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Kenchappa, S. C.

Publications and source records attributed to Kenchappa, S. C..

2 recordsLinked to original sources

Layer-specific reorganization of mnemonic representations in primate retrosplenial cortex during learning

Rapid learning of associations between co-occurring stimuli is essential for episodic memory formation. The retrosplenial cortex (RSC) is strongly interconnected with the hippocampus, and in rodents, the RSC has been shown to support spatial navigation and fear conditioning. Although lesion and neuroimaging studies in humans and macaques have further implicated the RSC in episodic memory, it is unclear how memory representations form and evolve in the RSC. Here we show that representations of memorized contexts in primate RSC form within minutes. These initial representations reorganize as the memory matures, with a shift in the weight of neuronal contributions from superficial to deep RSC layers across an hour and increased local connectivity between deep layer neurons. Because RSC superficial and deep layers represent input and output layers respectively, it suggests that hippocampal inputs provide context information to superficial layers during early learning, and this context information consolidates in deep RSC layers.

neuroscience↗

Real-time experimental control using network-based parallel processing

Modern neuroscience research often requires the coordination of multiple processes such as stimulus generation, real-time experimental control, as well as behavioral and neural measurements. The technical demands required to simultaneously manage these processes with high temporal fidelity limits the number of labs capable of performing such work. Here we present an open-source network-based parallel processing framework that eliminates these barriers. The Real-Time Experimental Control with Graphical User Interface (REC-GUI) framework offers multiple advantages: (i) a modular design agnostic to coding language(s) and operating system(s) that maximizes experimental flexibility and minimizes researcher effort, (ii) simple interfacing to connect measurement and recording devices, (iii) high temporal fidelity by dividing task demands across CPUs, and (iv) real-time control using a fully customizable and intuitive GUI. Testing results demonstrate that the REC-GUI framework facilitates technically demanding, behavior-contingent neuroscience research. Sample code and hardware configurations are downloadable, and future developments will be regularly released.

neuroscience↗