Nonspatial Sequence Coding in CA1 Neurons

Abstract

The hippocampus is critical to the memory for sequences of events, a defining feature of episodic memory. However, the fundamental neuronal mechanisms underlying this capacity remain elusive. While considerable research indicates hippocampal neurons can represent sequences of locations, direct evidence of coding for the memory of sequential relationships among nonspatial events remains lacking. To address this important issue, we recorded neural activity in CA1 as rats performed a hippocampus-dependent sequencememory task. Briefly, the task involves the presentation of repeated sequences of odors at a single port and requires rats to identify each item as "in sequence" or "out of sequence". We report that, while the animals' location and behavior remained constant, hippocampal activity differed depending on the temporal context of items-in this case, whether they were presented in or out of sequence. Some neurons showed this effect across items or sequence positions (general sequence cells), while others exhibited selectivity for specific conjunctions of item and sequence position information (conjunctive sequence cells) or for specific probe types (probe-specific sequence cells). We also found that the temporal context of individual trials could be accurately decoded from the activity of neuronal ensembles, that sequence coding at the single-cell and ensemble level was linked to sequence memory performance, and that slow-gamma oscillations (20 -40 Hz) were more strongly modulated by temporal context and performance than theta oscillations (4 -12 Hz). These findings provide compelling evidence that sequence coding extends beyond the domain of spatial trajectories and is thus a fundamental function of the hippocampus.

Nonspatial sequence coding varies along the CA1 transverse axis

“…To enhance the segmentation between each odor sequence (completed correctly or not), rats were required to run to the end of the track opposite the odor port before the next sequence could be presented ( Fig 1A), which allowed us to examine spatial coding properties in the same population of recorded neurons. To maximize our sample sizes, our analyses collapsed data from three separate sessions (Well-Trained, Novel1, and Novel2; see Allen et al, 2016), but data from a single session (WellTrained) is also included for comparison.…”

“…Since two 250 ms time windows were tested (-500 ms to -250 ms, -250 ms to 0 ms; relative to port withdrawal), we applied the Bonferroni correction, such that a comparison was considered statistically significant if this probability was < 0.025 (two-tailed). Note that the results presented hereafter were obtained using this random bin shuffling (as in Allen et al, 2016) but that, as a comparison, we also generated the distributions by permuting across trials only (preserving the order of the five bins within each trial) and observed nearly identical results (81 sequence cells, compared to 80 with the random bin permutations, with a 94.7% overlap in the cells identified), indicating that differences in activity were primarily accounted by trial type.…”

“…More specifically, the hippocampus, which receives a strong convergence of input from cortical regions, is thought to play a key role in associating information about specific events with the spatial and temporal contexts in which they occurred (Eichenbaum et al, 2012;Allen and Fortin, 2013;Howard and Eichenbaum, 2015;Knierim, 2015). Consistent with this view, neurons in hippocampal subregion CA1 have shown selectivity for specific stimuli, spatial locations, as well as specific time periods or temporal relationships (e.g., Wood et al, 1999;MacDonald et al, 2011;Igarashi et al, 2014b;Allen et al, 2016). Importantly, these different coding properties may not be uniformly distributed within CA1, as distal CA1 (toward subiculum) is more strongly associated with the lateral entorhinal cortex (LEC) and proximal CA1 (toward CA3) with the medial entorhinal cortex (MEC; Witter et al, 2000;Naber et al, 2001;Igarashi et al, 2014a;Cappaert et al, 2015;Knierim, 2015).…”

“…A total of 713 single units were recorded over 13 sessions (Well-Trained: n = 5 rats; Novel1: n = 4; Novel2: n =4). Units were classified as putative pyramidal neurons (n = 599) and interneurons (n = 114) by previously identified characteristic firing rates and valley-to-peak spike widths (Csicsvari et al, 1998(Csicsvari et al, , 1999Mizuseki and Buzsáki, 2013;Allen et al, 2016), with both types of neurons included in our analyses. To confirm recording sites, current was passed through the electrodes before perfusion (0.9% PBS followed by 4% paraformaldehyde) to produce small marking lesions, which were subsequently localized on Nissl-stained tissue slices.…”

“…To match the overall proportions reported in our previous study , we combined the data from the three recording sessions and used the same conservative criterion to identify sequence cells. Under this conservative criterion, only OutSeq trials with odor sampling periods of at least 500 ms are included in the analyses, which excludes many correctly identified OutSeq trials, but ensures that differences are not simply due to limited sampling or to potential differences in internal motor dynamics or state immediately preceding the motor response (see Allen et al, 2016).…”