Sharp wave‐associated activity patterns of cortical neurons in the mouse piriform cortex

Abstract

The olfactory piriform cortex (PC) is thought to participate in olfactory associative memory. Like the hippocampus, which is essential for episodic memory, it belongs to an evolutionally conserved paleocortex and comprises a three-layered cortical structure. During slow-wave sleep, the olfactory PC becomes less responsive to external odor stimuli and instead displays sharp wave (SPW) activity similar to that observed in the hippocampus. Neural activity patterns during hippocampal SPW have been intensively studied in terms of memory consolidation; however, little is known about the activity patterns of olfactory cortical neurons during olfactory cortex sharp waves (OC-SPWs). In this study, we recorded multi-unit neural activities in the anterior PC in urethane-anesthetized mice. We found that the activity patterns of olfactory cortical neurons during OC-SPWs were non-randomly organized. Individual olfactory cortical neurons varied in the timings of their peak firing rates during OC-SPW events. Moreover, specific pairs of olfactory cortical neurons were more frequently activated together than expected by chance. On the basis of these observations, we speculate that coordinated activation of specific subsets of olfactory cortical neurons repeats during OC-SPWs, thereby facilitating synaptic plasticity underlying the consolidation of olfactory associative memories.

LRP4 mediated synaptic plasticity in the piriform cortex

“…As a higher olfactory center receiving direct input from the olfactory bulb, the piriform cortex comprises the largest area of the olfactory cortex, which is vital for olfactory function processing its connection with all of the entorhinal cortical domains [27,28]. Like the hippocampus, the piriform cortex with a three-layered cortical structure belongs to an evolutionally conserved paleocortex, phylogenetically ancient structure [26,29].…”

Measuring Events: An Investigation into the Stability of Event Segmentation Patterns Across Groups

“…The first measure, agreement index, has been commonly used in prior research (Kurby & Zacks, 2011). We applied the second measure, the surprise index (Katori et al, 2018), to segmentation data for the first time to evaluate whether it provides a more stable index of individual-group agreement. To calculate agreement index , we transformed individual button press data into a time series indicating the presence or absence of a button press in 1s long bins (individual timeseries), as is standard for this measure.…”

“…This measure reflects the extent to which an individual's button presses are expected, given the normative boundaries identified by an independent group. It is adapted from work that estimates the probability of spike co-occurrence across two neurons using the Poisson distribution (Katori et al, 2018). We used the surprise index to capture the cooccurrence of an individual's button presses with a group's normative boundaries.…”

“…We then calculated k as the number of times the individual's boundaries overlapped with (i.e., fell within 1 s of) a group normative boundary and rarity of such overlap as the summed probability of overlap occurring more than k-number of times. Surprise index was then calculated by -log2 transforming rarity (Katori et al, 2018).…”

“…Measures were selected based on their ability to capture the expected reproducibility of group level boundaries as well as the degree to which an individual's performance is consistent with group performance. Specifically, these measures quantified agreement in boundary placement within a group of individuals (peakiness), between two groups of individuals (peak-to-peak distance), and between an individual and a separate group (surprise index, adapted from Katori et al, 2018 ;and agreement index, Zacks et al, 2006). We asked how each of these agreement measures perform according to three criteria: 1) the ability to distinguish segmentation behavior MEASURING EVENTS 7 from noise; 2) the ability to distinguish segmentation of one movie from another; and 3) the sample size needed for the measure to stabilize and show little improvement with additional participants.…”