10.15479/AT:ISTA:13126},
  year         = {2023},
}

@article{13267,
  abstract     = {Three-dimensional (3D) reconstruction of living brain tissue down to an individual synapse level would create opportunities for decoding the dynamics and structure–function relationships of the brain’s complex and dense information processing network; however, this has been hindered by insufficient 3D resolution, inadequate signal-to-noise ratio and prohibitive light burden in optical imaging, whereas electron microscopy is inherently static. Here we solved these challenges by developing an integrated optical/machine-learning technology, LIONESS (live information-optimized nanoscopy enabling saturated segmentation). This leverages optical modifications to stimulated emission depletion microscopy in comprehensively, extracellularly labeled tissue and previous information on sample structure via machine learning to simultaneously achieve isotropic super-resolution, high signal-to-noise ratio and compatibility with living tissue. This allows dense deep-learning-based instance segmentation and 3D reconstruction at a synapse level, incorporating molecular, activity and morphodynamic information. LIONESS opens up avenues for studying the dynamic functional (nano-)architecture of living brain tissue.},
  author       = {Velicky, Philipp and Miguel Villalba, Eder and Michalska, Julia M and Lyudchik, Julia and Wei, Donglai and Lin, Zudi and Watson, Jake and Troidl, Jakob and Beyer, Johanna and Ben Simon, Yoav and Sommer, Christoph M and Jahr, Wiebke and Cenameri, Alban and Broichhagen, Johannes and Grant, Seth G.N. and Jonas, Peter M and Novarino, Gaia and Pfister, Hanspeter and Bickel, Bernd and Danzl, Johann G},
  issn         = {1548-7105},
  journal      = {Nature Methods},
  pages        = {1256--1265},
  publisher    = {Springer Nature},
  title        = {{Dense 4D nanoscale reconstruction of living brain tissue}},
  doi          = {10.1038/s41592-023-01936-6},
  volume       = {20},
  year         = {2023},
}