Biology subjects

Priestley, J. B.

Publications and source records attributed to Priestley, J. B..

5 recordsLinked to original sources

bioRxiv2026

CICADA: A unified framework for NWB-based neurophysiological data analysis

Neurophysiology datasets are becoming increasingly complex, combining behavioral measurements with high-dimensional neuronal activity recordings coming from optical and/or electrophysiological measurements. The Neurodata Without Borders (NWB) standard has emerged in the community as the format of record. While standardized and widely used preprocessing tools generating NWB files have been developed, extensible frameworks for scientific analysis downstream of the NWB ecosystem are still under-represented. We present CICADA, a Python framework dedicated to analysis of neurophysiological data in the standardized NWB format. The toolbox is built as three hierarchically-organized packages: cicada-nwb (NWB access layer), cicada-analysis (plugin-based analysis engine and tool library), and cicada-gui (PyQt5 desktop application at the head of the pipeline). Beyond this architectural separation, CICADA is built around a central design principle: supporting a continuum from turnkey use to full modularity. Researchers can use the complete GUI-driven cicada-gui workflow without writing code, programmatically use existing analysis plugins from cicada-analysis, contribute to new analysis plugins, reuse utilities from cicada-tools, or build entirely custom pipelines on top of the cicada-nwb access layer alone. The same analysis plugin runs identically in interactive GUI and parameter-configured headless modes, enabling reproducible multi-session, multi-animal group analyses. We illustrate the versatility of CICADA with example analyses of behavioral, calcium imaging (two-photon and widefield) and extracellular electrophysiology datasets from rodent laboratories. CICADA is open source, actively maintained, and designed so that any laboratory can contribute at any level of the stack without modifying the core framework.

neuroscience↗

bioRxiv2025

Hippocampal sequences traverse a memory space

Highly reliable sequential dynamics organize neural activity in the hippocampus and progress with changes in physical, sensory, or cognitive variables, typified by place cells during active exploration. But sensory and behavioral variables are often confounded, and so it remains intensely debated whether hippocampal sequences are driven by sensory content directly or rather reflect the alignment of internally generated dynamics to external perception. Here we used mouse virtual reality to characterize hippocampal dynamics in environments of systematically different sensory complexity, and to test the robustness of neural sequences to manipulations of exact sensory content. The structure of neural sequences was perturbed by changes in both moment-to-moment sensation and ongoing behavior, in a pattern that was best explained by the different memory strategies employed by the animal in each context. Neural subsequences could be flexibly interrupted, inserted, and resumed wherever a memory of prior experience was violated, indicating that the propagation of hippocampal dynamics was not rigidly fixed by internal networks and initial conditions. Rather, we argue that hippocampal sequences explore an abstract memory space of selectively remembered experiences, which flexibly encompasses both sensory and internal factors in a context-dependent manner.

neuroscience↗

bioRxiv2023

behaviorMate: An Intranet of Things Approach for Adaptable Control of Behavioral and Navigation-Based Experiments

1Investigators conducting behavioral experiments often need precise control over the timing of the delivery of stimuli to subjects and to collect the precise times of the subsequent behavioral responses. Furthermore, investigators want fine-tuned control over how various multi-modal cues are presented. behaviorMate takes an "Intranet of Things" approach, using a networked system of hardware and software components for achieving these goals. The system outputs a file with integrated timestamp-event pairs that investigators can then format and process using their own analysis pipelines. We present an overview of the electronic components and GUI application that make up behaviorMate as well as mechanical designs for compatible experimental rigs to provide the reader with the ability to set up their own system. A wide variety of paradigms are supported, including goal-oriented learning, random foraging, and context switching. We demonstrate behaviorMates utility and reliability with a range of use cases from several published studies and benchmark tests. Finally, we present experimental validation demonstrating different modalities of hippocampal place field studies. Both treadmill with burlap belt and virtual reality with running wheel paradigms were performed to confirm the efficacy and flexibility of the approach. Previous solutions rely on proprietary systems that may have large upfront costs or present frameworks that require customized software to be developed. behaviorMate uses open-source software and a flexible configuration system to mitigate both concerns. behaviorMate has a proven record for head-fixed imaging experiments and could be easily adopted for task control in a variety of experimental situations.

neuroscience↗

bioRxiv2021

Signatures of rapid synaptic learning in the hippocampus during novel experiences

Neurons in the hippocampus exhibit striking selectivity for specific combinations of sensory features, forming representations which are thought to subserve episodic memory. Even during a completely novel experience, ensembles of hippocampal "place cells" are rapidly configured such that the population sparsely encodes visited locations, stabilizing within minutes of the first exposure to a new environment. What cellular mechanisms enable this fast encoding of experience? Here we leverage virtual reality and large scale neural recordings to dissect the effects of novelty and experience on the dynamics of place field formation. We show that the place fields of many CA1 neurons transiently shift locations and modulate the amplitude of their activity immediately after place field formation, consistent with rapid plasticity mechanisms driven by plateau potentials and somatic burst spiking. These motifs were particularly enriched during initial exploration of a novel context and decayed with experience. Our data suggest that novelty modulates the effective learning rate in CA1, favoring burst-driven field formation to support fast synaptic updating during new experience.

neuroscience↗

bioRxiv2019

Hippocampal network reorganization underlies the formation of a temporal association memory

Episodic memory requires linking events in time, a function dependent on the hippocampus. In \"trace\" fear conditioning, animals learn to associate a neutral cue with an aversive stimulus despite their separation in time by a delay period on the order of tens of seconds. But how this temporal association forms remains unclear. Here we use 2-photon calcium imaging to track neural population dynamics over the complete time-course of learning and show that, in contrast to previous theories, the hippocampus does not generate persistent activity to bridge the time delay. Instead, learning is concomitant with broad changes in the active neural population in CA1. While neural responses were highly stochastic in time, cue identity could be reliably read out from population activity rates over longer timescales after learning. These results question the ubiquity of neural sequences during temporal association learning, and suggest that trace fear conditioning relies on mechanisms that differ from persistent activity accounts of working memory.

neuroscience↗