Biology subjects

Souza, B. C.

Publications and source records attributed to Souza, B. C..

3 recordsLinked to original sources

bioRxiv2022

Coexisting persistent and dynamic representations of stimulus, time and trial outcome emerge in the hippocampus-prefrontal circuitry during learning of a temporal association

Temporal associations between sensory stimuli separated in time rely on the interaction between the hippocampus and medial prefrontal cortex (mPFC). However, it is not known how changes in their neural activity support the emergence of temporal association learning. Here, we use simultaneous electrophysiological recordings in the hippocampal CA1 region and mPFC of mice to elucidate the neural dynamics underlying memory formation in an auditory trace conditioning task. We found that in both areas conditioned (CS+/CS-) and unconditioned stimuli (US) evoked similar temporal sequences of neural responses that progressively diverged during learning. Additionally, persistent CS representations emerged in mPFC after learning, supported by CS+ coding states whose transient reactivation reliably predicted lick onset and behavioral performance on single trials. These results show that coordination of temporal sequences in CA1 and persistent activity in mPFC may underlie temporal association learning, and that transient reactivations of engrams in mPFC predict the animal behavior. HighlightsO_LITemporal representation of CS+, but not CS- and US, strengthens in CA1 after learning. C_LIO_LISimilarity between stimulus and reward temporal representations decrease with learning, but is recovered in error trials. C_LIO_LIRepresentation of stimulus identity is strong and stable in PFC since stimulus onset, while it only emerges in CA1 during trace period. C_LIO_LINeural states defined on faster time-scales reveal the emergence of CS coding states in PFC whose onset predicts lick times and task performance. C_LIO_LIPFC-CA1 states do not increase coordination during late CS+ stimulus and trace. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/497926v2_fig0.gif" ALT="Figure 0"> View larger version (32K): org.highwire.dtl.DTLVardef@93f7a7org.highwire.dtl.DTLVardef@6804a0org.highwire.dtl.DTLVardef@107a6a2org.highwire.dtl.DTLVardef@14cfb23_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 0:C_FLOATNO Graphical Abstract C_FIG

neuroscience↗

bioRxiv2020

Learning differentially shapes prefrontal and hippocampal activity patterns during classical conditioning

The ability to use sensory cues to inform goal directed actions is a critical component of intelligent behavior. To study how sound cues are translated into anticipatory licking during classical appetitive conditioning, we employed high-density electrophysiological recordings from the hippocampal CA1 area and the prefrontal cortex (PFC). CA1 and PFC neurons undergo distinct learning dependent changes at the single cell level and maintain representations of cue identity during anticipatory behavior at the population level. In addition, reactivation of task-related neuronal assemblies during hippocampal awake Sharp-Wave Ripples (aSWR) changed within individual sessions in CA1 and over the course of multiple sessions in PFC. Despite both areas being highly engaged and synchronized during the task, we found no evidence for coordinated single cell or assembly activity during conditioning trials or aSWR.

neuroscience↗

bioRxiv2018

Spike sorting with Gaussian mixture models

The shape of extracellularly recorded action potentials is a product of several variables, such as the biophysical and anatomical properties of the neuron and the relative position of the electrode. This allows for isolating spikes of different neurons recorded in the same channel into clusters based on waveform features. However, correctly classifying spike waveforms into their underlying neuronal sources remains a main challenge. This process, called spike sorting, typically consists of two steps: (1) extracting relevant waveform features (e.g., height, width), and (2) clustering them into non-overlapping groups believed to correspond to different neurons. In this study, we explored the performance of Gaussian mixture models (GMMs) in these two steps. We extracted relevant waveform features using a combination of common techniques (e.g., principal components and wavelets) and GMM fitting parameters (e.g., standard deviations and peak distances). Then, we developed an approach to perform unsupervised clustering using GMMs, which estimates cluster properties in a data-driven way. Our results show that the proposed GMM-based framework outperforms previously established methods when using realistic simulations of extracellular spikes and actual extracellular recordings to evaluate sorting performance. We also discuss potentially better techniques for feature extraction than the widely used principal components. Finally, we provide a friendly graphical user interface in MATLAB to run our algorithm, which allows for manual adjustment of the automatic results.

neuroscience↗