Pattern separation and completion of distinct axonal inputs transmitted via micro-tunnels between co-cultured hippocampal dentate, CA3, CA1 and entorhinal cortex networks

Abstract

Objective: Functions ascribed to the hippocampal sub-regions for encoding episodic memories include the separation of activity patterns propagated from the entorhinal cortex (EC) into the dentate gyrus (DG) and pattern completion in CA3 region. Since a direct assessment of these functions is lacking at the level of specific axonal inputs, our goal is to directly measure the separation and completion of distinct axonal inputs in engineered pairs of hippocampal sub-regional circuits.

Approach: We co-cultured EC-DG, DG-CA3, CA3-CA1 or CA1-EC neurons in a two-chamber PDMS device over a micro-electrode array (MEA60), inter-connected via distinct axons that grow through the micro-tunnels between the compartments. Taking advantage of the axonal accessibility, we quantified pattern separation and completion of the evoked activity transmitted through the tunnels from source into target well. Since pattern separation can be inferred when inputs are more correlated than outputs, we first compared the correlations among axonal inputs with those of target somata outputs. We then compared, in an analog approach, the distributions of correlation distances between rate patterns of the axonal inputs inside the tunnels with those of the somata outputs evoked in the target well. Finally, in a digital approach, we measured the spatial population distances between binary patterns of the same axonal inputs and somata outputs.

Main results: We found the strongest separation of the propagated axonal inputs when EC was axonally connected to DG, with a decline in separation to CA3 and to CA1 for both rate and digital approaches. Furthermore, the digital approach showed stronger pattern completion in CA3, then CA1 and EC.

Significance: To the best of our knowledge, these are the first direct measures of pattern separation and completion for axonal transmission to the somata target outputs at the rate and digital population levels in each of four stages of the EC-DG-CA3-CA1 circuit.

Figure 1

Hippocampal neurons harvested from 4 sub-regions shown in (a) were co-cultured in pairs in a two-well PDMS device (b) over a micro-electrode array. (c) Micro-fluidic tunnels (3 x 10 x 400 um) exclude cell bodies from one well to the other, and permit axonal growth and propagation of axonal spikes. From the 51 communicating micro-tunnels, 7 of these tunnels overlaid 2 electrodes (one of the remaining tunnels had only a single electrode as the second was coupled to a the internal reference electrode). For each pair of tunnel electrodes we used the recording site reporting higher spike rate, under the assumption that the axon coupled more accurately to the more active electrode. (d) A paired-pulse stimulation protocol was applied at 22 different sites in each well and repeated 25 times to evoke activity between the co-cultures through the micro-tunnels. (e) Evoked responses during one stimulation trial at electrode 17 (column 1, row 7). (f) Illustration of pattern separation and completion (adapted from Yassa and Stark 2011).

Figure 2

Analog pattern separation or completion from distributions of correlation distances of input and output rate patterns. (a) Pearson correlation between rate patterns, computationally described by log spike rates evoked at each tunnel (input) or target (output) electrode. Examples of strong Pearson correlation between two rate input patterns (r=0.77, a.i) and weak Pearson correlation between two rate output patterns (r=0.08, a.ii) are shown in the figure. (b) Hypothetical distributions of correlation distances of input and output rate patterns. In our method we aggregate the non-zero input correlation distances in one cumulative curve and the non-zero output correlation distances in another. Therefore, both hypothetical curves show cumulative totals less than 1 because instances of zero correlations are not included in the cumulative sum. The area between these two curves would quantify pattern separation if the inputs correlated more frequently than the outputs (black area); otherwise, if the outputs correlated more frequently than the inputs, this area quantified pattern completion (gray area).

Figure 3

Digital pattern separation or completion from spatial population distances between binary input and output patterns. (a) Examples of spatial population distance based on Jaccard distance adjusted for sparseness between two binary input patterns (Δ Input, a.i) and between two binary output patterns (Δ Output, a.ii) are shown in the figure. (b) Model adapted from Yassa and Stark (2011) for pattern separation and completion based on spatial population distances of binary input and output patterns (Δ Inputs vs. Δ Outputs). The black portion describes situations in which outputs are more dissimilar than inputs (i.e. separation: Δ Outputs > Δ Inputs). The gray portion describes the reverse situation in which outputs are more similar than inputs (i.e. completion: Δ Outputs < Δ Inputs).

Figure 4

The activity measured among axons correlates with the source and target evoked responses better than the separate EC somata with DG somata. We use a log scale to cover the large dynamic rage of the spike rates. (a) Strong positive correlation between EC inputs and axonal outputs in micro-tunnels (r=0.81, p=10⁻²², slope=1.05). (b) Stronger positive correlation between axonal inputs in the tunnels and DG somata outputs (r=0.87, p=10⁻²⁸, slope=1.07). (c) High proportionality between EC inputs and DG outputs, consistent with feed-forward propagation of the information flow (r=0.67, p=10⁻¹³, slope=1.07). All points show the averaged log spike rate evoked at the tunnel and chamber electrodes in 80 ms (i.e., 40 ms x 2 pulses) over 25 trials from multiple arrays. Therefore, all scatter plots show 4 arrays x 22 stimulation sites = 88 points.

Figure 5

Correlations extracted from the non-repeated combinations trials/stimuli in EC and assembled in one single matrix showing axonal spike rates evoked in the tunnels (below diagonal) more correlated than the DG target outputs (above diagonal). Note the larger fraction of r=0 (black) correlations among somata outputs than axonal inputs. (a) Examples of correlations among axonal inputs evoked by specific stimulation electrodes in EC during the first stimulation trial. Scatter plot (below) between two of these aforementioned axonal inputs: axonal inputs during trial 1, stimulation electrode 13 in EC vs. axonal inputs during trial 1, stimulation electrode 5 in EC). (b) Examples of correlations among somata outputs evoked by specific stimulation electrodes in EC during the first stimulation trial. Scatter plot (right) between two of these somata outputs: somata outputs during trial 1, stimulation electrode 13 in EC vs. somata outputs during trial 1, stimulation electrode 5 in EC.

Figure 6

Comparison of the distributions of the correlation distances between input and output rate patterns show strong pattern separation in dentate gyrus and CA3, but evidence for pattern completion in CA1 and EC. The first four panels show the cumulative distribution of the correlations among axonal inputs in micro-tunnels (solid line; Pearson correlations in absolute value) vs. the cumulative distribution of the correlations among target outputs (dashed lines). The area between curves, evaluated for each hippocampal pair, quantifies pattern separation (black; Outputs < Inputs) and pattern completion (gray; Outputs > Inputs). (a) Distribution of correlation distances from 4 EC-DG arrays between rate patterns evoked by all non-repeated combinations of 22 stimulation sites and 25 trials (n = 4 x 150,975 = 603,900 comparisons). Asymptotes are below 1 as only non-zero correlation values are used. (b) DG-CA3 networks (n=754,875 from 5 arrays). (c) CA3-CA1 networks (n=754,875 from 5 arrays). (d) CA1-EC co-cultures (n=754,875 from 5 arrays). (e) Black bar heights are the average integrated areas for pattern separation (inputs greater than outputs) from multiple arrays of the same configuration of hippocampal sub-regions (n=4 for EC-DG networks, n=5 for the other co-cultures). S.E.M. is a function of the variance for each of these 4 or 5 replicate configurations. Similarly, gray bar heights represent the average integrated areas for pattern completion (inputs less than outputs). Note that some configurations contain evidence for both pattern separation and pattern completion. Since the normality assumption fails by the Kolmogorov-Smirnov test, we perform the non-parametric Wilcoxon Rank Sum test. Pattern separation in EC-DG and DG-CA3 is significantly greater than zero (p=0.03 and p=0.008, respectively). In the same hippocampal sub-networks, pattern separation is significantly greater than the pattern completion (p=0.03 and p=0.008 for EC-DG and DG-CA3, respectively) and different from the pattern separation observed in the other sub-regions (p=0.01). CA3-CA1 co-cultures show a significant bias toward pattern separation (p=0.048) and completion (p=0.008). Furthermore, pattern separation is significantly greater than the pattern completion (p=0.001). CA1-EC networks also show significantly difference between pattern separation and completion (p=0.001) and a significant bias toward pattern completion (p=0.008). Finally, pattern completion in the co-cultures with CA1 involvement is significantly different from the co-cultures with DG neurons (p=0.02).

Figure 7

Control networks show distributions of correlation distances of axonal input and somata output rates indicative of pattern separation in DG, CA1 and EC. (a) Cumulative distributions of non-zero correlation distances of input and output rate patterns evoked by all non-repeated combinations of 22 stimulation sites and 25 trials from 5 DG-DG arrays stimulated in both chambers (n=5 arrays x 150,975 correlations x 2 stimulated chambers =1,509,750 correlation values). (b) Five CA3-CA3 networks (n=1,509,750). (c) Five CA1-CA1 networks (n=1,509,750). (d) Five EC-EC co-cultures (n=1,509,750). (e) Pattern separation in DG-DG networks is significantly greater than zero (p=0.001) and statistically different not only from the pattern completion in the same networks (p=0.0003) but also from the separation degree of the other regions (p=0.05). CA3-CA3 co-cultures show evidence of pattern separation and completion greater than zero (p=0.015 and p=0.027, respectively). CA1-CA1 networks show significant pattern separation (p=10⁻⁶) and completion (p=0.0085). Furthermore, pattern separation is statistically different from pattern completion (p=10⁻⁸). Pattern separation in EC-EC networks is significantly greater than zero (p=0.0028) and statistically different from pattern completion (p=0.001). Statistically significant differences have been assessed by one and two-tailed t-tests.

Figure 8

Spatial population distances adjusted for sparseness between binary input (Δ Inputs) and output (Δ Outputs) patterns show pattern separation EC axonal inputs transmitted into DG and completion of DG axonal inputs propagated into CA3. Δ Inputs vs. Δ Outputs describe pattern separation (Δ Outputs > Δ Inputs) and completion (Δ Outputs < Δ Inputs). The first four panels depict the area above (black) and below (gray) the diagonal (Δ Outputs=Δ Inputs). The curves shown in these panels are obtained by evaluating the mean and S.E.M of Δoutputs (y axis) in constant bins of 0.005 allocated to the Δinput values of each pair. Since the maximum value of the spatial population distance could change from one pair to another, by using the same bin size we can have different numbers of bins involved. (a) Δ Outputs vs. Δ Inputs extracted from the spatial population distances between all non-repeated couples of binary patterns evoked in the tunnels and in DG by 22 stimuli in EC for 4 arrays (n = 4 x 231 = 924 comparisons; 0.005 bin size x 13 bins). (b) DG-CA3 (n=1,155 from 5 arrays in 15 bins). (c) CA3-CA1 (n=1,155 from 5 arrays in 12 bins). (d) CA1-EC (n=1,155 from 5 arrays in 13 bins). (e) Separation is measured by the sum of the excess distances of Δ Outputs > Δ Inputs above the diagonal (Δ Outputs=Δ Inputs), divided by the number of bins (black bars). Separation is significant for EC-DG (p=0.0005), DG-CA3 (p=0.023) and CA1-EC (p=0.023), not for CA3-CA1. Completion, similarly measured by the average of the excess distances of Δ Outputs < Δ Inputs (gray bars) below the diagonal, is significant for DG-CA3 (p=0.01), CA3-CA1 (p=0.004) and CA1-EC (p=0.009), not for EC-DG. Separation is also different from completion for EC-DG (p=10⁻⁵), DG-CA3 (p=0.0037), CA3-CA1 (p=0.003) and CA1-EC (p=0.002). Separation in EC-DG and DG-CA3 (i.e., when DG is involved) is further different from CA3-CA1 and CA1-EC (p=0.005). Statistical analyses have been assessed by one and two-tailed t-tests.

Figure 9

Spatial population distances adjusted for sparseness show a separation of DG axonal inputs in DG-DG control networks that is weaker than EC-DG co-cultures, while CA3 patterns appear to self-complete. (a) Δ Outputs vs. Δ Inputs extracted from the spatial population distances between all non-repeated couples of binary input or output patterns evoked by 44 stimuli (22 stimulation sites in each well) in 5 DG-DG arrays (n = 5 x 2 x 231= 2,310 comparisons; 0.005 bin size x 25 bins). (b) CA3-CA3 (n = 5 x 2 x 231= 2,310 from 5 arrays in 23 bins). (c) CA1-CA1 (n = 5 x 2 x 231 = 2,310 from 5 arrays in 11 bins). (d) EC-EC (n = 5 x 2 x 231= 2,310 from 5 arrays in 14 bins). (e) Pattern separation, measured by the sum of the excess distances of Δ Outputs > Δ Inputs above the diagonal, divided by the number of bins (black bars), is significant for DG-DG (p=0.0009), as well as CA1-CA1 (p=0.0002) and EC-EC (p=0.0036). Pattern completion, similarly measured by the average of the excess distances of the Δ Outputs < Δ Inputs (gray bars) below the diagonal, is significant for DG-DG (p=0.0015), CA3-CA3 (p=10⁻⁵) and EC-EC (p=0.02). Pattern completion is significantly different from separation for DG-DG (p=10⁻⁵), CA3-CA3 (p=10⁻⁵), CA1-CA1 (p=10⁻⁵) and EC-EC (p=0.0005). Pattern completion in CA3-CA3 is also significantly different from the other regions (p=10⁻⁵). Statistical significance was assessed by one and two-tailed t-tests.