A novel whole-cell mechanism for long-term memory enhancement.

Olfactory-discrimination learning was shown to induce a profound long-lasting enhancement in the strength of excitatory and inhibitory synapses of pyramidal neurons in the piriform cortex. Notably, such enhancement was mostly pronounced in a sub-group of neurons, entailing about a quarter of the cell population. Here we first show that the prominent enhancement in the subset of cells is due to a process in which all excitatory synapses doubled their strength and that this increase was mediated by a single process in which the AMPA channel conductance was doubled. Moreover, using a neuronal-network model, we show how such a multiplicative whole-cell synaptic strengthening in a sub-group of cells that form a memory pattern, sub-serves a profound selective enhancement of this memory. Network modeling further predicts that synaptic inhibition should be modified by complex learning in a manner that much resembles synaptic excitation. Indeed, in a subset of neurons all GABAA-receptors mediated inhibitory synapses also doubled their strength after learning. Like synaptic excitation, Synaptic inhibition is also enhanced by two-fold increase of the single channel conductance. These findings suggest that crucial learning induces a multiplicative increase in strength of all excitatory and inhibitory synapses in a subset of cells, and that such an increase can serve as a long-term whole-cell mechanism to profoundly enhance an existing Hebbian-type memory. This mechanism does not act as synaptic plasticity mechanism that underlies memory formation but rather enhances the response of already existing memory. This mechanism is cell-specific rather than synapse-specific; it modifies the channel conductance rather than the number of channels and thus has the potential to be readily induced and un-induced by whole-cell transduction mechanisms

Real Time Multiplicative Memory Amplification Mediated by Whole-Cell Scaling of Synaptic Response in Key Neurons

<div><p>Intense spiking response of a memory-pattern is believed to play a crucial role both in normal learning and pathology, where it can create biased behavior. We recently proposed a novel model for memory amplification where the simultaneous two-fold increase of all excitatory (AMPAR-mediated) and inhibitory (GABA_AR-mediated) synapses in a sub-group of cells that constitutes a memory-pattern selectively amplifies this memory. Here we confirm the cellular basis of this model by validating its major predictions in four sets of experiments, and demonstrate its induction via a whole-cell transduction mechanism. Subsequently, using theory and simulations, we show that this whole-cell two-fold increase of all inhibitory and excitatory synapses functions as an instantaneous and multiplicative amplifier of the neurons’ spiking. The amplification mechanism acts through multiplication of the net synaptic current, where it scales both the average and the standard deviation of the current. In the excitation-inhibition balance regime, this scaling creates a linear multiplicative amplifier of the cell’s spiking response. Moreover, the direct scaling of the synaptic input enables the amplification of the spiking response to be synchronized with rapid changes in synaptic input, and to be independent of previous spiking activity. These traits enable instantaneous real-time amplification during brief elevations of excitatory synaptic input. Furthermore, the multiplicative nature of the amplifier ensures that the net effect of the amplification is large mainly when the synaptic input is mostly excitatory. When induced on all cells that comprise a memory-pattern, these whole-cell modifications enable a substantial instantaneous amplification of the memory-pattern when the memory is activated. The amplification mechanism is induced by CaMKII dependent phosphorylation that doubles the conductance of all GABA_A and AMPA receptors in a subset of neurons. This whole-cell transduction mechanism enables both long-term induction of memory amplification when necessary and extinction when not further required.</p></div

The cells in the CaMKII blocker affected cluster were affected by the task learning through whole-cell twofold multiplication of the AMPA single-channel current.

<p>A. The average single channel current of the cells in the CaMKII affected cluster from the trained group (T-reduced, n = 6) was approximately twice that of the average single channel current in the naive group (n = 13) and the non-affected cells in the trained group (T-not reduced, n = 11). B. The amplitude distribution curve of the cells in the more affected cluster could be reconstructed by multiplying all events in the naive group by a factor of two (T-reduced, n = 6; T-not reduced, n = 11; naïve, n = 13). C. The trained group deviated from the control group mainly by the sub-group of cells that exhibited a high single channel current. D. The cumulative frequency curve of the cell’s average distribution curve could not be fully explained by two-fold multiplication of the average event amplitude of a randomly selected 40% of the cells in the naive group.</p

CaMKII blockers (KN93 left; tatCN21 right) act through a whole-cell, two-fold division of the GABA_A single channel conductance in a subset of cells from trained rats.

<p>A, E: Depicted for each cell is the effect of CaMKII blocker on the average and standard deviation of the event amplitudes. The effect of the CaMKII blocker clearly exhibits two clusters, where one cluster exhibits a large blocker effect on the average and standard deviation of the event amplitude (KN93: trained, n = 16; pseudo, n = 8; naïve, n = 10. tatCN21: trained, n = 10; pseudo, n = 7; naive, n = 5) B, F: Similarly, the effect of CaMKII blocker on the average event amplitude and single channel current exhibits two clusters (KN93: trained, n = 12; pseudo, n = 6; naive, n = 5. tatCN21: trained, n = 10; pseudo, n = 4; naïve, n = 5). C, G: An amplitude distribution curve averaged for all cells in the affected cluster (all cells in the blocker affected cluster for which the single channel current could be calculated; KN93: n = 5; tatCN21: n = 4) is shown for the event amplitudes recorded before (purple) and after (orange) CaMKII blocker. The blocker effect was computationally reversed (black curve) by applying the reverse calculation on the data recorded after applying the CaMKII blocker: The fraction of events that were affected by the drug was calculated (Materials and Methods). This fraction of events was randomly selected from the events recorded after the blocker was applied and the amplitude of these events was multiplied by a factor of two. A new amplitude distribution curve was calculated from the whole set of data, using both the multiplied and the non-multiplied events, exhibiting a fully computationally reversed CaMKII blocker effect. The P values were calculated using Cramer Von Mises two-sample test. D, H: the blocker effect could be reversed in each cell separately. Left: an example of one cell where the histogram of events amplitudes before CaMKII blocker (blue) matched the histogram of the calculated events amplitude after the blocker effect was computationally reversed (red), right the P value was calculated for each cell and the average of these P values was calculated from all cells in the highly affected cluster.</p

In a sub-group of cells from trained rats, amplitudes of all inhibitory miniature events are doubled.

<p><b>A.</b> Each cell was plotted as a function of its averaged event amplitude and standard deviation. Few cells from trained-group had exceptionally large averaged amplitudes and Standard-deviations. Using hierarchical clustering analysis the cells were divided to two groups (separated by the dotted line; the same division was obtained for all the methods that were implemented). <b>B.</b> The distribution curve describing the greatly-enhanced trained neurons can be constructed from the distribution curve describing the pseudo-trained neurons. The expected curve (black) calculated from pseudo events (green) overlaps the averaged distribution curve of the greatly-enhanced-trained group (blue). (r = 0.85, only amplitudes >13pA were used, since at lower amplitudes, multiplication factors bigger than two requires unavailable data in amplitudes <6pA, see Methods) <b>C.</b> The averaged GABA_A single channel conductance in the greatly-enhanced-trained-group was doubled compared with the pseudo-trained group, and is 82% bigger than in the moderately-enhanced-trained group. Values represent mean ± SE, (***, p<0.001). The number of active GABA_A channels does not differ between groups. <b>D.</b> The greatly enhanced group shows distinct values of averaged GABA_AR conductance as compared with the moderately enhanced group.</p

The cells in the CaMKII blocker affected cluster were affected by the task learning through whole-cell twofold multiplication of the GABA_A single-channel current.

<p><b>A.</b> The average single channel current of the cells in the CaMKII affected cluster from the trained group (T-reduced) was approximately twice that of the average single channel current in the naïve group and the non affected trained group (T–not reduced). <b>B.</b> The amplitude distribution curve of the cells in the affected cluster in the trained group could be reconstructed by multiplying <u>all</u> events in the naive group by a factor of two (T-reduced, n = 9; T-not reduced, n = 12; naïve, n = 12). <b>C.</b> The trained group deviated from the control group mainly by the presence of the sub-group of cells that exhibited a large single channel current. <b>D.</b> The cumulative frequency distribution of the trained group could be explained by multiplying by two the average event amplitude of a randomly selected 35% of the cells in the naïve group (P<0.34 Kolmogorov-Smirnov test). Each point represents the average event amplitude in a neuron (x-axis).</p

The learning-induced modulation of the inhibitory unitary synaptic events amplitude is dominated by a single process in which the events amplitude was doubled.

<p><b>A.</b> For each neuron an amplitude distribution curve was reconstructed from the mIPSC's amplitudes and was normalized (data taken form <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068131#pone.0068131-Saar2" target="_blank">[17]</a>). Averaged amplitude histograms, were calculated for all neurons from the three groups. Notably, a significant portion of events in the trained group are of higher values. <b>B.</b> For each cell the weight for the first component (X-axis) was drawn against its weight for the second component (Y-axis). Only the weights of the first component are significantly different between groups. <b>C.</b> The curve calculated by subtracting the averaged pseudo amplitude distribution curve from the averaged trained amplitude distribution curve (orange) match the first component calculated by PCA (black). <b>D.</b> For each multiplication factor a different curve that describes the difference between groups assuming a multiplication model was calculated. The calculated curve assuming multiplication factor of 2 (black) matched the curve that describes the main difference between groups (orange). Inset: the correlation coefficient was calculated for each multiplication factor (for calculation of R only amplitudes >13pA were used, since at lower amplitudes, multiplication factors bigger than two requires unavailable data in amplitudes <6pA, see Methods).</p

In a sub-group of cells from trained rats' amplitudes of all excitatory miniature events is doubled.

<p><b>A.</b> Each cell was plotted as a function of its averaged event amplitude and standard deviation. Few cells from trained-group had exceptionally large averaged amplitudes and Standard-deviations. Using hierarchical clustering analysis the cells were divided to two groups (separated by the dotted line). <b>B.</b> The averaged amplitude of the greatly-enhanced-trained-group only is doubled to a value that is significantly higher than that observed for the three other represented sub-groups. Note that while the average amplitude of the moderately enhanced group is significantly lower than that of the greatly enhanced group, it is still higher that the averaged amplitude of the pseudo trained group. Values represent mean ± SE (**, p<0.01 ***, p<0.001). <b>C.</b> The standard deviation of the greatly-enhanced-trained-group is also doubled, compared to the other three sub-groups, which have all similar values. Values represent mean ± SE, (***, p<0.001). <b>D.</b> The distribution curve describing the trained neurons can be constructed from the events amplitudes of the pseudo-trained neurons. The expected curve (black) calculated from pseudo events (green) overlaps (r = 0.96) the trained distribution curve (blue). <b>E.</b> The distribution curve describing the greatly-enhanced trained neurons can be constructed from the events amplitudes of the large pseudo-trained neurons. The expected curve (black) calculated from events of the 4 biggest cells in the pseudo group (green) is similar to the averaged distribution curve of the greatly-enhanced-trained group (blue). (r = 0.73, both in D and in E only amplitudes >7pA were used, since at lower amplitudes, multiplication factors bigger than two requires unavailable data in amplitudes <3pA, see Methods).</p

Contrast-enhancement causes selective memory enhancement.

<p>Cells in the network were arranged in a matrix in which the intensity level corresponds to the change in the number of spikes during memory activation compared to background. Cells that responded to an arbitrary input-X were arranged in T-shape. <b>A.</b> Contrast-enhancement applied to neurons constructing pattern X, substantially increased their response intensity to the input-X, but activity did not spread to neurons out of this pattern (the same neurons are activated before and after contrast enhancement is applied). <b>B.</b> Contrast enhancement of pattern X did not affect the intensity of another memory-pattern induced by a different input, although the two memory-patterns had a considerable overlap (correlation of 0.74). Notice the vague shape of the T pattern. <b>C.</b> The ratio between the number of spikes before and after contrast-enhancement in response to different inputs. Only the response to input-X (red) was considerably enhanced. Pattern #6 is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068131#pone-0068131-g004" target="_blank">figure 4B</a>.</p