DOI: 10.5061/dryad.x0k6djhwb
title: Impact of the entorhinal feed-forward connection to the CA3 on hippocampal coding

description: Each sub-region of the hippocampus plays a critical computational role in
 the formation of episodic learning and memory, but studies have yet to
 show and interpret the individual spiking dynamics of each region and how
 that information is passed between each subregion. This is in part due to
 the difficulty in accessing individual communicating axons. Here, we
 created a novel microfluidic device that facilitates network growth of
 four separated hippocampal subregions over a micro-electrode array. This
 device enabled monitoring single axons over two electrodes so direction of
 spike propagation in interregional communication could be ascertained. In
 this in vitro hippocampal study, we compared spiking dynamics across two
 novel four-compartment device architectures: one with four sets of axon
 tunnels between subregions that excluded the perforant pathway from
 EC-CA3, and one with five sets of axon tunnels that included the EC-CA3
 connection. We found 30-90% faster feed-forward firing rates (shorter
 interspike intervals) in axons in the five-tunnel model with 35-75% slower
 bursting dynamics (longer interburst intervals) compared to the
 four-tunnel model. Comparing the percentage of spikes in bursts between
 array designs, the five-tunnel architecture showed that the CA3-CA1 and
 CA1-EC axons had more spikes in bursts than the four-tunnel counterpart
 suggesting more structured information transfer. Feedback firing rates
 were similar between configurations. The faster feed-forward
 inter-regional spiking in the more natural five-tunnel configuration than
 with four-tunnel suggests tighter control of spiking and possibly more
 precise communication between subregions.
description: <strong>In vitro hippocampal neuronal network culture in a
 four-chamber device with four-way and five-way microfluidic
 interconnections</strong> Microfluidic and
 culturing methods were previously described in more detail in Vakilna et
 al. (2021) and Chen et al. (2023). In short, we employed a novel
 four-chamber device containing microfluidic tunnels for axonal
 communication between chambers. Each chamber contained dissociated neurons
 from micro dissections of the entorhinal cortex (EC), dentate gyrus and
 hilus (DG), CA3, and CA1 including the subiculum from postnatal day 4
 Sprague Dawley rat pups under anesthesia as approved by the UC Irvine
 Institutional Animal Care and Use Committee (IACUC). Brain cells were
 dissociated and plated at 1,000 cells/mm<sup>2</sup> for DG,
 330 for CA3, 410 for CA1 (including subiculum), and 330 for EC, in order
 to reflect in vivo neural densities: EC-DG 1:3, DG-CA3 3:1, CA3-CA1
 1:1.25, and CA1-EC 1.25:1 (Braitenberg, 1981). Cells were in 10 μL of
 NbActiv4 medium (Transnetyx BrainBits, Springfield, IL; Brewer et al.,
 2009b) and were plated into the wells sequentially. After thirty minutes
 in the incubator to allow for adhesion, 0.8 mL culture medium was added.  
 The cultures were capped with Teflon sheets (ALA Scientific, Farmingdale
 NY) and incubated for 21–26 days in humidified 5%
 CO<sub>2</sub> and 9% O<sub>2</sub> (Brewer and
 Cotman, 1989). Half of the medium was changed every 3-4 days. Activity was
 recorded on days 21-26, 2-5 days after a medium change when the networks
 had reached maturity. MEA120 glass multielectrode arrays (MEA) equipped
 with 120 30-μm-diameter electrodes spaced 200 μm served as the substrates
 for the culture of neuronal networks (Multichannel Systems, Reutlingen,
 Germany; ALA Scientific, Farmingdale, NY, USA). A custom
 polydimethylsiloxane (PDMS) device was aligned and attached to the MEA
 that separates the microelectrodes into four chambers for each cell type.
 Axon-isolating tunnels connected each compartment. Each PDMS well was 9.7
 mm<sup>2</sup> by 1-mm high. Each microfluidic tunnel was 3 μm
 high × 10 μm wide × 400 μm long spaced 50 μm apart. Two
 configurations of microfluidic tunnels for axonal communication were
 compared. In the first configuration, each compartment was connected to
 the adjacent one to allow feed-forward connection according to Cajal’s
 original understanding of the trisynaptic loop. Five of the 51 axon
 tunnels between each compartment were monitored with a pair of electrodes
 underneath. In the second configuration, two electrodes were under each of
 4 of the 67 tunnels between the same subregions, but to instantiate the
 perforant path (Andersen et al., 1971), axonal tunnels diagonally
 connected the EC to the CA3 wells. Five of these 11 EC to CA3 tunnels were
 monitored by pairs of electrodes and two more by a single electrode. From
 the spike timing delay between the electrode pairs in each tunnel, we
 determined the direction of axonal potential propagation and
 categorization of action potentials as either feed-forward or feedback. In
 both configurations, nineteen electrodes were at the bottom of each
 subregion.  The substrate was treated with oxygen plasma followed by
 coating the wells with poly-D-lysine for cell adhesion. Finally, we placed
 micro dissected dissociated neurons from four-day-old rat hippocampi in
 the corresponding wells and cultured them in NbActive4 (Brewer 2008) for
 18-22 days (Chen et al., 2023).
 <strong>Multi-electrode array and
 recording</strong> A Multichannel Systems MEA120
 1100 (Multichannel Systems, Reutlingen, Germany) amplifier was used to
 record spontaneous activity on the 120-electrode microarray. Spontaneous
 activity was analyzed using MC_Rack software at a sampling rate of 25 kHz
 at 37<sup>o</sup> C for 5 min, in humidified 5%
 CO<sub>2</sub>, 9% O<sub>2</sub> (custom Airgas
 USA, Santa Ana, CA).  Recordings were initiated several minutes after
 transfer from the culture incubator to the amplifier, once at least 80% of
 the tunnels had stable activity. Arrays with &lt;80% active tunnels or
 that had poor growth in one of the compartments were rejected for
 recording. <strong>Spike detection, sorting, and
 axonal propagation direction </strong> Details on
 our axonal spike directionality algorithm were previously described in
 Lassers et al. (2023). Briefly, raw tunnel data sampled at 25 kHz were
 filtered through Wave_Clus (Chaure et al., 2018), and spike detection and
 clustering were computed from 5 to 50 S.D. noise and 50.1 to 500 S.D. to
 ensure the counting and clustering of large axonal spiking. A refractory
 period of 1.5 ms was specified. Any spike shapes differing by less than
 three standard deviations from the mean spike shape were included in a
 single cluster. The large, tolerated deviation was chosen to accommodate
 different axon-electrode coupling for a single axon on each of the two
 electrodes. Clustering was used to discard complex spikes from multiple
 axons in a microtunnel that produce overlapping spike waveforms. Similar
 to previous analyses of these tunnel devices that demonstrated that ∼63%
 of tunnels contained only one axon (Narula et al., 2017), single axons
 were identified by their uniform conduction velocities or spike timing
 delays. These timing delays were used to generate a normalized matching
 indexing (NMI) algorithm which was computed for every tunnel using the
 timing comparison made from the two electrodes spanning each tunnel. All
 tunnels were the same length of 400 μm except for the EC-CA3 diagonal
 tunnels that ranged from 400-600 μm.  Tunnels with NMI
 &gt; 0.2 (20% of spikes matched between two clusters) were considered
 valid. This threshold was sufficient for eliminating spurious spike pair
 correlations during high spike rates. A histogram of
 conduction times was generated with thresholding and peak prominence
 values provided by the MATLAB findpeaks function. Valid delay times were
 between 0.2 ms and 1 ms if there was a peak at sufficiently fast
 conduction times. Feed-forward axons were identified by positive delay
 times and feed-back axons by negative conduction times, the basis for our
 determination of the feed-forward and feedback directionality of axonal
 communication. In contrast, raw data from the wells were filtered through
 Wave_Clus and spikes were detected at the threshold of ±3.5 S.D. noise.
 This lower threshold than for axons better accommodated the lower signal
 to noise ratio in the wells. Spike clustering was not needed since single
 neurons were detected in &gt;90% of cases.
 <strong>Spike dynamics and probability
 distributions</strong> Vakilna et al. (2021)
 showed that the distribution of inter-spike intervals (ISI) and
 inter-burst intervals (IBI) follow log–log distributions and were
 visualized as normalized complementary cumulative probability
 distributions (CCDs) with logarithmically spaced bins (Newman, 2005). A
 log-transformed linear model of the slope, m, and intercept c was used to
 fit the CCD after log transformation. A grid search of the local maximum
 for was used to find the best fits with time limits varied up to 50% with
 a step size of 5%. A single fit was found for all ISI CCDs over the
 probability range from 1 to 0.1. The four-tunnel ISI CCDS intervals were
 different for each tunnel, for both feed-forward and feedback axons. For
 the five-tunnel design, intervals were from 0.01 to 0.2 s except for
 CA1-EC feed-forward and feedback axons which were fit for intervals
 between 0.01 to 0.11 s and 0.01 to 0.09 s respectively, to account for
 non-linearities in the distribution. Two linear fits were calculated for
 all inter-burst interval (IBI) CCDs piecewise to account for the “up
 states” and “down states,” referring to fast and slow bursting,
 respectively (Vakilna et al., 2021). The minimum time for the up states
 was used as the maximum time for the down states. We compared the
 distribution of ISI to a log-normal distribution instead of a log-log to
 show that the distributions were not log-normal. The burstiness of a
 neuronal unit was defined as the percentage of tagged spikes that appear
 in bursts, at least four spikes, each separated by less than 50 ms.
 Distributions of axonal and somal burstiness were plotted normalized by
 the number of arrays in each network design and statistical tests were
 performed to assess changes in burstiness between designs.
 <strong>Statistics </strong> Data
 were analyzed with custom MATLAB 2023a scripts. Slopes were compared for
 significant statistical differences at alpha=0.05 using analysis of
 covariance (ANCOVA) followed Tukey’s honest significant difference (HSD)
 test. The burstiness of the data was compared using the nonparametric
 Kruskal-Wallis test since the distributions of burstiness had significant
 deviations from the normal distribution to determine if the data came from
 different distributions at p&lt;0.05. Data were combined and analyzed
 for nine separately plated networks for the four-tunnel model and six
 separately plated networks for the five-tunnel model.
description: # Impact of the entorhinal feed-forward connection to the CA3 on
 hippocampal coding
 [https://doi.org/10.5061/dryad.x0k6djhwb](https://doi.org/10.5061/dryad.x0k6djhwb) ## Description of the data and file structure The data are stored in MATLAB R2024b (.mat) files within the .zip files. Each zip file contains the data collected for either the four-tunnel MEA architecture experiments or the five-tunnel MEA architecture. The axon and well data are recorded from self-wiring model of the trisynaptic loop and contain subregions from the Entorhinal Cortex (EC), Dentate Gyrus (DG), Cornu Ammonis 3 (CA3), and Cornu Ammonis 1 (CA1) and provided via binary spike trains. The four-tunnel architecture excludes the EC-CA3 connection which is compared to the dynamics of the five-tunnel architecture that does include the EC-CA3 connection. Within either experiment, there are two sub directories for either axon data or well data. The axonal data has been preprocessed so that directions are known from normalized matching indexing. The well data contains the activity of each electrode that is not assigned to the tunnels. Additionally, the spiking dynamics such as interspike interval (ISI) in seconds, interburst interval (IBI) in seconds, intra burst spike rate (Hz), spikes per burst, burst duration (ms), and the percentage of spike appearing in each burst are calculated for both axons and somal well data. ### Files and variables #### File: Five_Tunnel_Architecture.zip **Description:**  There are two subdirectories inside this file associated with axonal data and somal well data. Inside the folder labeled "Axons" contains two .mat files. The first one is named "allregion_unit_matched_cleaned.mat". This is a cell array where each cell contains a table of spike trains associated to electrodes in tunnels identifiable by the Subregion and Electrode Pairs columns. Regi is the region ID where 1=EC-DG, 2=DG-CA3, 3=CA3-CA1, 4=CA1-EC, and 5=EC-CA3. Chani is a variable for a quick reference to an arbitrary tunnel. Since there are two electrodes in a tunnel, the up_ff and up_fb correspond to the activity of the first electrode listed in the electrode pairs list. The down_ff and down_fb columns correspond to the second electrode listed in the electrode pairs column. The spike trains are also in a cell format, as there can be multiple axons per tunnel. There are additional columns that denote the conduction time (ff_cdt is the feed-forward conduction time in ms and fb_cdt is the feedback conduction time in ms) and the names of the electrodes associated with the spike trains, stored as a vector of 1's and 0's where 1’s represent the index of a spike. The cells are ordered by file ID (FID) 1-6, as each cell corresponds to a different network culture. The second file, "spike_burst_dyn_table_stim.mat", is a singular table that contains the aforementioned spiking dynamics with columns that identify the axon, electrode, and region that these dynamics appear in. In this table, fi is defined as the File ID and regi is the region ID where 1=EC-DG, 2=DG-CA3, 3=CA3-CA1, 4=CA1-EC, and 5=EC-CA3. The variable if_ff denotes if the axon's direction of communication is feed-forward (1) or feedback (0). The second subdirectory named "Wells" contains three .mat files. The first, "all_regions_unit_matched.mat", is structured similarly to "allregion_unit_matched_cleaned.mat", where each cell contains a table denoting which binary spike train belongs to each electrode. The cultured networks were sparse enough such that each somal well electrode records only one neuron (&gt;90% of the electrodes). The next file in the well directory is called "matching_table_wells_CW.mat", which is just a table that says which well electrodes belong to which subregion culture. The final file "well_spike_dynamics_table_hfs_3-5.mat" is the well spiking dynamics table. The only difference between this file and the axon counterpart is direction, as the wells don't have measurable directional activity. #### File: Four_Tunnel_Architecture.zip **Description:**  The four-tunnel architecture is structured the same as the five-tunnel with a few minor differences. There are two subdirectories inside this file associated with axonal data and somal well data. Inside the folder labeled "Axons" are two .mat files. The first one is named "allregion_unit_matched_stim.mat". This is a cell array where each cell contains a table of spike trains associated with electrodes in tunnels. This is a cell array where each cell contains a table of spike trains associated to electrodes in tunnels identifiable by the Subregion and Electrode Pairs columns. Regi is the region ID where 1=EC-DG, 2=DG-CA3, 3=CA3-CA1, and 4=CA1-EC. Chani is a variable for a quick reference to an arbitrary tunnel. Since there are two electrodes in a tunnel, the up_ff and up_fb correspond to the activity of the first electrode listed in the electrode pairs list. The down_ff and down_fb columns correspond to the second electrode listed in the electrode pairs column. The spike trains are also in a cell format as there can be multiple axons per tunnel. There are additional columns that denote the conduction time (ff_cdt is the feed-forward conduction time in ms and fb_cdt is the feedback conduction time in ms) and the names of the electrodes associated with the spike trains, stored as a vector of 1's and 0's where 1’s represent the index of a spike. The cells are ordered file ID (FID) 1-6 as each cell corresponds to a different network culture. The second file, "spike_burst_dyn_table_stim.mat", is a singular table that contains the aforementioned spiking dynamics with columns that identify the axon, electrode, and hippocampal subregion. In this table, fi is defined as the File ID and regi is the region ID where 1=EC-DG, 2=DG-CA3, 3=CA3-CA1, and 4=CA1-EC. The variable if_ff denotes if the axon's direction of communication is feed-forward (1) or feedback (0). The second subdirectory named "Wells" contains four .mat files. The first, "all_regions_unit_matched.mat", is structured similarly to "allregion_unit_matched_cleaned.mat", where each cell contains a table denoting which binary spike train belongs to each electrode. The cultured networks were sparse enough such that each somal well electrode records only one neuron (&gt;90% of electrodes). The next file in the well directory is called "matching_table_wells_CW.mat", which is a table that defines which well electrodes belong to which subregion culture. There were also arrays cultured in the opposite direction (CCW=counterclockwise; or CW=clockwise EC-DG-CA3-CA1), which use the next file entitled "matching_table_wells_CCW.mat". The final file "well_spike_dynamics_table_hfs_3-5.mat" is the well spiking dynamics table. The only difference between this file and the axon counterpart is direction, as the wells don't have measurable directional activity. The variables are defined the same way. ## Code/software The preprocessing pipeline can be found at: [https://github.com/Brewer-Neurolab/Lassers-2023-Axon-Flow](https://github.com/Brewer-Neurolab/Lassers-2023-Axon-Flow) The code to process this data can be found at: [https://github.com/Brewer-Neurolab/Khatri_Lassers_EC_CA3_Pathway_Effect/tree/main](https://github.com/Brewer-Neurolab/Khatri_Lassers_EC_CA3_Pathway_Effect/tree/main) ## Access information Data was derived from the following sources: * Our four-tunnel data was originally reported in: * Vakilna, Y. S., Tang, W. C., Wheeler, B. C., & Brewer, G. J. (2021). The flow of axonal information among hippocampal subregions: 1. feed-forward and feedback network spatial dynamics underpinning emergent information processing. *Frontiers in Neural Circuits*, *15*, 660837. * Our five-tunnel data was originally reported in: * Chen, R., Vakilna, Y. S., Lassers, S. B., Tang, W. C., & Brewer, G. (2023). Hippocampal network axons respond to patterned theta burst stimulation with lower activity of initially higher spike train similarity from EC to DG and later similarity of axons from CA1 to EC. *Journal of Neural Engineering*, *20*(5), 056004.