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Neuronal competition: microcircuit mechanisms define the sparsity of the engram
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ScienceDirect Neuronal competition: microcircuit mechanisms define the sparsity of the engram Priyanka Rao-Ruiz1,8, Julia Yu2,3,8, Steven A Kushner4 and Sheena A Josselyn2,3,5,6,7 Extensive work in computational modeling has highlighted the advantages for employing sparse yet distributed data representation and storage Kanerva (1998), properties that extend to neuronal networks encoding mnemonic information (memory traces or engrams). While neurons that participate in an engram are distributed across multiple brain regions, within each region, the cellular sparsity of the mnemonic representation appears to be quite fixed. Although technological advances have enabled significant progress in identifying and manipulating engrams, relatively little is known about the region-dependent microcircuit rules governing the cellular sparsity of an engram. Here we review recent studies examining the mechanisms that help shape engram architecture and examine how these processes may regulate memory function. We speculate that countervailing forces in local microcircuits contribute to the generation and maintenance of engrams and discuss emerging questions regarding how engrams are formed, stored and used. Addresses 1 Dept. of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, The Netherlands 2 Program in Neurosciences & Mental Health, Hospital for Sick Children, 555 University Ave. Toronto, ON, M5G 1X8, Canada 3 Dept. of Physiology, University of Toronto, Toronto, ON, M5G 1X8, Canada 4 Dept. of Psychiatry, Erasmus MC University Medical Center, Rotterdam, The Netherlands 5 Dept. of Psychology, University of Toronto, Toronto, ON, M5G 1X8, Canada 6 Institute of Medical Sciences, University of Toronto, Toronto, ON, M5G 1X8, Canada 7 Brain, Mind & Consciousness Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ontario M5G 1M1, Canada Corresponding authors: Kushner, Steven A ([email protected]), Josselyn, Sheena A ([email protected]) 8 Co-first authors. Current Opinion in Neurobiology 2019, 56:163–170 This review comes from a themed issue on Neuronal identity Edited by Sacha Nelson and Oliver Hobert
https://doi.org/10.1016/j.conb.2018.10.013 0959-4388/ã 2018 Elsevier Ltd. All rights reserved.
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Introduction Memory of an experience is thought to be represented by long-lasting changes to the brain (referred to as a memory trace or ‘engram’) [1,2,3,4,5,6]. These experienceinduced brain changes may occur at multiple levels (ranging from epigenetic, synaptic, cellular to networklevel modifications) [1,7,8,9–11]. However, considerable progress has been made by focusing on engrams at the level of cell ensemble. Recent findings indicate that an engram is composed of sparsely distributed populations of neurons spanning multiple brain regions. Neural network models of efficient data representation emphasize the benefits of sparse, distributed coding schemes [12–14]. From an evolutionary perspective, therefore, sparse coding may have emerged as an effective solution to balance memory capacity and stability in the face of synapse and cell turnover, ongoing learning and learninginduced remodeling. Viewed from the perspective of the fidelity of memory storage, dedicating too many cells per memory exerts pressure on storage capacity, while dedicating too few cells per memory risks the enduring integrity of memories due to stochastic or disease-related synaptic/cellular loss. This suggests that there may be an optimal number and/or density of cells devoted to an engram. However, the mechanisms underlying engram sparsity and their consequent implications for mnemonic function are only beginning to be understood (Figure 1).
Engram sparsity differs between brain regions To visually identify cells that are critical components of an engram (‘engram cells’), typically researchers have either labeled cells active immediately following a memory retrieval test with immediate early genes (IEGs) or used inducible IEG promoters to ‘tag’ cells active at the time of memory encoding with fluorescent markers or optogenetic/chemogenetic actuators [15,16–23]. Using these techniques, ‘engram cell ensembles’ supporting conditioned fear memories have been observed across many brain [21,24,25]. These findings show that the relative sparsity of engram cells differs considerably between brain regions [15,16–23]. For instance, 2–6% of neurons in the dentate gyrus of the hippocampus (DG) [16,26] are estimated to become part of an engram supporting a conditioned fear memory, compared to 10–20% of neurons in the CA1 region of the hippocampus [26], the lateral nucleus of the amygdala (LA) [15,27] and prefrontal cortex (PFC) [28]. This pattern of results is remarkably consistent across studies, despite the use of Current Opinion in Neurobiology 2019, 54:163–170
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Figure 1
(a)
Learning
Increased excitability
Allocated to engram
Baseline excitability
(b) Principal neuron (high excitability)
Activated principal neuron activates local inhibitory interneuron
Principal neuron (low excitability)
Neuronal inhibition by neghbouring cell activity
PV+ interneuron
Learning
SOM+ interneuron
Cell body inhibition by PV+interneuron
Dendritic inhibition by SOM+ interneuron
Current Opinion in Neurobiology
Schematic depicting allocation process. (a) Eligible excitatory principal neurons (PN) in the lateral amygdala (LA) compete for allocation to an engram. At the time of a learning event, PNs with increased excitability (blue) relative to their neighbours with lower excitability (black) prevail in this competitive process of allocation. Once allocated, these PNs become both necessary (indispensable) and sufficient (inducing) components of the engram supporting this memory. (b) In addition to changes in intrinsic excitability, engram architecture is also mediated by local microcircuitry critically involving the contribution of GABAergic interneurons. Parvalbumin-positive (PV+) interneurons in the LA, and somatostatin-positive (SOM +) interneurons in the dentate gyrus (DG) of the hippocampus may amplify differences in PN excitability and serve to constrain engram sparsity.
diverse approaches for engram labeling (including labelling with different IEGs or using different inducible IEG promoters, Arc versus c-Fos) and distinct types of conditioned fear (auditory versus contextual fear conditioning). What are the physiological principles that govern the relative size of an engram across brain regions? Could the stereotyped size of an engram in a given brain region reflect that region’s temporal requirement for memory retrieval? The results from a recent study suggest the relative sparsity of a memory trace within a given brain region may be related to memory consolidation. Using inducible IEG promoters to visualize engram neurons supporting a contextual fear memory in the PFC, DG, and basolateral amygdala (BLA), Kitamura et al. [28] Current Opinion in Neurobiology 2019, 54:163–170
showed that a greater proportion of PFC engram cells are active during retrieval of a remote fear memory (as opposed to retrieval of a recent fear memory), while the opposite pattern of results was observed in for engram cells in the DG. In contrast, a consistent proportion of BLA engram cells were activated at both recent and remote time points. Engram sparsity, therefore, may be modified with systems consolidation, a process during which memories reorganize over time, initially depending more on the hippocampus and gradually become more reliant on the cortex [29]. It may be that the very sparse engram in the DG reflects its time-limited role in supporting memory expression, while BLA and PFC microcircuitry evolved to have larger engrams because these regions serve more enduring roles in memory www.sciencedirect.com
Allocation to an engram Rao-Ruiz et al. 165
retrieval [28]. Alternatively, it could be that different brain regions require a smaller engram to properly index or cue the retrieval of memories stored in the cortex. Future studies are needed to examine how engrams may evolve over time and whether the sparsity in different regions is related to systems consolidation.
Engram sparsity is constant across memories within a brain region Although engram sparsity differs between brain regions, the proportion of engram cells within a given brain region is remarkably stable. For instance, the size of the LA component of an engram remains quite consistent, irrespective of the content (conditioned fear [27] versus conditioned reward [30]) or strength [15,27] of the associative memory. Interestingly, although increasing the strength of a contextual fear memory does not recruit a larger engram, it does engage greater synaptic connectivity between ‘engram cells’ in the CA1 and CA3 regions of dorsal hippocampus [31]. That the size of the engram does not vary with type of training or memory strength suggests the existence of fundamental gating mechanisms that ensure engram sparsity. The role of neuronal intrinsic excitability
One gating mechanism that serves to constrain engram size and sparsity is intrinsic neuronal excitability. This was first examined in the LA, a brain region established as critical for the encoding and expression of auditory fear conditioning memories [32–36]. These studies grew out of the observation across several labs that although most LA excitatory principal neurons (PNs) seem to be anatomically and functionally ‘eligible’ to become part of an engram supporting a conditioned fear memory [37– 39], only a relatively small proportion (10–20%) of these PNs are recruited (or allocated) to the engram [15 ,19,27,38–41,42]. Subsequent manipulation experiments showed that it was possible to bias the recruitment of individual PNs to an engram by manipulating their relative excitability. Using virally-mediated expression of various constructs to artificially increase excitability in a sparse, random population (15%) of LA PNs, several groups have now shown that enhancing the excitability of a subset of PNs immediately before training strongly biases their allocation to the engram supporting the auditory fear memory [27,43–45,46,47–49]. These allocated PNs become necessary (indispensable) and sufficient (inducing) components of the engram supporting the conditioned fear memory [1]. Ablating or silencing this small subpopulation of PNs after training impaired subsequent memory expression, whereas silencing a similar number of control PNs did not [43,50,51]. Conversely, artificial reactivation of allocated PNs induced freezing (the conditioned response) in the absence of an external sensory retrieval cue (the tone or contextual conditioned stimulus) [44,52]. www.sciencedirect.com
The finding that increasing excitability in a small proportion of random PNs biases their allocation is mirrored by studies in which decreasing excitability in a similarly small proportion of randomly selected PNs immediately before training substantially reduced the likelihood that these neurons were integrated into the engram [27,43,44,49]. Importantly, neither the strength of the memory nor the sparsity of the engram was affected by decreasing excitability in this small population of PNs, presumably because a sufficient number of non-manipulated PNs were readily available for engram allocation. That the size of the engram remained constant even with manipulations that increased or decreased neuronal excitability of a small population of PNs suggests that engram allocation is a competitive, rather than cell autonomous, process. Evidence for similar competitive processes for allocation to an engram has subsequently been found in the dorsal hippocampal CA1 and DG regions [46,47,48], as well as the insular [53] and retrosplenial cortices [54]. One caveat of the above studies is that neuronal excitability was artificially manipulated. Does neuronal competition-based on excitability govern allocation to an engram under normal physiological conditions? To the best of our knowledge, only one study has examined the endogenous mechanisms by which LA PNs are allocated to a memory trace. Using a mouse that transiently ( 4 hour) expresses a fluorescent protein following neuronal activity (destabilized Venus fluorescent protein driven by an Arc promoter, Arc::dVenus mice), 10% of LA PNs were observed to be endogenously active (dVenus+) in a pre-training baseline period, and importantly, 92.6% of these cells became part of the engram supporting that fear memory [15]. Notably, whole-cell recordings revealed that Arc::dVenus+ neurons were already more excitable than their Arc::dVenus neighbors before training. This finding provides strong evidence that, under normal physiological conditions, PNs with higher excitability at the time of training are preferentially allocated to an engram. Together these data support a model whereby neuronal competition-based on the intrinsic excitability of individual PNs is a critical determinant for cellular allocation to an engram. The role of local microcircuitry
Computational studies have highlighted the importance of local microcircuitry, specifically the interaction between excitatory PNs and inhibitory neurons [including parvalbumin positive (PV+) and somatostatin positive (SOM+)] in regulating engram sparsity. For instance in the LA, highly excitable PNs may inhibit neighboring PNs via disynaptic inhibition, thereby excluding neighboring PNs from becoming part of the engram [55,56,57]. This process would serve to magnify the difference in activity between allocated engram PNs (recruited to the engram) and non-allocated PNs, thereby helping to ‘cap’ the number of PNs recruited into an engram [57]. These modeling Current Opinion in Neurobiology 2019, 54:163–170
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studies suggest that maintaining sparsity of an engram is important for memory specificity. Experimental work confirms many results of these modeling studies. For instance, the LA and more ventral basal amygdala exhibit disynaptic, feedforward inhibition, primarily mediated by PV + interneurons [58,59]. In this region, PV interneurons form a broad, inter-connected inhibitory network that tightly controls the activity of excitatory PNs via perisomatic contacts onto PNs [58,59]. Chemogenetically silencing PV interneurons in the LA and basal amygdala during auditory fear conditioning resulted in an engram with increased size [60]. In the DG, SOM+ interneurons mediate feedforward inhibition by targeting the dendrites of excitatory granule cells [61]. Silencing SOM interneurons during context fear conditioning increased the size of a contextual fear engram [61]. Together, these findings indicate that the ongoing activity of interneurons in different brain regions is critical for maintaining engram sparsity. It would be interesting to determine how the fidelity of the resulting memory is impacted by a larger engram size. In addition, as distinct interneuron subtypes show a biased subcellular distribution of their postsynaptic targets [62], the exact spatial-mechanism and temporal-mechanism by which inhibitory neurons constrain engram size is a critical unsolved issue. Inhibitory interneurons may also help shape an engram during memory consolidation by modifying which PNs remain part of an engram or engram cell firing properties. The observation that only a subset of PNs allocated to a given engram are reactivated during retrieval [19,26] suggests that the engram is refed over time [63]. In line with this notion, learning appears to initially elicit intrinsic changes in CA1 PNs, and only subsequently in the excitability of SOM+ interneurons, a mechanistic sequence that may serve to gate information flow to downstream brain regions [64]. Furthermore, at a systems level, long-range inhibitory connections [65] may also play a critical, but as yet unidentified, role in determining the sparsity and brain-wide distribution of engrams as memories consolidate over time. A challenge moving forward will be to understand how distinct populations of interneurons differentially modulate the activity of excitatory neurons to mediate the nature of what is encoded. Together, these results suggest two complementary processes as cellular determinants of engram sparsity: first, neuronal intrinsic excitability, and, second, local microcircuit architecture. How these, and other, mechanisms interact to sculpt an engram remains to be determined. Also unknown is how distinct cognitive features of a memory are influenced by varying engram sparsity. For example, what is the minimum engram size that is Current Opinion in Neurobiology 2019, 54:163–170
capable of supporting long-term memory? And to what extent might increasing engram size result in more robust memories without a loss in information-specificity? Future studies combining modeling and experimental data might be a particularly advantageous approach for providing deeper insights into these fundamental questions. In the following sections, we touch upon additional open questions and issues related to the principles that govern the size and (co)allocation of memories to sparse populations of neurons.
How are memories organized across engrams? Memories beget memories of related events. One advantage of sparse coding is that each local node in the distributed network can be involved in multiple mnemonic representations. This may increase the efficiency and flexibility of memory storage. For instance, memories that are linked in time can be encoded by overlapping populations of neurons [50,66]. The linking of memories to overlapping populations occurs as a result of transient increases in the excitability of allocated neurons [15,27,43,44,46,66] and competitive interactions between PNs mediated by disynaptic inhibitory connections [50]. But how does each memory preserve its own identity? To probe this question, it may be important to examine engrams at the level of the synapse. Processes such as spine clustering [67,68] and synaptic tagging and capture [69] (also reviewed in Ref. [52]) provide compelling models of how memories can be linked at a sub cellular level. For instance, long-lasting synaptic plasticity induced by a strong memory can stabilize sub threshold metaplasticity induced by a subsequent weaker memory that is allocated to a different synapse on the same neuron, within a temporally defined window [68,70]. The question of how co-allocated memories maintain their unique identity is less well understood. Abdou et al. [71] showed that although related fear memories are allocated to overlapping cellular ensembles in the LA, each memory retains its distinct identity via the engagement of discrete synaptic populations. It will be interesting to determine whether these rules of engram engagement apply to other brain regions such as the hippocampus, where the concomitant emergence of time-limited versus stable representations of context have been observed in a subfield-specific manner [72]. Furthermore, it is tempting to speculate that memory organization might make use of temporally-constrained co-allocation as a neural substrate to link objects or words, as measured by semantic and phonemic fluency tests. Deciphering the underpinnings of memory (co)allocation may therefore be critical in helping us understand both age-related cognitive decline [73] as well as the memory loss observed in Alzheimer’s disease [74]. www.sciencedirect.com
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While the field has come a long way in understanding the principles that guide neuronal [27,43,44,75] and synaptic [71] allocation, the molecular and physiological kinetics of how engram cells encode and link memor(ies) have yet to be elucidated. The rapid evolution of techniques with increasingly precise temporal and spatial functionality, such as dual-eGRASP for examining synapses between ‘engram cells’ [31], in combination with cell-specific [76] and synapse-specific [77] transcriptomics/proteomics holds considerable promise in determining the molecular mechanisms that underlie the establishment and maintenance of engrams supporting different types of memory. These techniques, especially in combination with more traditional tools, will enable further progress in probing the mechanisms that underlie neuronal competition, and the consequences of sparsity in memory formation. In addition, they may help identify precisely what engram cells encode. For instance, a recent study combined inducible IEG tagging of CA1 neurons active during context exposure and in vivo electrophysiological identification of place cells [78]. Consistent with previous reports [26], a sparse population of neurons (20%) was identified as ‘engram cells’. Intriguingly, though, while most engram cells were also identified as place cells, the majority of place cells were not engram cells. That is, most engram cells did not consistently encode spatial location (they preferentially remapped their place fields), yet their activity was able to discriminate between a novel versus familiar context. On the other hand, most place cells (cells that encoded a stable spatial map between encoding and recall) were largely non-engram cells. These findings support the idea that the engram comprise a sparse subset of neurons activated by stimuli present at the time of learning but raise a number of tantalizing questions that await further investigation. Increased understanding of the fundamental neural processes meditating memory formation, storage and flexible retrieval will likely involve the use of novel approaches to answer age-old questions of how we learn and remember.
Acknowledgements We thank Albert Park for making the figure. This work was funded by a NWO VENI (016.171.033) and ZonMw TOP grant (40-00812-98-15030) to PRR, a Restracomp Fellowship from the Hospital for Sick Children to JY, NWO VIDI (017.106.384), NWO ALW (834.12.002) and ZonMw TOP grant (40-00812-98-15030) to SAK and Canadian Institute of Health Research (CIHR) and Canadian Institute for Advanced Research (CIFAR) grants to SAJ.
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the mechanism underlying this effect involves a winner-take-all competition that is mediated by ongoing excitability in PNs and an important contribution of inhibitory interneurons. See also Cai et al (2016). 51. Berndt A, Lee SY, Wietek J, Ramakrishnan C, Steinberg EE, Rashid AJ, Kim H, Park S, Santoro A, Frankland PW et al.: Structural foundations of optogenetics: determinants of channelrhodopsin ion selectivity. Proc Natl Acad Sci U S A 2016, 113:822-829. 52. Rogerson T, Cai DJ, Frank A, Sano Y, Shobe J, Lopez-Aranda MF, Silva AJ: Synaptic tagging during memory allocation. Nat Rev Neurosci 2014, 15:157-169. 53. Sano Y, Shobe JL, Zhou M, Huang S, Shuman T, Cai DJ, Golshani P, Kamata M, Silva AJ: CREB regulates memory allocation in the insular cortex. Curr Biol: CB 2014, 24:28332837. 54. Czajkowski R, Jayaprakash B, Wiltgen B, Rogerson T, GuzmanKarlsson MC, Barth AL, Trachtenberg JT, Silva AJ: Encoding and storage of spatial information in the retrosplenial cortex. Proc Natl Acad Sci U S A 2014, 111:8661-8666. 55. Feng F, Samarth P, Pare D, Nair SS: Mechanisms underlying the formation of the amygdalar fear memory trace: A computational perspective. Neuroscience 2016, 322:370-376. Using a biophysically-inspired 1000-cell computational model of the lateral amygdala, these authors examine the contributions of intrinsic excitability and local inhibition on engram architecture. They conclude that the size of the engram is controlled by the overall level of inhibition within the lateral amygdala. 56. Kim D, Pare D, Nair SS: Assignment of model amygdala neurons to the fear memory trace depends on competitive synaptic interactions. J Neurosci 2013, 33:14354-14358. 57. Kim D, Samarth P, Feng F, Pare D, Nair SS: Synaptic competition in the lateral amygdala and the stimulus specificity of conditioned fear: a biophysical modeling study. Brain Struct Funct 2016, 221:2163-2182. Using a realistic, multi-compartment, conductance-based model of the lateral amygdala, this study showed that the number of cells recruited to a conditioned fear engram was significantly altered when plasticity was prevented in either excitatory-excitatory synapses or inhibitory-excitatory synapses. These findings highlight the importance of both intrinsic and synaptic plasticity in excitatory and inhibitory circuits as a mechanism for neuronal competition. 58. Ehrlich I, Humeau Y, Grenier F, Ciocchi S, Herry C, Luthi A: Amygdala inhibitory circuits and the control of fear memory. Neuron 2009, 62:757-771. 59. Lucas EK, Jegarl AM, Morishita H, Clem RL: Multimodal and sitespecific plasticity of amygdala parvalbumin interneurons after fear learning. Neuron 2016, 91:629-643. This paper shows that parvalbumin-containing interneurons (PV INs) are important for input gating and learning-related plasticity in the amygdala. They find that plasticity in PV INs differs in the lateral and basal amygdala, and that lateral amygdala PV INs are preferentially involved in mediating feedforward inhibition. These findings povide insight into the mechanisms by which PV INs may regulate the recruitment of PNs in fear learning and make a compelling case for clearly delineating the lateral amygdala from the basal amygdala. 60. Morrison DJ, Rashid AJ, Yiu AP, Yan C, Frankland PW, Josselyn SA: Parvalbumin interneurons constrain the size of the lateral amygdala engram. Neurobiol Learn Mem 2016, 135:91-99. First study to systematically examine the relationship between engram size and memory strength by manipulating the intensity of the unconditioned stimulus (footshock) in an auditory fear conditioning experiment. While an intense footshock resulting in stronger memory (behaviourally), it did not increase the size of the engram in the lateral amygdala. However, silencing parvalbumin-containing interneurons (PV INs) in the lateral and basal amygdala nuclei during training increased the size of the engram. These results suggest that 1) the size of an auditory fear engram in the lateral amygdala does not encode memory strength, and, 2) the sparsity of a memory trace is modulated by local inhibitory microcircuits. 61. Stefanelli T, Bertollini C, Luscher C, Muller D, Mendez P: Hippocampal somatostatin interneurons control the size of neuronal memory ensembles. Neuron 2016, 89:1074-1085. This paper examined the role of GABAergic interneurons in the dorsal hippocampal dentate gyrus (DG) in engram sparsity and memory www.sciencedirect.com
strength. While chemogenetically inhibiting parvalbumin-containing (PV +) interneurons had no effect on the size of a contextual fear engram, silencing somatostatin-containing (SOM+) interneurons increased the size of the engram and strengthened memory. In subsequent electrophysiological recordings, the authors speculate that in the DG, lateral inhibition may be responsible for modulating the size of the engram. 62. Kepecs A, Fishell G: Interneuron cell types are fit to function. Nature 2014, 505:318-326. 63. Richards BA, Frankland PW: The conjunctive trace. Hippocampus 2013, 23:207-212. 64. McKay BM, Oh MM, Disterhoft JF: Learning increases intrinsic excitability of hippocampal interneurons. J Neurosci 2013, 33:5499-5506. 65. Saffari R, Teng Z, Zhang M, Kravchenko M, Hohoff C, Ambree O, Zhang W: NPY+-, but not PV+- GABAergic neurons mediated long-range inhibition from infra- to prelimbic cortex. Transl Psychiatry 2016, 6:e736. 66. Cai DJ, Aharoni D, Shuman T, Shobe J, Biane J, Song W, Wei B, Veshkini M, La-Vu M, Lou J et al.: A shared neural ensemble links distinct contextual memories encoded close in time. Nature 2016, 534:115-118. This study used elegant real-timein vivo calcium imaging in the dorsal hippocampus of freely-behaving mice to show that neuronal representations of two distinct contexts overlap if mice are exposed to both contexts within 5 h, causing the memories to become linked by virtue of engaging overlapping engrams. Older mice show a deficit in this memory linking. Together with Rashid et al. (2016), these studies established the phenomena whereby memory linking occurs due to overlapping engrams of events that were experienced in close temporal proximity. 67. Govindarajan A, Kelleher RJ, Tonegawa S: A clustered plasticity model of long-term memory engrams. Nat Rev Neurosci 2006, 7:575-583. 68. Kastellakis G, Silva AJ, Poirazi P: Linking memories across time via neuronal and dendritic overlaps in model neurons with active dendrites. Cell Rep 2016, 17:1491-1504. Here, the authors built a neural network model that integrates experimental evidence for intrinsic excitability, homeostatic plasticity, and synaptic tagging and capture in associative memory formation. The model generated several hypotheses for how engram sparsity may be determined by the locus of the distribution of plasticity-related proteins across cellular subcompartments. In particular, the model provided support for dendritic clustering as a mechanism for co-allocation of a weak and strong memory. 69. Frey U, Morris RG: Synaptic tagging: implications for late maintenance of hippocampal long-term potentiation. Trends Neurosci 1998, 21:181-188. 70. Ramiro-Cortes Y, Hobbiss AF, Israely I: Synaptic competition in structural plasticity and cognitive function. Philos Trans R Soc Lond B Biol Sci 2014, 369 20130157. 71. Abdou K, Shehata M, Choko K, Nishizono H, Matsuo M, Muramatsu SI, Inokuchi K: Synapse-specific representation of the identity of overlapping memory engrams. Science 2018, 360:1227-1231. This study takes advantage of cutting-edge optogenetic and virallymediated circuit mapping techniques to investigate how the identity of specific memories is maintained if memories for multiple learning events are encoded by overlapping ensembles. The authors conclude that memory identity is delineated not only in terms of the population of cells that are activated during training and recall, but by plasticity occurring at specific synapses that underlie each memory trace. 72. Hainmueller T, Bartos M: Parallel emergence of stable and dynamic memory engrams in the hippocampus. Nature 2018. 73. Brickman AM, Paul RH, Cohen RA, Williams LM, MacGregor KL, Jefferson AL, Tate DF, Gunstad J, Gordon E: Category and letter verbal fluency across the adult lifespan: relationship to EEG theta power. Arch Clin Neuropsychol 2005, 20:561-573. 74. Chan AS, Butters N, Paulsen JS, Salmon DP, Swenson MR, Maloney LT: An assessment of the semantic network in patients with Alzheimer’s disease. J Cogn Neurosci 1993, 5:254-261.
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75. Sehgal M, Ehlers VL, Moyer JR Jr: Learning enhances intrinsic excitability in a subset of lateral amygdala neurons. Learn Mem 2014, 21:161-170. 76. Zeisel A, Munoz-Manchado AB, Codeluppi S, Lonnerberg P, La Manno G, Jureus A, Marques S, Munguba H, He L, Betsholtz C et al.: Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 2015, 347:1138-1142. 77. Biesemann C, Gronborg M, Luquet E, Wichert SP, Bernard V, Bungers SR, Cooper B, Varoqueaux F, Li L, Byrne JA et al.: Proteomic screening of glutamatergic mouse brain synaptosomes isolated by fluorescence activated sorting. EMBO J 2014, 33:157-170.
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78. Tanaka KZ, He H, Tomar A, Niisato K, Huang AJY, McHugh TJ: The hippocampal engram maps experience but not place. Science 2018, 361:392. This study combined "engram tagging" strategies (using an inducible cFos promoter) within vivo electrophysiological identification of place cells during context exploration. They showed that a sparse population of neurons were identified as ‘engram cells’ based on c-Fos expression during training (20%). While 80% of engram cells were also ‘place cells’ (i.e. encoded a specific spatial location), only 25% of place cells were engram cells. That is, most engram cells did not consistently encode spatial location, yet their activity was able to discriminate between a novel vs. familiar context.
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ScienceDirect Neuronal competition: microcircuit mechanisms define the sparsity of the engram Priyanka Rao-Ruiz1,8, Julia Yu2,3,8, Steven A Kushner4 and Sheena A Josselyn2,3,5,6,7 Extensive work in computational modeling has highlighted the advantages for employing sparse yet distributed data representation and storage Kanerva (1998), properties that extend to neuronal networks encoding mnemonic information (memory traces or engrams). While neurons that participate in an engram are distributed across multiple brain regions, within each region, the cellular sparsity of the mnemonic representation appears to be quite fixed. Although technological advances have enabled significant progress in identifying and manipulating engrams, relatively little is known about the region-dependent microcircuit rules governing the cellular sparsity of an engram. Here we review recent studies examining the mechanisms that help shape engram architecture and examine how these processes may regulate memory function. We speculate that countervailing forces in local microcircuits contribute to the generation and maintenance of engrams and discuss emerging questions regarding how engrams are formed, stored and used. Addresses 1 Dept. of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, The Netherlands 2 Program in Neurosciences & Mental Health, Hospital for Sick Children, 555 University Ave. Toronto, ON, M5G 1X8, Canada 3 Dept. of Physiology, University of Toronto, Toronto, ON, M5G 1X8, Canada 4 Dept. of Psychiatry, Erasmus MC University Medical Center, Rotterdam, The Netherlands 5 Dept. of Psychology, University of Toronto, Toronto, ON, M5G 1X8, Canada 6 Institute of Medical Sciences, University of Toronto, Toronto, ON, M5G 1X8, Canada 7 Brain, Mind & Consciousness Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ontario M5G 1M1, Canada Corresponding authors: Kushner, Steven A ([email protected]), Josselyn, Sheena A ([email protected]) 8 Co-first authors. Current Opinion in Neurobiology 2019, 56:163–170 This review comes from a themed issue on Neuronal identity Edited by Sacha Nelson and Oliver Hobert
https://doi.org/10.1016/j.conb.2018.10.013 0959-4388/ã 2018 Elsevier Ltd. All rights reserved.
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Introduction Memory of an experience is thought to be represented by long-lasting changes to the brain (referred to as a memory trace or ‘engram’) [1,2,3,4,5,6]. These experienceinduced brain changes may occur at multiple levels (ranging from epigenetic, synaptic, cellular to networklevel modifications) [1,7,8,9–11]. However, considerable progress has been made by focusing on engrams at the level of cell ensemble. Recent findings indicate that an engram is composed of sparsely distributed populations of neurons spanning multiple brain regions. Neural network models of efficient data representation emphasize the benefits of sparse, distributed coding schemes [12–14]. From an evolutionary perspective, therefore, sparse coding may have emerged as an effective solution to balance memory capacity and stability in the face of synapse and cell turnover, ongoing learning and learninginduced remodeling. Viewed from the perspective of the fidelity of memory storage, dedicating too many cells per memory exerts pressure on storage capacity, while dedicating too few cells per memory risks the enduring integrity of memories due to stochastic or disease-related synaptic/cellular loss. This suggests that there may be an optimal number and/or density of cells devoted to an engram. However, the mechanisms underlying engram sparsity and their consequent implications for mnemonic function are only beginning to be understood (Figure 1).
Engram sparsity differs between brain regions To visually identify cells that are critical components of an engram (‘engram cells’), typically researchers have either labeled cells active immediately following a memory retrieval test with immediate early genes (IEGs) or used inducible IEG promoters to ‘tag’ cells active at the time of memory encoding with fluorescent markers or optogenetic/chemogenetic actuators [15,16–23]. Using these techniques, ‘engram cell ensembles’ supporting conditioned fear memories have been observed across many brain [21,24,25]. These findings show that the relative sparsity of engram cells differs considerably between brain regions [15,16–23]. For instance, 2–6% of neurons in the dentate gyrus of the hippocampus (DG) [16,26] are estimated to become part of an engram supporting a conditioned fear memory, compared to 10–20% of neurons in the CA1 region of the hippocampus [26], the lateral nucleus of the amygdala (LA) [15,27] and prefrontal cortex (PFC) [28]. This pattern of results is remarkably consistent across studies, despite the use of Current Opinion in Neurobiology 2019, 54:163–170
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Figure 1
(a)
Learning
Increased excitability
Allocated to engram
Baseline excitability
(b) Principal neuron (high excitability)
Activated principal neuron activates local inhibitory interneuron
Principal neuron (low excitability)
Neuronal inhibition by neghbouring cell activity
PV+ interneuron
Learning
SOM+ interneuron
Cell body inhibition by PV+interneuron
Dendritic inhibition by SOM+ interneuron
Current Opinion in Neurobiology
Schematic depicting allocation process. (a) Eligible excitatory principal neurons (PN) in the lateral amygdala (LA) compete for allocation to an engram. At the time of a learning event, PNs with increased excitability (blue) relative to their neighbours with lower excitability (black) prevail in this competitive process of allocation. Once allocated, these PNs become both necessary (indispensable) and sufficient (inducing) components of the engram supporting this memory. (b) In addition to changes in intrinsic excitability, engram architecture is also mediated by local microcircuitry critically involving the contribution of GABAergic interneurons. Parvalbumin-positive (PV+) interneurons in the LA, and somatostatin-positive (SOM +) interneurons in the dentate gyrus (DG) of the hippocampus may amplify differences in PN excitability and serve to constrain engram sparsity.
diverse approaches for engram labeling (including labelling with different IEGs or using different inducible IEG promoters, Arc versus c-Fos) and distinct types of conditioned fear (auditory versus contextual fear conditioning). What are the physiological principles that govern the relative size of an engram across brain regions? Could the stereotyped size of an engram in a given brain region reflect that region’s temporal requirement for memory retrieval? The results from a recent study suggest the relative sparsity of a memory trace within a given brain region may be related to memory consolidation. Using inducible IEG promoters to visualize engram neurons supporting a contextual fear memory in the PFC, DG, and basolateral amygdala (BLA), Kitamura et al. [28] Current Opinion in Neurobiology 2019, 54:163–170
showed that a greater proportion of PFC engram cells are active during retrieval of a remote fear memory (as opposed to retrieval of a recent fear memory), while the opposite pattern of results was observed in for engram cells in the DG. In contrast, a consistent proportion of BLA engram cells were activated at both recent and remote time points. Engram sparsity, therefore, may be modified with systems consolidation, a process during which memories reorganize over time, initially depending more on the hippocampus and gradually become more reliant on the cortex [29]. It may be that the very sparse engram in the DG reflects its time-limited role in supporting memory expression, while BLA and PFC microcircuitry evolved to have larger engrams because these regions serve more enduring roles in memory www.sciencedirect.com
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retrieval [28]. Alternatively, it could be that different brain regions require a smaller engram to properly index or cue the retrieval of memories stored in the cortex. Future studies are needed to examine how engrams may evolve over time and whether the sparsity in different regions is related to systems consolidation.
Engram sparsity is constant across memories within a brain region Although engram sparsity differs between brain regions, the proportion of engram cells within a given brain region is remarkably stable. For instance, the size of the LA component of an engram remains quite consistent, irrespective of the content (conditioned fear [27] versus conditioned reward [30]) or strength [15,27] of the associative memory. Interestingly, although increasing the strength of a contextual fear memory does not recruit a larger engram, it does engage greater synaptic connectivity between ‘engram cells’ in the CA1 and CA3 regions of dorsal hippocampus [31]. That the size of the engram does not vary with type of training or memory strength suggests the existence of fundamental gating mechanisms that ensure engram sparsity. The role of neuronal intrinsic excitability
One gating mechanism that serves to constrain engram size and sparsity is intrinsic neuronal excitability. This was first examined in the LA, a brain region established as critical for the encoding and expression of auditory fear conditioning memories [32–36]. These studies grew out of the observation across several labs that although most LA excitatory principal neurons (PNs) seem to be anatomically and functionally ‘eligible’ to become part of an engram supporting a conditioned fear memory [37– 39], only a relatively small proportion (10–20%) of these PNs are recruited (or allocated) to the engram [15 ,19,27,38–41,42]. Subsequent manipulation experiments showed that it was possible to bias the recruitment of individual PNs to an engram by manipulating their relative excitability. Using virally-mediated expression of various constructs to artificially increase excitability in a sparse, random population (15%) of LA PNs, several groups have now shown that enhancing the excitability of a subset of PNs immediately before training strongly biases their allocation to the engram supporting the auditory fear memory [27,43–45,46,47–49]. These allocated PNs become necessary (indispensable) and sufficient (inducing) components of the engram supporting the conditioned fear memory [1]. Ablating or silencing this small subpopulation of PNs after training impaired subsequent memory expression, whereas silencing a similar number of control PNs did not [43,50,51]. Conversely, artificial reactivation of allocated PNs induced freezing (the conditioned response) in the absence of an external sensory retrieval cue (the tone or contextual conditioned stimulus) [44,52]. www.sciencedirect.com
The finding that increasing excitability in a small proportion of random PNs biases their allocation is mirrored by studies in which decreasing excitability in a similarly small proportion of randomly selected PNs immediately before training substantially reduced the likelihood that these neurons were integrated into the engram [27,43,44,49]. Importantly, neither the strength of the memory nor the sparsity of the engram was affected by decreasing excitability in this small population of PNs, presumably because a sufficient number of non-manipulated PNs were readily available for engram allocation. That the size of the engram remained constant even with manipulations that increased or decreased neuronal excitability of a small population of PNs suggests that engram allocation is a competitive, rather than cell autonomous, process. Evidence for similar competitive processes for allocation to an engram has subsequently been found in the dorsal hippocampal CA1 and DG regions [46,47,48], as well as the insular [53] and retrosplenial cortices [54]. One caveat of the above studies is that neuronal excitability was artificially manipulated. Does neuronal competition-based on excitability govern allocation to an engram under normal physiological conditions? To the best of our knowledge, only one study has examined the endogenous mechanisms by which LA PNs are allocated to a memory trace. Using a mouse that transiently ( 4 hour) expresses a fluorescent protein following neuronal activity (destabilized Venus fluorescent protein driven by an Arc promoter, Arc::dVenus mice), 10% of LA PNs were observed to be endogenously active (dVenus+) in a pre-training baseline period, and importantly, 92.6% of these cells became part of the engram supporting that fear memory [15]. Notably, whole-cell recordings revealed that Arc::dVenus+ neurons were already more excitable than their Arc::dVenus neighbors before training. This finding provides strong evidence that, under normal physiological conditions, PNs with higher excitability at the time of training are preferentially allocated to an engram. Together these data support a model whereby neuronal competition-based on the intrinsic excitability of individual PNs is a critical determinant for cellular allocation to an engram. The role of local microcircuitry
Computational studies have highlighted the importance of local microcircuitry, specifically the interaction between excitatory PNs and inhibitory neurons [including parvalbumin positive (PV+) and somatostatin positive (SOM+)] in regulating engram sparsity. For instance in the LA, highly excitable PNs may inhibit neighboring PNs via disynaptic inhibition, thereby excluding neighboring PNs from becoming part of the engram [55,56,57]. This process would serve to magnify the difference in activity between allocated engram PNs (recruited to the engram) and non-allocated PNs, thereby helping to ‘cap’ the number of PNs recruited into an engram [57]. These modeling Current Opinion in Neurobiology 2019, 54:163–170
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studies suggest that maintaining sparsity of an engram is important for memory specificity. Experimental work confirms many results of these modeling studies. For instance, the LA and more ventral basal amygdala exhibit disynaptic, feedforward inhibition, primarily mediated by PV + interneurons [58,59]. In this region, PV interneurons form a broad, inter-connected inhibitory network that tightly controls the activity of excitatory PNs via perisomatic contacts onto PNs [58,59]. Chemogenetically silencing PV interneurons in the LA and basal amygdala during auditory fear conditioning resulted in an engram with increased size [60]. In the DG, SOM+ interneurons mediate feedforward inhibition by targeting the dendrites of excitatory granule cells [61]. Silencing SOM interneurons during context fear conditioning increased the size of a contextual fear engram [61]. Together, these findings indicate that the ongoing activity of interneurons in different brain regions is critical for maintaining engram sparsity. It would be interesting to determine how the fidelity of the resulting memory is impacted by a larger engram size. In addition, as distinct interneuron subtypes show a biased subcellular distribution of their postsynaptic targets [62], the exact spatial-mechanism and temporal-mechanism by which inhibitory neurons constrain engram size is a critical unsolved issue. Inhibitory interneurons may also help shape an engram during memory consolidation by modifying which PNs remain part of an engram or engram cell firing properties. The observation that only a subset of PNs allocated to a given engram are reactivated during retrieval [19,26] suggests that the engram is refed over time [63]. In line with this notion, learning appears to initially elicit intrinsic changes in CA1 PNs, and only subsequently in the excitability of SOM+ interneurons, a mechanistic sequence that may serve to gate information flow to downstream brain regions [64]. Furthermore, at a systems level, long-range inhibitory connections [65] may also play a critical, but as yet unidentified, role in determining the sparsity and brain-wide distribution of engrams as memories consolidate over time. A challenge moving forward will be to understand how distinct populations of interneurons differentially modulate the activity of excitatory neurons to mediate the nature of what is encoded. Together, these results suggest two complementary processes as cellular determinants of engram sparsity: first, neuronal intrinsic excitability, and, second, local microcircuit architecture. How these, and other, mechanisms interact to sculpt an engram remains to be determined. Also unknown is how distinct cognitive features of a memory are influenced by varying engram sparsity. For example, what is the minimum engram size that is Current Opinion in Neurobiology 2019, 54:163–170
capable of supporting long-term memory? And to what extent might increasing engram size result in more robust memories without a loss in information-specificity? Future studies combining modeling and experimental data might be a particularly advantageous approach for providing deeper insights into these fundamental questions. In the following sections, we touch upon additional open questions and issues related to the principles that govern the size and (co)allocation of memories to sparse populations of neurons.
How are memories organized across engrams? Memories beget memories of related events. One advantage of sparse coding is that each local node in the distributed network can be involved in multiple mnemonic representations. This may increase the efficiency and flexibility of memory storage. For instance, memories that are linked in time can be encoded by overlapping populations of neurons [50,66]. The linking of memories to overlapping populations occurs as a result of transient increases in the excitability of allocated neurons [15,27,43,44,46,66] and competitive interactions between PNs mediated by disynaptic inhibitory connections [50]. But how does each memory preserve its own identity? To probe this question, it may be important to examine engrams at the level of the synapse. Processes such as spine clustering [67,68] and synaptic tagging and capture [69] (also reviewed in Ref. [52]) provide compelling models of how memories can be linked at a sub cellular level. For instance, long-lasting synaptic plasticity induced by a strong memory can stabilize sub threshold metaplasticity induced by a subsequent weaker memory that is allocated to a different synapse on the same neuron, within a temporally defined window [68,70]. The question of how co-allocated memories maintain their unique identity is less well understood. Abdou et al. [71] showed that although related fear memories are allocated to overlapping cellular ensembles in the LA, each memory retains its distinct identity via the engagement of discrete synaptic populations. It will be interesting to determine whether these rules of engram engagement apply to other brain regions such as the hippocampus, where the concomitant emergence of time-limited versus stable representations of context have been observed in a subfield-specific manner [72]. Furthermore, it is tempting to speculate that memory organization might make use of temporally-constrained co-allocation as a neural substrate to link objects or words, as measured by semantic and phonemic fluency tests. Deciphering the underpinnings of memory (co)allocation may therefore be critical in helping us understand both age-related cognitive decline [73] as well as the memory loss observed in Alzheimer’s disease [74]. www.sciencedirect.com
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While the field has come a long way in understanding the principles that guide neuronal [27,43,44,75] and synaptic [71] allocation, the molecular and physiological kinetics of how engram cells encode and link memor(ies) have yet to be elucidated. The rapid evolution of techniques with increasingly precise temporal and spatial functionality, such as dual-eGRASP for examining synapses between ‘engram cells’ [31], in combination with cell-specific [76] and synapse-specific [77] transcriptomics/proteomics holds considerable promise in determining the molecular mechanisms that underlie the establishment and maintenance of engrams supporting different types of memory. These techniques, especially in combination with more traditional tools, will enable further progress in probing the mechanisms that underlie neuronal competition, and the consequences of sparsity in memory formation. In addition, they may help identify precisely what engram cells encode. For instance, a recent study combined inducible IEG tagging of CA1 neurons active during context exposure and in vivo electrophysiological identification of place cells [78]. Consistent with previous reports [26], a sparse population of neurons (20%) was identified as ‘engram cells’. Intriguingly, though, while most engram cells were also identified as place cells, the majority of place cells were not engram cells. That is, most engram cells did not consistently encode spatial location (they preferentially remapped their place fields), yet their activity was able to discriminate between a novel versus familiar context. On the other hand, most place cells (cells that encoded a stable spatial map between encoding and recall) were largely non-engram cells. These findings support the idea that the engram comprise a sparse subset of neurons activated by stimuli present at the time of learning but raise a number of tantalizing questions that await further investigation. Increased understanding of the fundamental neural processes meditating memory formation, storage and flexible retrieval will likely involve the use of novel approaches to answer age-old questions of how we learn and remember.
Acknowledgements We thank Albert Park for making the figure. This work was funded by a NWO VENI (016.171.033) and ZonMw TOP grant (40-00812-98-15030) to PRR, a Restracomp Fellowship from the Hospital for Sick Children to JY, NWO VIDI (017.106.384), NWO ALW (834.12.002) and ZonMw TOP grant (40-00812-98-15030) to SAK and Canadian Institute of Health Research (CIHR) and Canadian Institute for Advanced Research (CIFAR) grants to SAJ.
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39. Repa JC, Muller J, Apergis J, Desrochers TM, Zhou Y, LeDoux JE: Two different lateral amygdala cell populations contribute to the initiation and storage of memory. Nat Neurosci 2001, 4:724731. 40. Bergstrom HC, Johnson LR: An organization of visual and auditory fear conditioning in the lateral amygdala. Neurobiol Learn Mem 2014, 116:1-13. 41. Rumpel S, LeDoux J, Zador A, Malinow R: Postsynaptic receptor trafficking underlying a form of associative learning. Science 2005, 308:83-88. 42. Yang Y, D-q Liu, Huang W, Deng J, Sun Y, Zuo Y, M-m Poo: Selective synaptic remodeling of amygdalocortical connections associated with fear memory. Nat Neurosci 2016, 19:1348. Elegantin vivo imaging study of pre- and post-synaptic spines and boutons showing that auditory fear conditioning induces structural plasticity in existing synapses. Specifically, the authors observed that the majority of remodelling occurred via the addition of a new dendrite or bouton in already-formed synapses in synaptic partners between neurons in the lateral amygdala and the auditory cortex. 43. Han JH, Kushner SA, Yiu AP, Hsiang HL, Buch T, Waisman A, Bontempi B, Neve RL, Frankland PW, Josselyn SA: Selective erasure of a fear memory. Science 2009, 323:1492-1496. 44. Yiu AP, Mercaldo V, Yan C, Richards B, Rashid AJ, Hsiang HL, Pressey J, Mahadevan V, Tran MM, Kushner SA et al.: Neurons are recruited to a memory trace based on relative neuronal excitability immediately before training. Neuron 2014, 83:722735. 45. Brightwell JJ, Smith CA, Neve RL, Colombo PJ: Long-term memory for place learning is facilitated by expression of cAMP response element-binding protein in the dorsal hippocampus. Learn Mem 2007, 14:195-199. 46. Park S, Kramer EE, Mercaldo V, Rashid AJ, Insel N, Frankland PW, Josselyn SA: Neuronal allocation to a hippocampal engram. Neuropsychopharmacology 2016, 41:2987-2993. In this study, optogenetically increasing the excitability of a sparse, random subset of dentate gyrus (DG) or CA1 neurons of the dorsal hippocampus immediately before training was sufficient to bias their allocation to an engram supporting a contextual fear memory. These results mirror similar findings in the LA, suggesting that enhanced excitability at the time of learning may be a key mechanism for allocation across multiple brain regions and for different types of memory. 47. Sekeres MJ, Mercaldo V, Richards B, Sargin D, Mahadevan V, Woodin MA, Frankland PW, Josselyn SA: Increasing CRTC1 function in the dentate gyrus during memory formation or reactivation increases memory strength without compromising memory quality. J Neurosci 2012, 32:1785717868. 48. Sekeres MJ, Neve RL, Frankland PW, Josselyn SA: Dorsal hippocampal CREB is both necessary and sufficient for spatial memory. Learn Mem 2010, 17:280-283. 49. Zhou Y, Won J, Karlsson MG, Zhou M, Rogerson T, Balaji J, Neve R, Poirazi P, Silva AJ: CREB regulates excitability and the allocation of memory to subsets of neurons in the amygdala. Nat. Neurosci 2009, 12:1438-1443. 50. Rashid AJ, Yan C, Mercaldo V, Hsiang HL, Park S, Cole CJ, De Cristofaro A, Yu J, Ramakrishnan C, Lee SY et al.: Competition between engrams influences fear memory formation and recall. Science 2016, 353:383-387. This study showed that two independently acquired auditory fear memories were linked if they occurred within a short temporal window (up to 6 h) by virtue of engaging overlapping engrams in the lateral amygdala. If, however, the same two auditory fear events were separated by 24 h, then the two memories were remembered distinctly and each memory was supported by a distinct non-overlapping engram. The authors show that www.sciencedirect.com
Allocation to an engram Rao-Ruiz et al. 169
the mechanism underlying this effect involves a winner-take-all competition that is mediated by ongoing excitability in PNs and an important contribution of inhibitory interneurons. See also Cai et al (2016). 51. Berndt A, Lee SY, Wietek J, Ramakrishnan C, Steinberg EE, Rashid AJ, Kim H, Park S, Santoro A, Frankland PW et al.: Structural foundations of optogenetics: determinants of channelrhodopsin ion selectivity. Proc Natl Acad Sci U S A 2016, 113:822-829. 52. Rogerson T, Cai DJ, Frank A, Sano Y, Shobe J, Lopez-Aranda MF, Silva AJ: Synaptic tagging during memory allocation. Nat Rev Neurosci 2014, 15:157-169. 53. Sano Y, Shobe JL, Zhou M, Huang S, Shuman T, Cai DJ, Golshani P, Kamata M, Silva AJ: CREB regulates memory allocation in the insular cortex. Curr Biol: CB 2014, 24:28332837. 54. Czajkowski R, Jayaprakash B, Wiltgen B, Rogerson T, GuzmanKarlsson MC, Barth AL, Trachtenberg JT, Silva AJ: Encoding and storage of spatial information in the retrosplenial cortex. Proc Natl Acad Sci U S A 2014, 111:8661-8666. 55. Feng F, Samarth P, Pare D, Nair SS: Mechanisms underlying the formation of the amygdalar fear memory trace: A computational perspective. Neuroscience 2016, 322:370-376. Using a biophysically-inspired 1000-cell computational model of the lateral amygdala, these authors examine the contributions of intrinsic excitability and local inhibition on engram architecture. They conclude that the size of the engram is controlled by the overall level of inhibition within the lateral amygdala. 56. Kim D, Pare D, Nair SS: Assignment of model amygdala neurons to the fear memory trace depends on competitive synaptic interactions. J Neurosci 2013, 33:14354-14358. 57. Kim D, Samarth P, Feng F, Pare D, Nair SS: Synaptic competition in the lateral amygdala and the stimulus specificity of conditioned fear: a biophysical modeling study. Brain Struct Funct 2016, 221:2163-2182. Using a realistic, multi-compartment, conductance-based model of the lateral amygdala, this study showed that the number of cells recruited to a conditioned fear engram was significantly altered when plasticity was prevented in either excitatory-excitatory synapses or inhibitory-excitatory synapses. These findings highlight the importance of both intrinsic and synaptic plasticity in excitatory and inhibitory circuits as a mechanism for neuronal competition. 58. Ehrlich I, Humeau Y, Grenier F, Ciocchi S, Herry C, Luthi A: Amygdala inhibitory circuits and the control of fear memory. Neuron 2009, 62:757-771. 59. Lucas EK, Jegarl AM, Morishita H, Clem RL: Multimodal and sitespecific plasticity of amygdala parvalbumin interneurons after fear learning. Neuron 2016, 91:629-643. This paper shows that parvalbumin-containing interneurons (PV INs) are important for input gating and learning-related plasticity in the amygdala. They find that plasticity in PV INs differs in the lateral and basal amygdala, and that lateral amygdala PV INs are preferentially involved in mediating feedforward inhibition. These findings povide insight into the mechanisms by which PV INs may regulate the recruitment of PNs in fear learning and make a compelling case for clearly delineating the lateral amygdala from the basal amygdala. 60. Morrison DJ, Rashid AJ, Yiu AP, Yan C, Frankland PW, Josselyn SA: Parvalbumin interneurons constrain the size of the lateral amygdala engram. Neurobiol Learn Mem 2016, 135:91-99. First study to systematically examine the relationship between engram size and memory strength by manipulating the intensity of the unconditioned stimulus (footshock) in an auditory fear conditioning experiment. While an intense footshock resulting in stronger memory (behaviourally), it did not increase the size of the engram in the lateral amygdala. However, silencing parvalbumin-containing interneurons (PV INs) in the lateral and basal amygdala nuclei during training increased the size of the engram. These results suggest that 1) the size of an auditory fear engram in the lateral amygdala does not encode memory strength, and, 2) the sparsity of a memory trace is modulated by local inhibitory microcircuits. 61. Stefanelli T, Bertollini C, Luscher C, Muller D, Mendez P: Hippocampal somatostatin interneurons control the size of neuronal memory ensembles. Neuron 2016, 89:1074-1085. This paper examined the role of GABAergic interneurons in the dorsal hippocampal dentate gyrus (DG) in engram sparsity and memory www.sciencedirect.com
strength. While chemogenetically inhibiting parvalbumin-containing (PV +) interneurons had no effect on the size of a contextual fear engram, silencing somatostatin-containing (SOM+) interneurons increased the size of the engram and strengthened memory. In subsequent electrophysiological recordings, the authors speculate that in the DG, lateral inhibition may be responsible for modulating the size of the engram. 62. Kepecs A, Fishell G: Interneuron cell types are fit to function. Nature 2014, 505:318-326. 63. Richards BA, Frankland PW: The conjunctive trace. Hippocampus 2013, 23:207-212. 64. McKay BM, Oh MM, Disterhoft JF: Learning increases intrinsic excitability of hippocampal interneurons. J Neurosci 2013, 33:5499-5506. 65. Saffari R, Teng Z, Zhang M, Kravchenko M, Hohoff C, Ambree O, Zhang W: NPY+-, but not PV+- GABAergic neurons mediated long-range inhibition from infra- to prelimbic cortex. Transl Psychiatry 2016, 6:e736. 66. Cai DJ, Aharoni D, Shuman T, Shobe J, Biane J, Song W, Wei B, Veshkini M, La-Vu M, Lou J et al.: A shared neural ensemble links distinct contextual memories encoded close in time. Nature 2016, 534:115-118. This study used elegant real-timein vivo calcium imaging in the dorsal hippocampus of freely-behaving mice to show that neuronal representations of two distinct contexts overlap if mice are exposed to both contexts within 5 h, causing the memories to become linked by virtue of engaging overlapping engrams. Older mice show a deficit in this memory linking. Together with Rashid et al. (2016), these studies established the phenomena whereby memory linking occurs due to overlapping engrams of events that were experienced in close temporal proximity. 67. Govindarajan A, Kelleher RJ, Tonegawa S: A clustered plasticity model of long-term memory engrams. Nat Rev Neurosci 2006, 7:575-583. 68. Kastellakis G, Silva AJ, Poirazi P: Linking memories across time via neuronal and dendritic overlaps in model neurons with active dendrites. Cell Rep 2016, 17:1491-1504. Here, the authors built a neural network model that integrates experimental evidence for intrinsic excitability, homeostatic plasticity, and synaptic tagging and capture in associative memory formation. The model generated several hypotheses for how engram sparsity may be determined by the locus of the distribution of plasticity-related proteins across cellular subcompartments. In particular, the model provided support for dendritic clustering as a mechanism for co-allocation of a weak and strong memory. 69. Frey U, Morris RG: Synaptic tagging: implications for late maintenance of hippocampal long-term potentiation. Trends Neurosci 1998, 21:181-188. 70. Ramiro-Cortes Y, Hobbiss AF, Israely I: Synaptic competition in structural plasticity and cognitive function. Philos Trans R Soc Lond B Biol Sci 2014, 369 20130157. 71. Abdou K, Shehata M, Choko K, Nishizono H, Matsuo M, Muramatsu SI, Inokuchi K: Synapse-specific representation of the identity of overlapping memory engrams. Science 2018, 360:1227-1231. This study takes advantage of cutting-edge optogenetic and virallymediated circuit mapping techniques to investigate how the identity of specific memories is maintained if memories for multiple learning events are encoded by overlapping ensembles. The authors conclude that memory identity is delineated not only in terms of the population of cells that are activated during training and recall, but by plasticity occurring at specific synapses that underlie each memory trace. 72. Hainmueller T, Bartos M: Parallel emergence of stable and dynamic memory engrams in the hippocampus. Nature 2018. 73. Brickman AM, Paul RH, Cohen RA, Williams LM, MacGregor KL, Jefferson AL, Tate DF, Gunstad J, Gordon E: Category and letter verbal fluency across the adult lifespan: relationship to EEG theta power. Arch Clin Neuropsychol 2005, 20:561-573. 74. Chan AS, Butters N, Paulsen JS, Salmon DP, Swenson MR, Maloney LT: An assessment of the semantic network in patients with Alzheimer’s disease. J Cogn Neurosci 1993, 5:254-261.
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75. Sehgal M, Ehlers VL, Moyer JR Jr: Learning enhances intrinsic excitability in a subset of lateral amygdala neurons. Learn Mem 2014, 21:161-170. 76. Zeisel A, Munoz-Manchado AB, Codeluppi S, Lonnerberg P, La Manno G, Jureus A, Marques S, Munguba H, He L, Betsholtz C et al.: Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 2015, 347:1138-1142. 77. Biesemann C, Gronborg M, Luquet E, Wichert SP, Bernard V, Bungers SR, Cooper B, Varoqueaux F, Li L, Byrne JA et al.: Proteomic screening of glutamatergic mouse brain synaptosomes isolated by fluorescence activated sorting. EMBO J 2014, 33:157-170.
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78. Tanaka KZ, He H, Tomar A, Niisato K, Huang AJY, McHugh TJ: The hippocampal engram maps experience but not place. Science 2018, 361:392. This study combined "engram tagging" strategies (using an inducible cFos promoter) within vivo electrophysiological identification of place cells during context exploration. They showed that a sparse population of neurons were identified as ‘engram cells’ based on c-Fos expression during training (20%). While 80% of engram cells were also ‘place cells’ (i.e. encoded a specific spatial location), only 25% of place cells were engram cells. That is, most engram cells did not consistently encode spatial location, yet their activity was able to discriminate between a novel vs. familiar context.
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