Biology subjects

Jun, J. J.

Publications and source records attributed to Jun, J. J..

4 recordsLinked to original sources

bioRxiv2020

SpikeForest: reproducible web-facing ground-truth validation of automated neural spike sorters

Spike sorting is a crucial but time-intensive step in electrophysiological studies of neuronal activity. While there are many popular software packages for spike sorting, there is little consensus about which are the most accurate under different experimental conditions. SpikeForest is an open-source and reproducible software suite that benchmarks the performance of automated spike sorting algorithms across an extensive, curated database of electrophysiological recordings with ground truth, displaying results interactively on a continuously-updating website. With contributions from over a dozen participating laboratories, our database currently comprises 650 recordings (1.3 TB total size) with around 35,000 ground-truth units. These data include extracellular recordings paired with intracellular voltages, state-of-the-art simulated recordings, and hybrid synthetic datasets. Ten of the most frequently used modern spike sorting codes are wrapped under a common Python framework and evaluated on a compute cluster using an automated pipeline. SpikeForest validates and documents community progress in automated spike sorting, and guides neuroscientists to an optimal choice of sorter and parameters for a wide range of probes and brain regions.

neuroscience↗

bioRxiv2018

Neural activity in a hippocampus-like region of the teleost pallium are associated with navigation and active sensing

Neural mechanisms underlying spatial navigation in fish are unknown and little is known, for any vertebrate, about the relationship between active sensing and the formation of spatial maps. The weakly electric fish, Gymnotus Carapo, uses their active electric sense for spatial navigation. The electric organ discharge rate (EODr) undergoes transient increases during navigation to enhance electrosensory sampling. Gymnotus also uses stereotyped forward/ backward swimming as a second form of active sensing that brings objects towards the electroreceptor-dense head region. We wirelessly recorded neural activity from the pallium of freely swimming Gymnotus. Spiking activity was sparse and occurred only during swimming. Notably, some units exhibited significant place specificity and/or association with both forms of active sensing. Our results provide the first characterization of neural activity in a hippocampal-like region of a teleost fish brain and connects active sensing via sensory sampling rate and directed movements to higher order encoding of spatial information.

neuroscience↗

bioRxiv2018

A novel time-stamp mechanism transforms egocentric encounters into an allocentric spatial representation

Learning the spatial organization of the environment is essential for most animals survival. This often requires the animal to derive allocentric information about the environment from egocentric sensory and motor experience. The neural circuits and mechanisms underlying this transformation are currently unknown. We addressed this problem in electric fish, which can precisely navigate in complete darkness and whose requisite brain circuitry is relatively simple. We conducted the first neural recordings in the preglomerular complex, the thalamic region exclusively connecting the optic tectum with the spatial learning circuits in the dorsolateral pallium. While tectal egocentric information was eliminated in preglomerular neurons, the time-intervals between object encounters were precisely encoded. We show that this highly-reliable temporal information, combined with a speed signal, can permit accurate path-integration that then enables computing allocentric spatial relations. Our results suggest that similar mechanisms are involved in spatial learning via sequential encounters in all vertebrates.

neuroscience↗

bioRxiv2017

Real-time spike sorting platform for high-density extracellular probes with ground-truth validation and drift correction

Electrical recordings from a large array of electrodes give us access to neural population activity with single-cell, single-spike resolution. These recordings contain extracellular spikes which must be correctly detected and assigned to individual neurons. Despite numerous spike-sorting techniques developed in the past, a lack of high-quality ground-truth datasets hinders the validation of spike-sorting approaches. Furthermore, existing approaches requiring manual corrections are not scalable for hours of recordings exceeding 100 channels. To address these issues, we built a comprehensive spike-sorting pipeline that performs reliably under noise and probe drift by incorporating covariance-based features and unsupervised clustering based on fast density-peak finding. We validated performance of our workflow using multiple ground-truth datasets that recently became available. Our software scales linearly and processes up to 1000-channel recording in real-time using a single workstation. Accurate, real-time spike sorting from large recording arrays will enable more precise control of closed-loop feedback experiments and brain-computer interfaces.

neuroscience↗