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Memory allocation is defined as the process that determines which specific synapses and neurons in a neural network will store a given memory.[1] Although multiple neurons can receive a stimulus, only a subset of the neurons will induce the necessary plasticity for memory encoding. The selection of this subset of neurons is termed neuronal allocation. Similarly, multiple synapses could be activated by a given set of inputs, but specific mechanisms determine which synapses actually go on to encode the memory, and this process is termed synaptic allocation.

Neurons with higher excitability are more likely to be recruited in a memory trace and the celluar transcription factor CREB (cAMP responsive element-binding protein) has been proposed to be involved neuronal allocation by altering neuronal excitability.[2]

Certain synapses on recruited neurons are more likely to undergo an enhancement of synaptic strength. In addition, long-term potentiation (LTP) is a well-studied form of synaptic strengthening widely considered as one of the major cellular mechanisms underlying learning and memory.[3] Proposed mechanisms that might contribute to synaptic allocation include synaptic tagging and synaptic clustering[1]

Neuronal and synaptic allocation mechanisms are thought to be theoretically important for memory recall and storage efficiency.[1] By directing relating information to overlapping populations of neurons, memory allocation mechanisms could link these memories, place them within a common context, save storage space and perhaps alter memory strength and stability.[3]

Neuronal Allocation

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Neuronal allocation is a phenomenon that accounts for how specific neurons in a network, and not others that receive similar input, are committed to storing a specific memory. [1]

CREB

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Membrane proteins activate various second messenger and intracellular signaling pathways, which eventually target CREB, promoting transcription and translation of target proteins that change neuronal mechanisms in the long term.

The transcription factor cAMP response element-binding protein (CREB) may be activated by multiple pathways. For example, the cyclic adenosine monophosphate (cAMP) and protein kinase A (PKA) pathways appear to participate in neuronal allocation[1]. When activated by the second messenger cAMP, PKA can translocate to the nucleus and phosphorylate CREB to initiate transcription of target genes. PKA inhibitors can block the development of long-lasting LTP, and this is accompanied by a reduction in the transcription of genes modulated by the CREB protein[4].

Most studies of neuronal allocation to date have used the amygdala as a model circuit [1]. Fear-related memory traces in the amygdala are mediated by CREB expression in the individual neurons allocated to those memories[5][6][7]. CREB modulates cellular processes that lead to neuronal allocation, particularly with regards to dendritic spine density and morphology[8]. As many of the memory mechanisms studied to date are conserved across different brain regions, it is possible that the mechanisms of fear-based memory allocation found in the amygdala will also be similarly present for other types of memories throughout different brain regions[1].

Metaplasticity

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There is extensive evidence that memory allocation is due not only to a switch-like control of plasticity states, but also to metaplasticity, or the “plasticity of plasticity.” Several studies have shown that neurons in a network receiving “priming activity” (such as neurotransmitters, paracrine signals, hormones, and others) minutes to days prior will show a lower threshold for induction of long term potentiation (LTP). Other studies have demonstrated that activation of NMDARs can raise the stimulation threshold for induction of LTP[1]. Thus, similar inputs on groups of neurons may induce LTP in some but not others based on prior activity of those neurons, providing a mechanism for neuronal memory allocation via synaptic tagging[9].

Several pathways have been implicated in these metaplastic effects including autophosphorylation of αCaMKII [10] and changes in NMDA receptor subunit composition[11][10][12]. In addition to determining to which neurons memory will be initially allocated, metaplastic effects regulate memory destabilization and reconsolidation. Changes in NMDA receptor subunit composition as well as manipulation of voltage-dependent calcium channels also may make potentiation more or less likely to persist long term in particular neurons.

Synaptic Allocation

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Synaptic allocation pertains to any mechanism that influences how specific synapses come to store a given memory.[1] Intrinsic to the idea of synaptic allocation is the concept that multiple synapses can be activated by a given set of inputs, but specific mechanisms determine which synapses actually go on the encode the memory. Allocation of memories to specific synapses are key to determining where memories are stored.

Synaptic Tag and Capture

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Synaptic activity can generate a synaptic tag, which will allow the stimulated spine to subsequently capture newly transcribed plasticity molecules such as Arc, and can engage the translation and transcription machinery. Weak stimulation can create synaptic tags but will not engage the translation and transcription machinery, whereas strong stimulation will create synaptic tags and also engage the translation and transcription machinery. Newly generated plasticity products (PP) can be captured by any tagged synapses, but untagged synapses are not eligible to receive new PPs. After a certain time period, synapses will lose their tag and return to their initial state. Furthermore, the supply of new PPs will deplete. The tags and new PPs must overlap in time to capture the PPs. [9]

The first evidence of synaptic tag and capture came from in vitro electrophysiological studies which showed that repeated strong tetanization of one stimulating pathway (S1) could produce lasting protein synthesis-dependent LTP in the S1 but not in a second pathway (S2). [13] However, the proteins needed for the maintenance of L-LTP, which were synthesized during the repeated tetanization of the S1 pathway, could be shared by synapses tagged during S2 tetanization and support L-LTP in the second pathway. S-LTP was sufficient to tag the S2 set of synapses which were then capable of capturing the proteins needed for the maintenance of L-LTP was generated by repeated tetanization of the S1 pathway.

Recent studies have shown temporal and spatial properties of the synaptic tagging. S-LTP of some neurons can transform into L-LTP by capturing plasticity-related proteins (PRP) produced in response to L-LTP of other spines. In addition, spine can be induced to change volume by a potentiated spine nearby.[14] The synaptic tag is inversely related to time between inducing stimuli, and is said to be temporarily asymmetrical. Furthermore, the tagging is also inversely related to the distance between spines, an important spatial properties of tagging. Conversely confirming the temporal and spatial properties of the synaptic tagging, subsequent imaging studies revealed that there are not only temporal constraints but also structural constraints that limit synaptic tagging and capture mechanisms.


Spine Clustering

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Spine clustering is another process that is thought to be involved in synaptic allocation. Synaptic clustering refers to the addition of new spines during learning to dendritic sites where other spines had already been added due to a previous training. Spine clustering is thought to result in the amplification of synaptic inputs due to their increase in the induction and propagation of dendritic spikes. [1]

One possible mechanism for spine cluster is the diffusible molecular crosstalk that occurs near activated spines. For example, different studies have shown that signalling molecules synthesized at one spine, such as activated RAS and RHOA, diffuse out and influence spine growth at nearby sites, thus contributing to spine clustering. Another recent study has implicated the Rho GTPase CDC42 as another determinant in spine clustering. CDC42 activation results from glutamate activation and leads to long-term spine volume increases. Interestingly, all these spine clustering molecules were shown to be dependent on CaMKII. Other molecules besides RAS, RHOA, CDC42, and CAMKII are involved in spine clustering, but recent studies have suggested that the overall process of spine clustering may be partly regulated by NMDA receptor activation and nitric oxide stimulation. [15][16]

In many scenarios synaptic clustering can refer to the addition of new spines during training to dendritic sites where other spines had been added in previous training trials. Spine clustering in the motor cortex reflects a morphological mechanism for synaptic storage of specific motor memories. These clustered spines are more stable than non-clustered new spines. This type of addition of spines occurs in a specific pattern, meaning that spines added after one task will not cluster with spines after an alternative task.[17] Loss of spine clustering is also a possibility as shown in some fear conditioning experiments, leading to the net loss of spines in the frontal association cortex, a region strongly associated in fear conditioning, which strongly correlates with memory on recall. Once spines were added after fear extinction had a similar orientation to the spines lost during the original fear conditioning.[18]

Current and Future Research

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Integrating Synaptic and Neuronal Allocation

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Experiments have yet to investigate the interaction of allocative mechanisms between the neuronal and synaptic levels. The two classes of processes are very likely to be interconnected considering the relationship between neurons and synapse in a neuronal network. For example, the synaptic tagging and capture involved in synaptic allocation requires the allocation of the neurons to which the synapses belong to. Moreover, increases in neuronal excitability in a given neuronal ensemble may affect some dendrites more than others, thus biasing memory storage to synapses in dendrites with higher excitability.[19] [20] Similarly, on the recruited neurons displaying increased excitability, specific synapses need to be selected for in order to store the information in the form of synaptic plasticity.

As described earlier, the acquisition and storage of one memory changes the neural circuit in such a way as to affect the storage and properties of another subsequent memory, a phenomenon called metaplasticity. Homosynaptic metaplasticity and heterosynaptic metaplasticity refer to whether the modulation of the subsequent plasticity is at the same or different synapses, respectively. Cellular excitability has been proposed as one of the mechanisms responsible for heterosynaptic metaplasticity.[21] CREB functions through elevating cell excitability as described above, thus it is also possibly involved in synaptic allocation. Synaptic tagging and capture, as introduced in sections above, can result in a weak memory (capable of triggering only E-LTP), which would otherwise be forgotten, but it can be strengthened and stabilized by a strong memory (capable of triggering L-LTP), which is a form of heterosynaptic plasticity.

Future Research

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Despite extensive research into the individual mechanisms of memory allocation, there are few studies investigating the integration of these mechanisms. It has been proposed that understanding the implications of the molecular, cellular and systemic mechanisms of these processes may elucidate how they are coordinated and integrated during memory formation.[1] For example, identifying the plasticity-related products (PRPs) involved in synaptic tagging and capture as well as the upstream and downstream molecules of CREB can help reveal potential interactions. Investigating the functional significance of these mechanisms will require tools that can directly manipulate and image the processes involved in the proposed mechanisms in vivo.[1] For instance, it is possible that the behavioral interactions ascribed to synaptic tagging and capture are caused by protein synthesis-dependent increases in neuromodulators such as dopamine rather than by synaptic tagging mechanisms. Examining the behavioral effects under direct manipulation can help rule out these other possible causes.

Further Reading

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References

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  1. ^ a b c d e f g h i j k l Rogerson, T., Cai, D. J., Frank, A., Sano, Y., Shobe, J., Lopez-Aranda, M. F., & Silva, A. J. Synaptic tagging during memory allocation. Nature Reviews Neuroscience (2014)
  2. ^ Yiu, A. P. et al. Neurons Are Recruited to a Memory Trace Based on Relative Neuronal Excitability Immediately before Training. Neuron 83, 722-735 (2014)
  3. ^ a b Bliss, T. V., & Collingridge, G. L. (1993). A synaptic model of memory: long-term potentiation in the hippocampus. Nature, 361(6407), 31-39.
  4. ^ Nguyen, P. V., & Woo, N. H. (2003). Regulation of hippocampal synaptic plasticity by cyclic AMP-dependent protein kinases. Progress in neurobiology,71(6), 401-437.
  5. ^ Han, J. H., Kushner, S. A., Yiu, A. P., Cole, C. J., Matynia, A., Brown, R. A., ... & Josselyn, S. A. (2007). Neuronal competition and selection during memory formation. science, 316(5823), 457-460.
  6. ^ Han, J. H., Kushner, S. A., Yiu, A. P., Hsiang, H. L. L., Buch, T., Waisman, A., ... & Josselyn, S. A. (2009). Selective erasure of a fear memory. Science, 323(5920), 1492-1496.
  7. ^ Zhou, Y., Won, J., Karlsson, M. G., Zhou, M., Rogerson, T., Balaji, J., ... & Silva, A. J. (2009). CREB regulates excitability and the allocation of memory to subsets of neurons in the amygdala. Nature neuroscience, 12(11), 1438-1443.
  8. ^ Sargin, D., Mercaldo, V., Yiu, A. P., Higgs, G., Han, J. H., Frankland, P. W., & Josselyn, S. A. (2013). CREB regulates spine density of lateral amygdala neurons: implications for memory allocation. Frontiers in behavioral neuroscience, 7.
  9. ^ a b Rudy, J. (2014). Specific mechanisms: Targeting plasticity products. In The neurobiology of Learning and Memory (Second ed., pp. 113-116). Sinauer Associates.
  10. ^ a b Zhang, L., Kirschstein, T., Sommersberg, B., Merkens, M., Manahan-Vaughan, D., Elgersma, Y., & Beck, H. (2005). Hippocampal synaptic metaplasticity requires inhibitory autophosphorylation of Ca2+/calmodulin-dependent kinase II. The Journal of neuroscience, 25(33), 7697-7707.
  11. ^ Lee, M. C., Yasuda, R., & Ehlers, M. D. (2010). Metaplasticity at single glutamatergic synapses. Neuron, 66(6), 859-870.
  12. ^ Cho, K. K., Khibnik, L., Philpot, B. D., & Bear, M. F. (2009). The ratio of NR2A/B NMDA receptor subunits determines the qualities of ocular dominance plasticity in visual cortex. Proceedings of the National Academy of Sciences, 106(13), 5377-5382.
  13. ^ Frey, U. & Morris, R. G. Synaptic tagging: implications for late maintenance of hippocampal long-term potentiation. Trends Neurosci. 21, 181–188 (1998)
  14. ^ Govindarajan, A. et al. The dendritic branch is the preferred integrative unit for protein synthesis-dependent LTP. Neuron 69, 132–146 (2011).
  15. ^ Harvey, C. D. et al. The spread of Ras activity triggered by activation of a single dendritic spine. Science 321, 136–140 (2008).
  16. ^ Murakoshi, H., Wang, H. & Yasuda, R. Local, persistent activation of Rho GTPases during plasticity of single dendritic spines. Nature 472, 100–104 (2011)
  17. ^ Fu, M. et al. Repetitive motor learning induces coordinated formation of clustered dendritic spines in vivo. Nature 483, 92–95 (2012).
  18. ^ Lai, C. S. et al. Opposite effects of fear conditioning and extinction on dendritic spine remodelling. Nature 483, 87–91 (2012).
  19. ^ Larkum, M. E. & Nevian, T. Synaptic clustering by dendritic signalling mechanisms. Curr. Opin. Neurobiol. 18, 321–331 (2008)
  20. ^ Losonczy, A., Makara, J. K. & Magee, J. C. Compartmentalized dendritic plasticity and input feature storage in neurons. Nature 452, 436–441 (2008)
  21. ^ Frick, A. & Johnston, D. Plasticity of dendritic excitability. J. Neurobiol. 64, 100–115 (2005)
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