Hotspots of dendritic spine turnover facilitate clustered spine addition and learning and memory.

Modeling studies suggest that clustered structural plasticity of dendritic spines is an efficient mechanism of information storage in cortical circuits. However, why new clustered spines occur in specific locations and how their formation relates to learning and memory (L&M) remain unclear. Using in vivo two-photon microscopy, we track spine dynamics in retrosplenial cortex before, during, and after two forms of episodic-like learning and find that spine turnover before learning predicts future L&M performance, as well as the localization and rates of spine clustering. Consistent with the idea that these measures are causally related, a genetic manipulation that enhances spine turnover also enhances both L&M and spine clustering. Biophysically inspired modeling suggests turnover increases clustering, network sparsity, and memory capacity. These results support a hotspot model where spine turnover is the driver for localization of clustered spine formation, which serves to modulate network function, thus influencing storage capacity and L&M

The dendritic engram

Accumulating evidence from a wide range of studies, including behavioral, cellular, molecular and computational findings, support a key role of dendrites in the encoding and recall of new memories. Dendrites can integrate synaptic inputs in non-linear ways, provide the substrate for local protein synthesis and facilitate the orchestration of signaling pathways that regulate local synaptic plasticity. These capabilities allow them to act as a second layer of computation within the neuron and serve as the fundamental unit of plasticity. As such, dendrites are integral parts of the memory engram, namely the physical representation of memories in the brain and are increasingly studied during learning tasks. Here, we review experimental and computational studies that support a novel, dendritic view of the memory engram that is centered on non-linear dendritic branches as elementary memory units. We highlight the potential implications of dendritic engrams for the learning and memory field and discuss future research directions

Computational modeling of memory allocation in neuronal and dendritic populations

Recent studies using molecular and cellular approaches have established that memory is supported by distributed and sparse populations of neurons. The allocation of neurons and synapses to store a long term memory engram is not random, but depends on properties such as neuronal excitability and CREB activation. The consolidation of synaptic plasticity, which is believed to serve long-term memory storage, is dependent on protein availability, and shaped by the mechanism of synaptic tagging and capture. In addition, dendritic protein synthesis allows for compartmentalized plasticity and synapse clustering. The implications of the rules governing long-term memory allocation in neurons and their dendrites are not yet known. To this aim, we present a model that incorporates multiple plasticity-related mechanisms which are known to be active during memory allocation and consolidation. Using this model, we show that memory allocation in neurons and their dendrites is affected by dendritic protein synthesis, and that the late-LTP associativity mechanisms allow related memories to be stored in overlapping populations of neurons

Linking memories across time via excitability and synaptic tagging

<p>Slides for the presentation</p

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

Synaptic clustering within dendrites: An emerging theory of memory formation

It is generally accepted that complex memories are stored in distributed representations throughout the brain, however the mechanisms underlying these representations are not understood. Here, we review recent findings regarding the subcellular mechanisms implicated in memory formation, which provide evidence for a dendrite-centered theory of memory. Plasticity-related phenomena which affect synaptic properties, such as synaptic tagging and capture, synaptic clustering, branch strength potentiation and spinogenesis provide the foundation for a model of memory storage that relies heavily on processes operating at the dendrite level. The emerging picture suggests that clusters of functionally related synapses may serve as key computational and memory storage units in the brain. We discuss both experimental evidence and theoretical models that support this hypothesis and explore its advantages for neuronal function

A locus coeruleus-dorsal CA1 dopaminergic circuit modulates memory linking.

Individual memories are often linked so that the recall of one triggers the recall of another. For example, contextual memories acquired close in time can be linked, and this is known to depend on a temporary increase in excitability that drives the overlap between dorsal CA1 (dCA1) hippocampal ensembles that encode the linked memories. Here, we show that locus coeruleus (LC) cells projecting to dCA1 have a key permissive role in contextual memory linking, without affecting contextual memory formation, and that this effect is mediated by dopamine. Additionally, we found that LC-to-dCA1-projecting neurons modulate the excitability of dCA1 neurons and the extent of overlap between dCA1 memory ensembles as well as the stability of coactivity patterns within these ensembles. This discovery of a neuromodulatory system that specifically affects memory linking without affecting memory formation reveals a fundamental separation between the brain mechanisms modulating these two distinct processes

Hotspots of dendritic spine turnover facilitate clustered spine addition and learning and memory.

Modeling studies suggest that clustered structural plasticity of dendritic spines is an efficient mechanism of information storage in cortical circuits. However, why new clustered spines occur in specific locations and how their formation relates to learning and memory (L&amp;M) remain unclear. Using in vivo two-photon microscopy, we track spine dynamics in retrosplenial cortex before, during, and after two forms of episodic-like learning and find that spine turnover before learning predicts future L&amp;M performance, as well as the localization and rates of spine clustering. Consistent with the idea that these measures are causally related, a genetic manipulation that enhances spine turnover also enhances both L&amp;M and spine clustering. Biophysically inspired modeling suggests turnover increases clustering, network sparsity, and memory capacity. These results support a hotspot model where spine turnover is the driver for localization of clustered spine formation, which serves to modulate network function, thus influencing storage capacity and L&amp;M