Selective memory generalization by spatial patterning of protein synthesis

Non-Hebbian plasticity transforms transient experiences into lasting memories

The dominant models of learning and memory, such as Hebbian plasticity, propose that experiences are transformed into memories through input-specific synaptic plasticity at the time of learning. However, synaptic plasticity is neither strictly input-specific nor restricted to the time of its induction. The impact of such forms of non-Hebbian plasticity on memory has been difficult to test, and hence poorly understood. Here, we demonstrate that synaptic manipulations can deviate from the Hebbian model of learning, yet produce a lasting memory. First, we established a weak associative conditioning protocol in mice, where optogenetic stimulation of sensory thalamic input to the amygdala was paired with a footshock, but no detectable memory was formed. However, when the same input was potentiated minutes before or after, or even 24 hr later, the associative experience was converted into a lasting memory. Importantly, potentiating an independent input to the amygdala minutes but not 24 hr after the pairing produced a lasting memory. Thus, our findings suggest that the process of transformation of a transient experience into a memory is neither restricted to the time of the experience nor to the synapses triggered by it; instead, it can be influenced by past and future events.

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.

L-DOPA increases slow-wave sleep duration and selectively modulates memory persistence in older adults

Introduction

Millions of people worldwide take medications such as L-DOPA that increase dopamine to treat Parkinson's disease. Yet, we do not fully understand how L-DOPA affects sleep and memory. Our earlier research in Parkinson's disease revealed that the timing of L-DOPA relative to sleep affects dopamine's impact on long-term memory. Dopamine projections between the midbrain and hippocampus potentially support memory processes during slow wave sleep. In this study, we aimed to test the hypothesis that L-DOPA enhances memory consolidation by modulating NREM sleep.

Methods

We conducted a double-blind, randomised, placebo-controlled crossover trial with healthy older adults (65-79 years, n = 35). Participants first learned a word list and were then administered long-acting L-DOPA (or placebo) before a full night of sleep. Before sleeping, a proportion of the words were re-exposed using a recognition test to strengthen memory. L-DOPA was active during sleep and the practice-recognition test, but not during initial learning.

Results

The single dose of L-DOPA increased total slow-wave sleep duration by approximately 11% compared to placebo, while also increasing spindle amplitudes around slow oscillation peaks and around 1-4 Hz NREM spectral power. However, behaviourally, L-DOPA worsened memory of words presented only once compared to re-exposed words. The coupling of spindles to slow oscillation peaks correlated with these differential effects on weaker and stronger memories. To gauge whether L-DOPA affects encoding or retrieval of information in addition to consolidation, we conducted a second experiment targeting L-DOPA only to initial encoding or retrieval and found no behavioural effects.

Discussion

Our results demonstrate that L-DOPA augments slow wave sleep in elderly, perhaps tuning coordinated network activity and impacting the selection of information for long-term storage. The pharmaceutical modification of slow-wave sleep and long-term memory may have clinical implications.

Clinical trial registration

Eudract number: 2015-002027-26; https://doi.org/10.1186/ISRCTN90897064, ISRCTN90897064.

Computational insights into mRNA and protein dynamics underlying synaptic plasticity rules

Recent advances in experimental techniques provide an unprecedented peek into the intricate molecular dynamics inside synapses and dendrites. The experimental insights into the molecular turnover revealed that such processes as diffusion, active transport, spine uptake, and local protein synthesis could dynamically modulate the copy numbers of plasticity-related molecules in synapses. Subsequently, theoretical models were designed to understand the interaction of these processes better and to explain how local synaptic plasticity cues can up or down-regulate the molecular copy numbers across synapses. In this review, we discuss the recent advances in experimental techniques and computational models to highlight how these complementary approaches can provide insight into molecular cross-talk across synapses, ultimately allowing us to develop biologically-inspired neural network models to understand brain function.

Increased rates of brain protein synthesis during [N1,N2] sleep: L-[1-11C]leucine PET studies in human subjects

During sleep, reduced brain energy demands provide an opportunity for biosynthetic processes like protein synthesis. Sleep is required for some forms of memory consolidation which requires de novo protein synthesis. We measured regional cerebral protein synthesis rates (rCPS) in human subjects to ascertain how rCPS is affected during sleep. Subjects underwent three consecutive L-[1-11C]leucine PET scans with simultaneous polysomnography: 1. rested awake, 2. sleep-deprived awake, 3. sleep. Measured rCPS were similar across the three conditions. Variations in sleep stage times during sleep scans were used to estimate rCPS in sleep stages under the assumption that measured rCPS is the weighted sum of rCPS in each stage, with weights reflecting time and availability of [11C]leucine in that stage. During sleep scans, subjects spent most of the time in N2, N3, and awake and very little time in N1 and REM; rCPS in N1 and REM could not be reliably estimated. When stages N1 and N2 were combined [N1,N2], estimates of rCPS were more robust. In selective regions, estimated rCPS were statistically significantly higher (30-39%) in [N1,N2] compared with N3; estimated rCPS in N3 were similar to values measured in sleep-deprived awake scans. Results indicate increased rates of protein synthesis linked to [N1,N2] sleep.

Adult-born neurons add flexibility to hippocampal memories

Although most neurons are generated embryonically, neurogenesis is maintained at low rates in specific brain areas throughout adulthood, including the dentate gyrus of the mammalian hippocampus. Episodic-like memories encoded in the hippocampus require the dentate gyrus to decorrelate similar experiences by generating distinct neuronal representations from overlapping inputs (pattern separation). Adult-born neurons integrating into the dentate gyrus circuit compete with resident mature cells for neuronal inputs and outputs, and recruit inhibitory circuits to limit hippocampal activity. They display transient hyperexcitability and hyperplasticity during maturation, making them more likely to be recruited by any given experience. Behavioral evidence suggests that adult-born neurons support pattern separation in the rodent dentate gyrus during encoding, and they have been proposed to provide a temporal stamp to memories encoded in close succession. The constant addition of neurons gradually degrades old connections, promoting generalization and ultimately forgetting of remote memories in the hippocampus. This makes space for new memories, preventing saturation and interference. Overall, a small population of adult-born neurons appears to make a unique contribution to hippocampal information encoding and removal. Although several inconsistencies regarding the functional relevance of neurogenesis remain, in this review we argue that immature neurons confer a unique form of transience on the dentate gyrus that complements synaptic plasticity to help animals flexibly adapt to changing environments.

Adult neurogenesis acts as a neural regularizer

New neurons are continuously generated in the subgranular zone of the dentate gyrus throughout adulthood. These new neurons gradually integrate into hippocampal circuits, forming new naive synapses. Viewed from this perspective, these new neurons may represent a significant source of "wiring" noise in hippocampal networks. In machine learning, such noise injection is commonly used as a regularization technique. Regularization techniques help prevent overfitting training data and allow models to generalize learning to new, unseen data. Using a computational modeling approach, here we ask whether a neurogenesis-like process similarly acts as a regularizer, facilitating generalization in a category learning task. In a convolutional neural network (CNN) trained on the CIFAR-10 object recognition dataset, we modeled neurogenesis as a replacement/turnover mechanism, where weights for a randomly chosen small subset of hidden layer neurons were reinitialized to new values as the model learned to categorize 10 different classes of objects. We found that neurogenesis enhanced generalization on unseen test data compared to networks with no neurogenesis. Moreover, neurogenic networks either outperformed or performed similarly to networks with conventional noise injection (i.e., dropout, weight decay, and neural noise). These results suggest that neurogenesis can enhance generalization in hippocampal learning through noise injection, expanding on the roles that neurogenesis may have in cognition.

A synaptic learning rule for exploiting nonlinear dendritic computation

Information processing in the brain depends on the integration of synaptic input distributed throughout neuronal dendrites. Dendritic integration is a hierarchical process, proposed to be equivalent to integration by a multilayer network, potentially endowing single neurons with substantial computational power. However, whether neurons can learn to harness dendritic properties to realize this potential is unknown. Here, we develop a learning rule from dendritic cable theory and use it to investigate the processing capacity of a detailed pyramidal neuron model. We show that computations using spatial or temporal features of synaptic input patterns can be learned, and even synergistically combined, to solve a canonical nonlinear feature-binding problem. The voltage dependence of the learning rule drives coactive synapses to engage dendritic nonlinearities, whereas spike-timing dependence shapes the time course of subthreshold potentials. Dendritic input-output relationships can therefore be flexibly tuned through synaptic plasticity, allowing optimal implementation of nonlinear functions by single neurons.

Dendritic Computing: Branching Deeper into Machine Learning

In this paper, we discuss the nonlinear computational power provided by dendrites in biological and artificial neurons. We start by briefly presenting biological evidence about the type of dendritic nonlinearities, respective plasticity rules and their effect on biological learning as assessed by computational models. Four major computational implications are identified as improved expressivity, more efficient use of resources, utilizing internal learning signals, and enabling continual learning. We then discuss examples of how dendritic computations have been used to solve real-world classification problems with performance reported on well known data sets used in machine learning. The works are categorized according to the three primary methods of plasticity used-structural plasticity, weight plasticity, or plasticity of synaptic delays. Finally, we show the recent trend of confluence between concepts of deep learning and dendritic computations and highlight some future research directions.

Neurogenesis-dependent transformation of hippocampal engrams

In humans and other mammals, memories of events are encoded by neuronal ensembles (or engrams) in the hippocampus. The mnemonic information stored in these engrams can then be used to guide future behavior, including prediction- and decision-making in dynamic environments. While some hippocampal engrams may be persistently stored, others are modified over time, suggesting that the represented memories may also be transformed. How might hippocampal engrams be modified through time? Adult hippocampal neurogenesis represents one process that continuously rewires hippocampal circuitry, presumably including stored hippocampal engrams. At intermediate stages, we propose that neurogenesis-mediated rewiring of hippocampal engram circuitry induces forgetting of specific stimulus attributes, and this less precise engram allows for generalization. At more advanced stages, we propose that neurogenesis-mediated rewiring of hippocampal engram circuitry leads to silencing of hippocampal engrams, rendering them no longer accessible by natural retrieval cues.

Memory consolidation and improvement by synaptic tagging and capture in recurrent neural networks

The synaptic-tagging-and-capture (STC) hypothesis formulates that at each synapse the concurrence of a tag with protein synthesis yields the maintenance of changes induced by synaptic plasticity. This hypothesis provides a biological principle underlying the synaptic consolidation of memories that is not verified for recurrent neural circuits. We developed a theoretical model integrating the mechanisms underlying the STC hypothesis with calcium-based synaptic plasticity in a recurrent spiking neural network. In the model, calcium-based synaptic plasticity yields the formation of strongly interconnected cell assemblies encoding memories, followed by consolidation through the STC mechanisms. Furthermore, we show for the first time that STC mechanisms modify the storage of memories such that after several hours memory recall is significantly improved. We identify two contributing processes: a merely time-dependent passive improvement, and an active improvement during recall. The described characteristics can provide a new principle for storing information in biological and artificial neural circuits.

Illuminating dendritic function with computational models

Dendrites have always fascinated researchers: from the artistic drawings by Ramon y Cajal to the beautiful recordings of today, neuroscientists have been striving to unravel the mysteries of these structures. Theoretical work in the 1960s predicted important dendritic effects on neuronal processing, establishing computational modelling as a powerful technique for their investigation. Since then, modelling of dendrites has been instrumental in driving neuroscience research in a targeted manner, providing experimentally testable predictions that range from the subcellular level to the systems level, and their relevance extends to fields beyond neuroscience, such as machine learning and artificial intelligence. Validation of modelling predictions often requires - and drives - new technological advances, thus closing the loop with theory-driven experimentation that moves the field forward. This Review features the most important, to our understanding, contributions of modelling of dendritic computations, including those pending experimental verification, and highlights studies of successful interactions between the modelling and experimental neuroscience communities.

Impact of functional synapse clusters on neuronal response selectivity

Clustering of functionally similar synapses in dendrites is thought to affect neuronal input-output transformation by triggering local nonlinearities. However, neither the in vivo impact of synaptic clusters on somatic membrane potential (sVm), nor the rules of cluster formation are elucidated. We develop a computational approach to measure the effect of functional synaptic clusters on sVm response of biophysical model CA1 and L2/3 pyramidal neurons to in vivo-like inputs. We demonstrate that small synaptic clusters appearing with random connectivity do not influence sVm. With structured connectivity,  ~10-20 synapses/cluster are optimal for clustering-based tuning via state-dependent mechanisms, but larger selectivity is achieved by 2-fold potentiation of the same synapses. We further show that without nonlinear amplification of the effect of random clusters, action potential-based, global plasticity rules cannot generate functional clustering. Our results suggest that clusters likely form via local synaptic interactions, and have to be moderately large to impact sVm responses.

Local resources of polyribosomes and SER promote synapse enlargement and spine clustering after long-term potentiation in adult rat hippocampus

Synapse clustering facilitates circuit integration, learning, and memory. Long-term potentiation (LTP) of mature neurons produces synapse enlargement balanced by fewer spines, raising the question of how clusters form despite this homeostatic regulation of total synaptic weight. Three-dimensional reconstruction from serial section electron microscopy (3DEM) revealed the shapes and distributions of smooth endoplasmic reticulum (SER) and polyribosomes, subcellular resources important for synapse enlargement and spine outgrowth. Compared to control stimulation, synapses were enlarged two hours after LTP on resource-rich spines containing polyribosomes (4% larger than control) or SER (15% larger). SER in spines shifted from a single tubule to complex spine apparatus after LTP. Negligible synapse enlargement (0.6%) occurred on resource-poor spines lacking SER and polyribosomes. Dendrites were divided into discrete synaptic clusters surrounded by asynaptic segments. Spine density was lowest in clusters having only resource-poor spines, especially following LTP. In contrast, resource-rich spines preserved neighboring resource-poor spines and formed larger clusters with elevated total synaptic weight following LTP. These clusters also had more shaft SER branches, which could sequester cargo locally to support synapse growth and spinogenesis. Thus, resources appear to be redistributed to synaptic clusters with LTP-related synapse enlargement while homeostatic regulation suppressed spine outgrowth in resource-poor synaptic clusters.

The Function(s) of Sleep

Sleep is a highly conserved phenomenon in endotherms, and therefore it must serve at least one basic function across this wide range of species. What that function is remains one of the biggest mysteries in neurobiology. By using the word neurobiology, we do not mean to exclude possible non-neural functions of sleep, but it is difficult to imagine why the brain must be taken offline if the basic function of sleep did not involve the nervous system. In this chapter we discuss several current hypotheses about sleep function. We divide these hypotheses into two categories: ones that propose higher-order cognitive functions and ones that focus on housekeeping or restorative processes. We also pose four aspects of sleep that any successful functional hypothesis has to account for: why do the properties of sleep change across the life span? Why and how is sleep homeostatically regulated? Why must the brain be taken offline to accomplish the proposed function? And, why are there two radically different stages of sleep?The higher-order cognitive function hypotheses we discuss are essential mechanisms of learning and memory and synaptic plasticity. These are not mutually exclusive hypotheses. Each focuses on specific mechanistic aspects of sleep, and higher-order cognitive processes are likely to involve components of all of these mechanisms. The restorative hypotheses are maintenance of brain energy metabolism, macromolecular biosynthesis, and removal of metabolic waste. Although these three hypotheses seem more different than those related to higher cognitive function, they may each contribute important components to a basic sleep function. Any sleep function will involve specific gene expression and macromolecular biosynthesis, and as we explain there may be important connections between brain energy metabolism and the need to remove metabolic wastes.A deeper understanding of sleep functions in endotherms will enable us to answer whether or not rest behaviors in species other than endotherms are homologous with mammalian and avian sleep. Currently comparisons across the animal kingdom depend on superficial and phenomenological features of rest states and sleep, but investigations of sleep functions would provide more insight into the evolutionary relationships between EEG-defined sleep in endotherms and rest states in ectotherms.

Untangling a Cholinergic Pathway from Wakefulness to Memory

Acetylcholine is a major modulator of learning and memory, and its availability varies across the sleep-wake cycle. In this issue of Neuron, Papouin et al. (2017) describe a D-serine-dependent pathway involving astroglia by which the transmitter tunes the hippocampus toward memory encoding during wakefulness.

Modelling plasticity in dendrites: from single cells to networks

One of the key questions in neuroscience is how our brain self-organises to efficiently process information. To answer this question, we need to understand the underlying mechanisms of plasticity and their role in shaping synaptic connectivity. Theoretical neuroscience typically investigates plasticity on the level of neural networks. Neural network models often consist of point neurons, completely neglecting neuronal morphology for reasons of simplicity. However, during the past decades it became increasingly clear that inputs are locally processed in the dendrites before they reach the cell body. Dendritic properties enable local interactions between synapses and location-dependent modulations of inputs, rendering the position of synapses on dendrites highly important. These insights changed our view of neurons, such that we now think of them as small networks of nearly independent subunits instead of a simple point. Here, we propose that understanding how the brain processes information strongly requires that we consider the following properties: which plasticity mechanisms are present in the dendrites and how do they enable the self-organisation of synapses across the dendritic tree for efficient information processing? Ultimately, dendritic plasticity mechanisms can be studied in networks of neurons with dendrites, possibly uncovering unknown mechanisms that shape the connectivity in our brains.

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.

Cooperation of Genomic and Rapid Nongenomic Actions of Estrogens in Synaptic Plasticity

Neuroplasticity refers to the changes in the molecular and cellular processes of neural circuits that occur in response to environmental experiences. Clinical and experimental studies have increasingly shown that estrogens participate in the neuroplasticity involved in cognition, behavior, and memory. It is generally accepted that estrogens exert their effects through genomic actions that occur over a period of hours to days. However, emerging evidence indicates that estrogens also rapidly influence the neural circuitry through nongenomic actions. In this review, we provide an overview of the genomic and nongenomic actions of estrogens and discuss how these actions may cooperate in synaptic plasticity. We then summarize the role of epigenetic modifications, synaptic protein synthesis, and posttranslational modifications, and the splice variants of estrogen receptors in the complicated network of estrogens. The combination of genomic and nongenomic mechanisms endows estrogens with considerable diversity in modulating neural functions including synaptic plasticity.

Fear Memory

Fear memory is the best-studied form of memory. It was thoroughly investigated in the past 60 years mostly using two classical conditioning procedures (contextual fear conditioning and fear conditioning to a tone) and one instrumental procedure (one-trial inhibitory avoidance). Fear memory is formed in the hippocampus (contextual conditioning and inhibitory avoidance), in the basolateral amygdala (inhibitory avoidance), and in the lateral amygdala (conditioning to a tone). The circuitry involves, in addition, the pre- and infralimbic ventromedial prefrontal cortex, the central amygdala subnuclei, and the dentate gyrus. Fear learning models, notably inhibitory avoidance, have also been very useful for the analysis of the biochemical mechanisms of memory consolidation as a whole. These studies have capitalized on in vitro observations on long-term potentiation and other kinds of plasticity. The effect of a very large number of drugs on fear learning has been intensively studied, often as a prelude to the investigation of effects on anxiety. The extinction of fear learning involves to an extent a reversal of the flow of information in the mentioned structures and is used in the therapy of posttraumatic stress disorder and fear memories in general.

Nanoconnectomic upper bound on the variability of synaptic plasticity

Information in a computer is quantified by the number of bits that can be stored and recovered. An important question about the brain is how much information can be stored at a synapse through synaptic plasticity, which depends on the history of probabilistic synaptic activity. The strong correlation between size and efficacy of a synapse allowed us to estimate the variability of synaptic plasticity. In an EM reconstruction of hippocampal neuropil we found single axons making two or more synaptic contacts onto the same dendrites, having shared histories of presynaptic and postsynaptic activity. The spine heads and neck diameters, but not neck lengths, of these pairs were nearly identical in size. We found that there is a minimum of 26 distinguishable synaptic strengths, corresponding to storing 4.7 bits of information at each synapse. Because of stochastic variability of synaptic activation the observed precision requires averaging activity over several minutes.

DOI:http://dx.doi.org/10.7554/eLife.10778.001

What is the memory capacity of a human brain? The storage capacity in a computer memory is measured in bits, each of which can have a value of 0 or 1. In the brain, information is stored in the form of synaptic strength, a measure of how strongly activity in one neuron influences another neuron to which it is connected. The number of different strengths can be measured in bits. The total storage capacity of the brain therefore depends on both the number of synapses and the number of distinguishable synaptic strengths.

Structurally, neurons consist of a cell body that influences other neurons through a cable-like axon. The cell body bears numerous short branches called dendrites, which are covered in tiny protrusions, or “spines”. Most excitatory synapses are formed between the axon of one neuron and a dendritic spine on another. When two neurons on either side of a synapse are active simultaneously, that synapse becomes stronger, a form of memory. The dendritic spine also becomes larger to accommodate the extra molecular machinery needed to support a stronger synapse.

Some axons form two or more synapses with the same dendrite, but on different dendritic spines. These synapses should be the same strength because they will have experienced the same history of neural activity. Bartol et al. used a technique called serial section electron microscopy to create a 3D reconstruction of part of the brain that allowed the sizes of the dendritic spines these synapses form on to be compared. This revealed that the synaptic areas and volumes of the spine heads were nearly identical. This remarkable similarity can be used to estimate the number of bits of information that a single synapse can store, since the size of dendritic spines and their synapses can be used as proxies for synaptic strength.

Measurements in a small cube of brain tissue revealed 26 different dendritic spine sizes, each associated with a distinct synaptic strength. This number translates into a storage capacity of roughly 4.7 bits of information per synapse. This estimate is markedly higher than previous suggestions. It implies that the total memory capacity of the brain – with its many trillions of synapses – may have been underestimated by an order of magnitude. Additional measurements in the same and other brain regions are needed to confirm this possibility.

DOI:http://dx.doi.org/10.7554/eLife.10778.002

Synaptic consolidation: from synapses to behavioral modeling

Synaptic plasticity, a key process for memory formation, manifests itself across different time scales ranging from a few seconds for plasticity induction up to hours or even years for consolidation and memory retention. We developed a three-layered model of synaptic consolidation that accounts for data across a large range of experimental conditions. Consolidation occurs in the model through the interaction of the synaptic efficacy with a scaffolding variable by a read-write process mediated by a tagging-related variable. Plasticity-inducing stimuli modify the efficacy, but the state of tag and scaffold can only change if a write protection mechanism is overcome. Our model makes a link from depotentiation protocols in vitro to behavioral results regarding the influence of novelty on inhibitory avoidance memory in rats.

Information processing in the CNS: a supramolecular chemistry?

How does central nervous system process information? Current theories are based on two tenets: (a) information is transmitted by action potentials, the language by which neurons communicate with each other-and (b) homogeneous neuronal assemblies of cortical circuits operate on these neuronal messages where the operations are characterized by the intrinsic connectivity among neuronal populations. In this view, the size and time course of any spike is stereotypic and the information is restricted to the temporal sequence of the spikes; namely, the "neural code". However, an increasing amount of novel data point towards an alternative hypothesis: (a) the role of neural code in information processing is overemphasized. Instead of simply passing messages, action potentials play a role in dynamic coordination at multiple spatial and temporal scales, establishing network interactions across several levels of a hierarchical modular architecture, modulating and regulating the propagation of neuronal messages. (b) Information is processed at all levels of neuronal infrastructure from macromolecules to population dynamics. For example, intra-neuronal (changes in protein conformation, concentration and synthesis) and extra-neuronal factors (extracellular proteolysis, substrate patterning, myelin plasticity, microbes, metabolic status) can have a profound effect on neuronal computations. This means molecular message passing may have cognitive connotations. This essay introduces the concept of "supramolecular chemistry", involving the storage of information at the molecular level and its retrieval, transfer and processing at the supramolecular level, through transitory non-covalent molecular processes that are self-organized, self-assembled and dynamic. Finally, we note that the cortex comprises extremely heterogeneous cells, with distinct regional variations, macromolecular assembly, receptor repertoire and intrinsic microcircuitry. This suggests that every neuron (or group of neurons) embodies different molecular information that hands an operational effect on neuronal computation.

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.

Computational models in the age of large datasets

Technological advances in experimental neuroscience are generating vast quantities of data, from the dynamics of single molecules to the structure and activity patterns of large networks of neurons. How do we make sense of these voluminous, complex, disparate and often incomplete data? How do we find general principles in the morass of detail? Computational models are invaluable and necessary in this task and yield insights that cannot otherwise be obtained. However, building and interpreting good computational models is a substantial challenge, especially so in the era of large datasets. Fitting detailed models to experimental data is difficult and often requires onerous assumptions, while more loosely constrained conceptual models that explore broad hypotheses and principles can yield more useful insights.

Extinction learning, which consists of the inhibition of retrieval, can be learned without retrieval

In the present study we test the hypothesis that extinction is not a consequence of retrieval in unreinforced conditioned stimulus (CS) presentation but the mere perception of the CS in the absence of a conditioned response. Animals with cannulae implanted in the CA1 region of hippocampus were subjected to extinction of contextual fear conditioning. Muscimol infused intra-CA1 before an extinction training session of contextual fear conditioning (CFC) blocks retrieval but not consolidation of extinction measured 24 h later. Additionally, this inhibition of retrieval does not affect early persistence of extinction when tested 7 d later or its spontaneous recovery after 2 wk. Furthermore, both anisomycin, an inhibitor of ribosomal protein synthesis, and rapamycin, an inhibitor of extraribosomal protein synthesis, given into the CA1, impair extinction of CFC regardless of whether its retrieval was blocked by muscimol. Therefore, retrieval performance in the first unreinforced session is not necessary for the installation, maintenance, or spontaneous recovery of extinction of CFC.

Synapse rearrangements upon learning: from divergent-sparse connectivity to dedicated sub-circuits

Learning can involve formation of new synapses and loss of synapses, providing memory traces of learned skills. Recent findings suggest that these synapse rearrangements reflect assembly of task-related sub-circuits from initially broadly distributed and sparse connectivity in the brain. These local circuit remodeling processes involve rapid emergence of synapses upon learning, followed by protracted validation involving strengthening of some new synapses, and selective elimination of others. The timing of these consolidation processes can vary. Here, we review these findings, focusing on how molecular/cellular mechanisms of synapse assembly, strengthening, and elimination might interface with circuit/system mechanisms of learning and memory consolidation. An integrated understanding of these learning-related processes should provide a better basis to elucidate how experience, genetic background, and disease influence brain function.