A shared neural ensemble links distinct contextual memories encoded close in time.

Recent studies suggest that a shared neural ensemble may link distinct memories encoded close in time. According to the memory allocation hypothesis, learning triggers a temporary increase in neuronal excitability that biases the representation of a subsequent memory to the neuronal ensemble encoding the first memory, such that recall of one memory increases the likelihood of recalling the other memory. Here we show in mice that the overlap between the hippocampal CA1 ensembles activated by two distinct contexts acquired within a day is higher than when they are separated by a week. Several findings indicate that this overlap of neuronal ensembles links two contextual memories. First, fear paired with one context is transferred to a neutral context when the two contexts are acquired within a day but not across a week. Second, the first memory strengthens the second memory within a day but not across a week. Older mice, known to have lower CA1 excitability, do not show the overlap between ensembles, the transfer of fear between contexts, or the strengthening of the second memory. Finally, in aged mice, increasing cellular excitability and activating a common ensemble of CA1 neurons during two distinct context exposures rescued the deficit in linking memories. Taken together, these findings demonstrate that contextual memories encoded close in time are linked by directing storage into overlapping ensembles. Alteration of these processes by ageing could affect the temporal structure of memories, thus impairing efficient recall of related information

Shaping Neuronal Network Activity by Presynaptic Mechanisms

<div><p>Neuronal microcircuits generate oscillatory activity, which has been linked to basic functions such as sleep, learning and sensorimotor gating. Although synaptic release processes are well known for their ability to shape the interaction between neurons in microcircuits, most computational models do not simulate the synaptic transmission process directly and hence cannot explain how changes in synaptic parameters alter neuronal network activity. In this paper, we present a novel neuronal network model that incorporates presynaptic release mechanisms, such as vesicle pool dynamics and calcium-dependent release probability, to model the spontaneous activity of neuronal networks. The model, which is based on modified leaky integrate-and-fire neurons, generates spontaneous network activity patterns, which are similar to experimental data and robust under changes in the model's primary gain parameters such as excitatory postsynaptic potential and connectivity ratio. Furthermore, it reliably recreates experimental findings and provides mechanistic explanations for data obtained from microelectrode array recordings, such as network burst termination and the effects of pharmacological and genetic manipulations. The model demonstrates how elevated asynchronous release, but not spontaneous release, synchronizes neuronal network activity and reveals that asynchronous release enhances utilization of the recycling vesicle pool to induce the network effect. The model further predicts a positive correlation between vesicle priming at the single-neuron level and burst frequency at the network level; this prediction is supported by experimental findings. Thus, the model is utilized to reveal how synaptic release processes at the neuronal level govern activity patterns and synchronization at the network level.</p></div

Model demonstrates how an increase in asynchronous release, and not spontaneous release, enhances neuronal network activity.

<p><b>(A)</b> Reduction of calcium clearance rate redistributes single-neuron vesicle release (left) and increases the proportion of asynchronous release to synchronous release in the model (right). <b>(B)</b> Higher simulated asynchronous release increases network burst firing rate (left). Experimentally, the increase in asynchronous release induced by strontium application is correlated with the increase in the network burst maximum firing rate (right; modified from Lavi et al [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004438#pcbi.1004438.ref003" target="_blank">3</a>]). <b>(C)</b> Illustration of the change in the release function leading to higher spontaneous release probability at low intracellular calcium concentration ([Ca²⁺]_I; top panel). Higher simulated spontaneous release reduces network burst firing rate (bottom left), in agreement with the reduction in activity induced by the expression of DOC2B^D218,220N (right) in the experimental MEA recordings. <b>(D)</b> Opposite effects of asynchronous and spontaneous release on network activity. In general, higher asynchronous release increases network activity whereas higher spontaneous release reduces network activity (error bars show SEM).</p

Asynchronous release utilizes the synaptic recycling pool (ReP) to elevate synaptic release during bursts.

<p>(<b>A</b>) Analysis of the number of vesicles in 4 representative neurons shows discrete changes in the number of vesicles throughout a single network burst (dotted lines). The stepwise increase in 2 of the neurons (marked in red and orange) represents the replenishment dynamics throughout the network burst, (black dashed line is the average readily releasable pool [RRP] depletion from all neurons participating in the network burst). Analysis of the average RRP depletion <b>(B)</b> and the average ReP consumption <b>(C)</b> over all neurons from all bursts in 10 simulations shows that higher asynchronous release ('+100%', blue line) leads to greater utilization of vesicles from the ReP, while the RRP is depleted by similar levels with or without changes in asynchronous release. Cumulative proportion of the average number of vesicles in the RRP <b>(D)</b> and ReP <b>(E)</b> across all neurons at the beginning (start, left) and end (end, right) of the network burst. Following an increase in asynchronous release, each neuron contributes ~2 extra vesicles within the first 300 ms of the burst <b>(F)</b>. Slower calcium efflux rate drives faster and larger accumulation of calcium during the network burst <b>(G)</b>.</p

Presynaptic-driven neuronal network computational model recreates spontaneous network activity recorded with microelectrode arrays.

<p><b>(A)</b> Color-coded raster plots of spontaneous neuronal network activity recorded on microelectrode array (each dot denotes spike from an electrode, colors code for average firing rate). <b>(B)</b> Profiles of neuronal network bursts demonstrating increased average firing rate with strontium application and DOC2B expression; DOC2B mutant (DOC2B^D218,220N) reduces network burst firing rate (modified from Lavi et al. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004438#pcbi.1004438.ref003" target="_blank">3</a>]). <b>(C)</b> Computational model comprises a network of excitatory and inhibitory neurons. Each connection/synapse accommodates a multistep process, as detailed in E (one synapse is marked with a blue rectangle and its synaptic components are detailed in E). <b>(D)</b> Synaptic release probability in the model is a function of intracellular calcium concentration ([Ca²⁺]_i). <b>(E)</b> Each synapse comprises reserve (RP), recycling (ReP) and readily releasable (RRP) vesicle pools. The transition between pools is bidirectional and is determined by the k_i rate constants (k₂ is Ca²⁺-dependent). <b>(F)</b> Spontaneous activity generated by the model is very similar to the experimental recording shown in A (each dot denotes neuronal action potential, colors code for average firing rate). ISI, inter-spike interval.</p

Summary of the primary parameters of the neuronal network model.

<p>The table summarizes the primary parameters used to construct and run the neuronal network computational model under baseline conditions. It also includes references to the original papers [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004438#pcbi.1004438.ref015" target="_blank">15</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004438#pcbi.1004438.ref025" target="_blank">25</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004438#pcbi.1004438.ref067" target="_blank">67</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004438#pcbi.1004438.ref096" target="_blank">96</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004438#pcbi.1004438.ref101" target="_blank">101</a>]. Parameter names correspond to the variable names used in the MATLAB code.</p><p>Summary of the primary parameters of the neuronal network model.</p

Vesicle priming rate governs the rate of network bursts.

<p><b>(A)</b> Priming rate manipulation was simulated by changing the maximum vesicle transition rate from the recycling pool (ReP) to the readily releasable pool (RRP) (red transparent circle). <b>(B)</b> Color-coded raster plots demonstrate that lower priming rate decreases ('Low'; left panel) and higher priming rate increases ('High'; right panel) network burst frequency and overall network activity (each dot denotes a neuronal action potential, colors code for average firing rate). <b>(C)</b> Analysis of network burst profiles shows that lower priming rate reduces firing rate throughout network bursts (top panel), while higher priming rate increases firing rate throughout network bursts (bottom panel). <b>(D)</b> Analysis of network burst activity parameters shows that while higher priming rate increases the frequency of network bursts and the overall spike rate, it does not change the duration of the network bursts; this suggests the involvement of other presynaptic release processes in regulating the duration of network bursts. <b>(E)</b> Spontaneous activity recorded from a neuronal network cultured on MEA before (left) and after (right) twofold expression of Munc13-1 (each dot denotes spike from an electrode, colors code for average firing rate). In agreement with the high priming demonstrated in A, Munc13-1 expression clearly increased the frequency of network bursts. ISI, inter-spike interval.</p