Intrinsic Neural Excitability Biases Allocation and Overlap of Memory Engrams

Memories are thought to be stored in neural ensembles known as engrams that are specifically reactivated during memory recall. Recent studies have found that memory engrams of two events that happened close in time tend to overlap in the hippocampus and the amygdala, and these overlaps have been shown to support memory linking. It has been hypothesized that engram overlaps arise from the mechanisms that regulate memory allocation itself, involving neural excitability, but the exact process remains unclear. Indeed, most theoretical studies focus on synaptic plasticity and little is known about the role of intrinsic plasticity, which could be mediated by neural excitability and serve as a complementary mechanism for forming memory engrams. Here, we developed a rate-based recurrent neural network that includes both synaptic plasticity and neural excitability. We obtained structural and functional overlap of memory engrams for contexts that are presented close in time, consistent with experimental and computational studies. We then investigated the role of excitability in memory allocation at the network level and unveiled competitive mechanisms driven by inhibition. This work suggests mechanisms underlying the role of intrinsic excitability in memory allocation and linking, and yields predictions regarding the formation and the overlap of memory engrams.

Hippocampus-to-amygdala pathway drives the separation of remote memories of related events

The mammalian brain can store and retrieve memories of related events as distinct memories and remember common features of those experiences. How it computes this function remains elusive. Here, we show in rats that recent memories of two closely timed auditory fear events share overlapping neuronal ensembles in the basolateral amygdala (BLA) and are functionally linked. However, remote memories have reduced neuronal overlap and are functionally independent. The activity of parvalbumin (PV)-expressing neurons in the BLA plays a crucial role in forming separate remote memories. Chemogenetic blockade of PV preserves individual remote memories but prevents their segregation, resulting in reciprocal associations. The hippocampus drives this process through specific excitatory connections with BLA GABAergic interneurons. These findings provide insights into the neuronal mechanisms that minimize the overlap between distinct remote memories and enable the retrieval of related memories separately.

Local memory allocation recruits memory ensembles across brain regions

Memories are thought to be stored in ensembles of neurons across multiple brain regions. However, whether and how these ensembles are coordinated at the time of learning remains largely unknown. Here, we combined CREB-mediated memory allocation with transsynaptic retrograde tracing to demonstrate that the allocation of aversive memories to a group of neurons in one brain region directly affects the allocation of interconnected neurons in upstream brain regions in a behavioral- and brain region-specific manner in mice. Our analysis suggests that this cross-regional recruitment of presynaptic neurons is initiated by downstream memory neurons through a retrograde mechanism. Together with statistical modeling, our results indicate that in addition to the anterograde flow of information between brain regions, the establishment of interconnected, brain-wide memory traces relies on a retrograde mechanism that coordinates memory ensembles at the time of learning.

Dimensions and mechanisms of memory organization

Memory formation is dynamic in nature, and acquisition of new information is often influenced by previous experiences. Memories sharing certain attributes are known to interact so that retrieval of one increases the likelihood of retrieving the other, raising the possibility that related memories are organized into associative mnemonic structures of interconnected representations. Although the formation and retrieval of single memories have been studied extensively, very little is known about the brain mechanisms that organize and link related memories. Here we review studies that suggest the existence of mnemonic structures in humans and animal models. These studies suggest three main dimensions of experience that can serve to organize related memories: time, space, and perceptual/conceptual similarities. We propose potential molecular, cellular, and systems mechanisms that might support organization of memories according to these dimensions.

The first two decades of CREB-memory research: data for philosophy of neuroscience

I recount some landmark discoveries that initially confirmed the cyclic AMP response element-binding (CREB) protein-memory consolidation and allocation linkages. This work constitutes one of the successes of the field of Molecular and Cellular Cognition (MCC) but is also of interest to philosophers of neuroscience. Two approaches, "mechanism" and "ruthless reductionism", claim to account for this case, yet these accounts differ in one crucial way. I explain this difference and argue that both the experiment designs and discussions of these discoveries by MCC scientists better fit the ruthless reductionist's account. This conclusion leads to further philosophical discussion about how discoveries in cellular/molecular neurobiology integrate with systems neuroscience findings.

The brain in motion: How ensemble fluidity drives memory-updating and flexibility

While memories are often thought of as flashbacks to a previous experience, they do not simply conserve veridical representations of the past but must continually integrate new information to ensure survival in dynamic environments. Therefore, 'drift' in neural firing patterns, typically construed as disruptive 'instability' or an undesirable consequence of noise, may actually be useful for updating memories. In our view, continual modifications in memory representations reconcile classical theories of stable memory traces with neural drift. Here we review how memory representations are updated through dynamic recruitment of neuronal ensembles on the basis of excitability and functional connectivity at the time of learning. Overall, we emphasize the importance of considering memories not as static entities, but instead as flexible network states that reactivate and evolve across time and experience.

The Interplay of Synaptic Plasticity and Scaling Enables Self-Organized Formation and Allocation of Multiple Memory Representations

It is commonly assumed that memories about experienced stimuli are represented by groups of highly interconnected neurons called cell assemblies. This requires allocating and storing information in the neural circuitry, which happens through synaptic weight adaptations at different types of synapses. In general, memory allocation is associated with synaptic changes at feed-forward synapses while memory storage is linked with adaptation of recurrent connections. It remains, however, largely unknown how memory allocation and storage can be achieved and the adaption of the different synapses involved be coordinated to allow for a faithful representation of multiple memories without disruptive interference between them. In this theoretical study, by using network simulations and phase space analyses, we show that the interplay between long-term synaptic plasticity and homeostatic synaptic scaling organizes simultaneously the adaptations of feed-forward and recurrent synapses such that a new stimulus forms a new memory and where different stimuli are assigned to distinct cell assemblies. The resulting dynamics can reproduce experimental in-vivo data, focusing on how diverse factors, such as neuronal excitability and network connectivity, influence memory formation. Thus, the here presented model suggests that a few fundamental synaptic mechanisms may suffice to implement memory allocation and storage in neural circuitry.

Localization of Contextual and Context Removed Auditory Fear Memory within the Basolateral Amygdala Complex

Debilitating and persistent fear memories can rapidly form in humans following exposure to traumatic events. Fear memories can also be generated and studied in animals via Pavlovian fear conditioning. The current study was designed to evaluate basolateral amygdala complex (BLC) involvement following the formation of different fear memories (two contextual fear memories and one adjusted auditory fear memory). Fear memories were created in the same context with five 1.0 mA (0.50 s) foot-shocks and, where necessary, five auditory tones (5 kHz, 75 dB, 20 s). The adjusted auditory fear conditioning protocol was employed to remove background contextual fear and produce isolated auditory fear memories. Immunofluorescent labeling was utilized to identify neurons expressing immediate early genes (IEGs). We found the two contextual fear conditioning (CFC) procedures to produce similar levels of fear-related freezing to context. Contextual fear memories produced increases in BLC IEG expression with distinct and separate patterns of expression. These data suggest contextual fear memories created in slightly altered contexts, can produce unique patterns of amygdala activation. The adjusted auditory fear conditioning procedure produced memories to a tone, but not to a context. This group, where no contextual fear was present, had a significant reduction in BLC IEG expression. These data suggest background contextual fear memories, created in standard auditory fear conditioning protocols, contribute significantly to increases in amygdala activation.

How the Hippocampus Represents Memories: Making Sense of Memory Allocation Studies

In recent years there has been a wealth of studies investigating how memories are allocated in the hippocampus. Some of those studies showed that it is possible to manipulate the identity of neurons recruited to represent a given memory without affecting the memory's behavioral expression. Those findings raised questions about how the hippocampus represents memories, with some researchers arguing that hippocampal neurons do not represent fixed stimuli. Herein, an alternative hypothesis is argued. Neurons in high-order brain regions can be tuned to multiple dimensions, forming complex, abstract representations. It is argued that such complex receptive fields allow those neurons to show some flexibility in their responses while still representing relatively fixed sets of stimuli. Moreover, it is pointed out that changes induced by artificial manipulation of cell assemblies are not completely redundant-the observed behavioral redundancy does not imply cognitive redundancy, as different, but similar, memories may induce the same behavior.

Astrocytic Activation Generates De Novo Neuronal Potentiation and Memory Enhancement

Astrocytes respond to neuronal activity and were shown to be necessary for plasticity and memory. To test whether astrocytic activity is also sufficient to generate synaptic potentiation and enhance memory, we expressed the Gq-coupled receptor hM3Dq in CA1 astrocytes, allowing their activation by a designer drug. We discovered that astrocytic activation is not only necessary for synaptic plasticity, but also sufficient to induce NMDA-dependent de novo long-term potentiation in the hippocampus that persisted after astrocytic activation ceased. In vivo, astrocytic activation enhanced memory allocation; i.e., it increased neuronal activity in a task-specific way only when coupled with learning, but not in home-caged mice. Furthermore, astrocytic activation using either a chemogenetic or an optogenetic tool during acquisition resulted in memory recall enhancement on the following day. Conversely, directly increasing neuronal activity resulted in dramatic memory impairment. Our findings that astrocytes induce plasticity and enhance memory may have important clinical implications for cognitive augmentation treatments.

Linking Memories across Time via Neuronal and Dendritic Overlaps in Model Neurons with Active Dendrites

Memories are believed to be stored in distributed neuronal assemblies through activity-induced changes in synaptic and intrinsic properties. However, the specific mechanisms by which different memories become associated or linked remain a mystery. Here, we develop a simplified, biophysically inspired network model that incorporates multiple plasticity processes and explains linking of information at three different levels: (1) learning of a single associative memory, (2) rescuing of a weak memory when paired with a strong one, and (3) linking of multiple memories across time. By dissecting synaptic from intrinsic plasticity and neuron-wide from dendritically restricted protein capture, the model reveals a simple, unifying principle: linked memories share synaptic clusters within the dendrites of overlapping populations of neurons. The model generates numerous experimentally testable predictions regarding the cellular and sub-cellular properties of memory engrams as well as their spatiotemporal interactions.

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Network model with active dendrites and synaptic, somatic, homeostatic plasticity

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Linked memories are stored in overlapping populations of neurons

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Linked memories share synaptic clusters in common dendritic branches

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The locus of protein synthesis or capture shapes the structure of the memory trace

Constructing Neuronal Network Models in Massively Parallel Environments

Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation of neuronal networks. Using an established benchmark, we divide the runtime of simulation code into the phase of network construction and the phase during which the dynamical state is advanced in time. We find that on multi-core compute nodes network creation scales well with process-parallel code but exhibits a prohibitively large memory consumption. Thread-parallel network creation, in contrast, exhibits speedup only up to a small number of threads but has little overhead in terms of memory. We further observe that the algorithms creating instances of model neurons and their connections scale well for networks of ten thousand neurons, but do not show the same speedup for networks of millions of neurons. Our work uncovers that the lack of scaling of thread-parallel network creation is due to inadequate memory allocation strategies and demonstrates that thread-optimized memory allocators recover excellent scaling. An analysis of the loop order used for network construction reveals that more complex tests on the locality of operations significantly improve scaling and reduce runtime by allowing construction algorithms to step through large networks more efficiently than in existing code. The combination of these techniques increases performance by an order of magnitude and harnesses the increasingly parallel compute power of the compute nodes in high-performance clusters and supercomputers.

How Does the Sparse Memory "Engram" Neurons Encode the Memory of a Spatial-Temporal Event?

Episodic memory in human brain is not a fixed 2-D picture but a highly dynamic movie serial, integrating information at both the temporal and the spatial domains. Recent studies in neuroscience reveal that memory storage and recall are closely related to the activities in discrete memory engram (trace) neurons within the dentate gyrus region of hippocampus and the layer 2/3 of neocortex. More strikingly, optogenetic reactivation of those memory trace neurons is able to trigger the recall of naturally encoded memory. It is still unknown how the discrete memory traces encode and reactivate the memory. Considering a particular memory normally represents a natural event, which consists of information at both the temporal and spatial domains, it is unknown how the discrete trace neurons could reconstitute such enriched information in the brain. Furthermore, as the optogenetic-stimuli induced recall of memory did not depend on firing pattern of the memory traces, it is most likely that the spatial activation pattern, but not the temporal activation pattern of the discrete memory trace neurons encodes the memory in the brain. How does the neural circuit convert the activities in the spatial domain into the temporal domain to reconstitute memory of a natural event? By reviewing the literature, here we present how the memory engram (trace) neurons are selected and consolidated in the brain. Then, we will discuss the main challenges in the memory trace theory. In the end, we will provide a plausible model of memory trace cell network, underlying the conversion of neural activities between the spatial domain and the temporal domain. We will also discuss on how the activation of sparse memory trace neurons might trigger the replay of neural activities in specific temporal patterns.