@article {49329, title = {Kilohertz frame-rate two-photon tomography.}, journal = {Nature Methods}, volume = {16}, year = {2019}, month = {2019 Jul 29}, pages = {778-86}, abstract = {

Point-scanning two-photon microscopy enables high-resolution imaging within scattering specimens such as the mammalian brain, but sequential acquisition of voxels fundamentally limits imaging speed. We developed a two-photon imaging technique that scans lines of excitation across a focal plane at multiple angles and uses prior information to recover high-resolution images at over 1.4 billion voxels per second. Using a structural image as a prior for recording neural activity, we imaged visually-evoked and spontaneous glutamate release across hundreds of dendritic spines in mice at depths over 250 microns and frame-rates over 1 kHz. Dendritic glutamate transients in anaesthetized mice are synchronized within spatially-contiguous domains spanning tens of microns at frequencies ranging from 1-100 Hz. We demonstrate high-speed recording of acetylcholine and calcium sensors, 3D single-particle tracking, and imaging in densely-labeled cortex. Our method surpasses limits on the speed of raster-scanned imaging imposed by fluorescence lifetime.

}, doi = {10.1101/357269}, author = {Kazemipour, Abbas and Novak, Ondrej and Flickinger, Daniel and Marvin, Jonathan S and Abdelfattah, Ahmed S and King, J. and Borden, PM and Kim, JJ and Al-Abdullatif, SH and Deal, PE and Miller, EW and Schreiter, ER and Druckmann, S and Svoboda, K and Looger, LL and Podgorski, K} } @article {6714, title = {Mapping mammalian synaptic connectivity.}, journal = {Cellular and Molecular Life Sciences: CMLS}, volume = {70}, year = {2013}, month = {2013 Dec}, pages = {4747-57}, abstract = {

Mapping mammalian synaptic connectivity has long been an important goal of neuroscientists since it is considered crucial for explaining human perception and behavior. Yet, despite enormous efforts, the overwhelming complexity of the neural circuitry and the lack of appropriate techniques to unravel it have limited the success of efforts to map connectivity. However, recent technological advances designed to overcome the limitations of conventional methods for connectivity mapping may bring about a turning point. Here, we address the promises and pitfalls of these new mapping technologies.

}, keywords = {Animals, Brain, Computational Biology, Connectome, Humans, Mammals, Nervous System Diseases, Synapses}, issn = {1420-9071}, doi = {10.1007/s00018-013-1417-y}, author = {Yook, Chaehyun and Druckmann, S and Kim, Jinhyun} } @article {5659, title = {A hierarchical structure of cortical interneuron electrical diversity revealed by automated statistical analysis.}, journal = {Cerebral Cortex}, volume = {23}, year = {2012}, month = {2012 Sep 17}, pages = {2994-3006}, abstract = {

Although the diversity of cortical interneuron electrical properties is well recognized, the number of distinct electrical types (e-types) is still a matter of debate. Recently, descriptions of interneuron variability were standardized by multiple laboratories on the basis of a subjective classification scheme as set out by the Petilla convention (Petilla Interneuron Nomenclature Group, PING). Here, we present a quantitative, statistical analysis of a database of nearly five hundred neurons manually annotated according to the PING nomenclature. For each cell, 38 features were extracted from responses to suprathreshold current stimuli and statistically analyzed to examine whether cortical interneurons subdivide into e-types. We showed that the partitioning into different e-types is indeed the major component of data variability. The analysis suggests refining the PING e-type classification to be hierarchical, whereby most variability is first captured within a coarse subpartition, and then subsequently divided into finer subpartitions. The coarse partition matches the well-known partitioning of interneurons into fast spiking and adapting cells. Finer subpartitions match the burst, continuous, and delayed subtypes. Additionally, our analysis enabled the ranking of features according to their ability to differentiate among e-types. We showed that our quantitative e-type assignment is more than 90\% accurate and manages to catch several human errors.

}, issn = {1460-2199}, doi = {10.1093/cercor/bhs290}, author = {Druckmann, S and Hill, Sean and Sch{\"u}rmann, Felix and Markram, Henry and Segev, Idan} } @article {5658, title = {Neuronal circuits underlying persistent representations despite time varying activity.}, journal = {Current Biology}, volume = {22}, year = {2012}, month = {2012 Nov 20}, pages = {2095-103}, abstract = {

Our brains are capable of remarkably stable stimulus representations despite time-varying neural activity. For instance, during delay periods in working memory tasks, while stimuli are represented in working memory, neurons in the prefrontal cortex, thought to support the memory representation, exhibit time-varying neuronal activity. Since neuronal activity encodes the stimulus, its time-varying dynamics appears to be paradoxical and incompatible with stable network stimulus representations. Indeed, this finding raises a fundamental question: can stable representations only be encoded with stable neural activity, or, its corollary, is every change in activity a sign of change in stimulus representation?

}, issn = {1879-0445}, doi = {10.1016/j.cub.2012.08.058}, author = {Druckmann, S and Chklovskii, Dmitri B} } @proceedings {5829, title = {Over-complete representations on recurrent neural networks can support persistent percepts.}, volume = {23}, year = {2010}, month = {2010}, pages = {541-9}, publisher = {MIT Press}, abstract = {

A striking aspect of cortical neural networks is the divergence of a relatively small number of input channels from the peripheral sensory apparatus into a large number of cortical neurons, an over-complete representation strategy. Cortical neurons are then connected by a sparse network of lateral synapses. Here we propose that such architecture may increase the persistence of the representation of an incoming stimulus, or a percept. We demonstrate that for a family of networks in which the receptive field of each neuron is re-expressed by its outgoing connections, a represented percept can remain constant despite changing activity. We term this choice of connectivity REceptive FIeld REcombination (REFIRE) networks. The sparse REFIRE network may serve as a high-dimensional integrator and a biologically plausible model of the local cortical circuit.

}, author = {Druckmann, S and Chklovskii, D} }