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Jun, J. J.

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

2 recordsLinked to original sources

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

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