Interrogating structural plasticity among synaptic engrams

Our daily experiences and learnings are stored in the form of memories. These experiences trigger synaptic plasticity and persistent structural and functional changes in neuronal synapses. Recently, cellular studies of memory storage and engrams have emerged over the last decade. Engram cells reflect interconnected neurons via modified synapses. However, we were unable to observe the structural changes arising from synaptic plasticity in the past, because it was not possible to distinguish the synapses between engram cells. To overcome this barrier, dual-eGRASP (enhanced green fluorescent protein reconstitution across synaptic partners) technology can label specific synapses among multiple synaptic ensembles. Selective labeling of engram synapses elucidated their role by observing the structural changes in synapses according to the memory state. Dual-eGRASP extends cellular level engram studies to introduce the era of synaptic level studies. Here, we review this concept and possible applications of the dual-eGRASP, including recent studies that provided visual evidence of structural plasticity at the engram synapse.     

Theoretical aspects of neuroplasticity.

The authors propose an integrative theory of the organization of neuroplastic processes. Neuroplasticity is assumed to be one of the essential characteristics of the nervous tissue which may be manifested comparatively rapidly and result in reversible changes (functional plasticity). It may also modulate the expression of genotype into phenotype (adaptation) and thus bring about long-lasting effects. Neuroplastic mechanisms are triggered by various natural or artificial stimuli, which may arise in the internal or external environment, and they may differ quantitatively or qualitatively. The effects of plasticity can lead to either positive or negative changes during development (evolutionary plasticity), after short-term exposition (reactive plasticity), after long-term or continuous stimuli (adaptational plasticity), and during functional or structural recovery of damaged neuronal circuits (reparation plasticity). Manifestations of plasticity have probably the same basis, irrespective of the cause which triggered them or the brain region where they were accomplished. Neuroplastic mechanisms are based on the modulation of signal transmission across synapses. They can be related to interneuronal relations. The resulting changes may occur in the communication between neurons (synaptic level), in the activity of local neuronal circuits (at the level of local circuits) or in the relations between individual functional brain systems (multimodular level).     

Principles of the design of D-neuronal networks

A self-learning neuronal network called MACSIM (Mathematical Analyzer and Composer of Simple Melodies) for music pattern recognition is proposed. As universal network elements the D-neurons defined in the previous paper (Fedor and Majernik, 1977) are used. MACSIM is intended to model, at the level of elementar neuronal circuits, the processing of simple melodies which takes place in the human brain auditory system. Melody composing and music itself are considered in this study in order to verify the principles of design and functioning of the proposed basic D-neuronal networks (organs) of MACSIM, only. Two kinds of neuronal organs are incorporated in MACSIM, namely the so-called analyzers and the so-called neuro-effectors. The first ones carry out predicting, learning and decoding of the input sequences of tones. An analyzer consists of several neuronal trees that have the ability to specialize and respecialize so that every melody repeated several times can be recognized after a corresponding learning period. Such a self-learning process runs without structural changes within the mentioned networks. A melody which has been memorized in an analyzer can be memorized later in a neuro-effector, namely in a form suitable for its recall at the output of MACSIM. The neuronal organization of the neuro-effectors is similar, to a certain extent, to that of the cerebellar cortex. With the help of the staggered arrangement of D-neurons set up on the lines of the organization of Purkinje cells in the cerebellar cortex, a new reliable memory storage mechanism is proposed. This paper is not self-contained; the reader is assumed to be familiar with the above-mentioned paper.     

The synaptic plasticity and memory hypothesis: encoding,storage and persistence

The synaptic plasticity and memory hypothesis asserts that activity-dependent synaptic plasticity is induced at appropriate synapses during memory formation and is both necessary and sufficient for the encoding and trace storage of the type of memory mediated by the brain area in which it is observed. Criteria for establishing the necessity and sufficiency of such plasticity in mediating trace storage have been identified and are here reviewed in relation to new work using some of the diverse techniques of contemporary neuroscience. Evidence derived using optical imaging, molecular-genetic and optogenetic techniques in conjunction with appropriate behavioural analyses continues to offer support for the idea that changing the strength of connections between neurons is one of the major mechanisms by which engrams are stored in the brain.     

A clustered plasticity model of long-term memory engrams

Long-term memory and its putative synaptic correlates the late phases of both long-term potentiation and long-term depression require enhanced protein synthesis. On the basis of recent data on translation-dependent synaptic plasticity and on the supralinear effect of activation of nearby synapses on action potential generation, we propose a model for the formation of long-term memory engrams at the single neuron level. In this model, which we call clustered plasticity, local translational enhancement, along with synaptic tagging and capture, facilitates the formation of long-term memory engrams through bidirectional synaptic weight changes among synapses within a dendritic branch.     

Prefrontal phase locking to hippocampal theta oscillations

The interactions between cortical and hippocampal circuits are critical for memory formation, yet their basic organization at the neuronal network level is not well understood. Here, we demonstrate that a significant portion of neurons in the medial prefrontal cortex of freely behaving rats are phase locked to the hippocampal theta rhythm. In addition, we show that prefrontal neurons phase lock best to theta oscillations delayed by approximately 50 ms and confirm this hippocampo-prefrontal directionality and timing at the level of correlations between single cells. Finally, we find that phase locking of prefrontal cells is predicted by the presence of significant correlations with hippocampal cells at positive delays up to 150 ms. The theta-entrained activity across cortico-hippocampal circuits described here may be important for gating information flow and guiding the plastic changes that are believed to underlie the storage of information across these networks.     

Efficient Partitioning of Memory Systems and Its Importance for Memory Consolidation

Long-term memories are likely stored in the synaptic weights of neuronal networks in the brain. The storage capacity of such networks depends on the degree of plasticity of their synapses. Highly plastic synapses allow for strong memories, but these are quickly overwritten. On the other hand, less labile synapses result in long-lasting but weak memories. Here we show that the trade-off between memory strength and memory lifetime can be overcome by partitioning the memory system into multiple regions characterized by different levels of synaptic plasticity and transferring memory information from the more to less plastic region. The improvement in memory lifetime is proportional to the number of memory regions, and the initial memory strength can be orders of magnitude larger than in a non-partitioned memory system. This model provides a fundamental computational reason for memory consolidation processes at the systems level.     

Neuropeptides as synaptic transmitters

Neuropeptides are small protein molecules (composed of 3–100 amino-acid residues) that have been localized to discrete cell populations of central and peripheral neurons. In most instances, they coexist with low-molecular-weight neurotransmitters within the same neurons. At the subcellular level, neuropeptides are selectively stored, singularly or more frequently in combinations, within large granular vesicles. Release occurs through mechanisms different from classical calcium-dependent exocytosis at the synaptic cleft, and thus they account for slow synaptic and/or non-synaptic communication in neurons. Neuropeptide co-storage and coexistence can be observed throughout the central nervous system and are responsible for a series of functional interactions that occur at both pre- and post-synaptic levels. Thus, the subcellular site(s) of storage and sorting mechanisms into different neuronal compartments are crucial to the mode of release and the function of neuropeptides as neuronal messengers.The original work described here was supported by local grants from the University of Torino, Regione Piemonte and Compagnia di San Paolo.     

Aspects of neural plasticity in the central nervous system—VII. Theoretical aspects of brain communication and computation

Some aspects of the communicational and computational features of the central nervous system are discussed. The existence in the central nervous system of two main types of interneuronal communication, the wiring (i.e. the classical type of synaptic transmission) and the volume (i.e. a humoral type of non-synaptic transmission) transmission, has been proposed. Some features of these types of transmission are discussed, with special reference to the informational properties of peptide transmitters. With respect to the computational aspects of neural function, the identification of putative computational structures at the macroscopic (network) and microscopic (local circuit, synapse) levels suggests the existence of a computational hierarchical organization. In this context, the existence of a compartmental organization of various cerebral regions is discussed. It is hypothesized that membrane domains, made by patches of membrane in which preselected molecular movements are possible resulting in molecular interactions, can have an important role in the integrative capabilities of neural tissue. The coexistence of multiple neuroactive substances in central synapses is analyzed in the framework of information transfer processes at this level. The presence of putative homeostatic, heterostatic and mnestic mechanisms in the synapse is also discussed.     

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.     

Modelling studies on the computational function of fast temporal structure in cortical circuit activity

The interplay between modelling and experimental studies can support the exploration of the function of neuronal circuits in the cortex. We exemplify such an approach with a study on the role of spike timing and gamma-oscillations in associative memory in strongly connected circuits of cortical neurones. It is demonstrated how associative memory studies on different levels of abstraction can specify the functionality to be expected in real cortical neuronal circuits. In our model overlapping random configurations of sparse cell populations correspond to memory items that are stored by simple Hebbian coincidence learning. This associative memory task will be implemented with biophysically well tested compartmental neurones developed by Pinsky and Rinzel . We ran simulation experiments to study memory recall in two network architectures: one interconnected pool of cells, and two reciprocally connected pools. When recalling a memory by stimulating a spatially overlapping set of cells, the completed pattern is coded by an event of synchronized single spikes occurring after 25-60 ms. These fast associations are performed even at a memory load corresponding to the memory capacity of optimally tuned formal associative networks (>0.1 bit/synapse). With tonic stimulation or feedback loops in the network the neurones fire periodically in the gamma-frequency range (20-80 Hz). With fast changing inputs memory recall can be switched between items within a single gamma cycle. Thus, oscillation is not a primary coding feature necessary for associative memory. However, it accompanies reverberatory feedback providing an improved iterative memory recall completed after a few gamma cycles (60-260 ms). In the bidirectional architecture reverberations do not express in a rigid phase locking between the pools. For small stimulation sets bursting occurred in these cells acting as a supportive mechanism for associative memory.     

Dynamics control of semantic processes in a hierarchical associative memory

A neural mechanism for control of dynamics and function of associative processes in a hierarchical memory system is demonstrated. For the representation and processing of abstract knowledge, the semantic declarative memory system of the human brain is considered. The dynamics control mechanism is based on the influence of neuronal adaptation on the complexity of neural network dynamics. Different dynamical modes correspond to different levels of the ultrametric structure of the hierarchical memory being invoked during an associative process. The mechanism is deterministic but may also underlie free associative thought processes. The formulation of an abstract neural network model of hierarchical associative memory utilizes a recent approach to incorporate neuronal adaptation. It includes a generalized neuronal activation function recently derived by a Hodgkin-Huxley-type model. It is shown that the extent to which a hierarchically organized memory structure is searched is controlled by the neuronal adaptability, i.e. the strength of coupling between neuronal activity and excitability. In the brain, the concentration of various neuromodulators in turn can regulate the adaptability. An autonomously controlled sequence of bifurcations, from an initial exploratory to a final retrieval phase, of an associative process is shown to result from an activity-dependent release of neuromodulators. The dynamics control mechanism may be important in the context of various disorders of the brain and may also extend the range of applications of artificial neural networks.     

Dynamics control of semantic processes in a hierarchical associative memory

 A neural mechanism for control of dynamics and function of associative processes in a hierarchical memory system is demonstrated. For the representation and processing of abstract knowledge, the semantic declarative memory system of the human brain is considered. The dynamics control mechanism is based on the influence of neuronal adaptation on the complexity of neural network dynamics. Different dynamical modes correspond to different levels of the ultrametric structure of the hierarchical memory being invoked during an associative process. The mechanism is deterministic but may also underlie free associative thought processes. The formulation of an abstract neural network model of hierarchical associative memory utilizes a recent approach to incorporate neuronal adaptation. It includes a generalized neuronal activation function recently derived by a Hodgkin-Huxley-type model. It is shown that the extent to which a hierarchically organized memory structure is searched is controlled by the neuronal adaptability, i.e. the strength of coupling between neuronal activity and excitability. In the brain, the concentration of various neuromodulators in turn can regulate the adaptability. An autonomously controlled sequence of bifurcations, from an initial exploratory to a final retrieval phase, of an associative process is shown to result from an activity-dependent release of neuromodulators. The dynamics control mechanism may be important in the context of various disorders of the brain and may also extend the range of applications of artificial neural networks. Received: 19 April 1995/Accepted in revised form: 8 August 1995     

Motor neurons and the sense of place

Seventy years ago George Romanes began to document the anatomical organization of the spinal motor system, uncovering a multilayered topographic plan that links the clustering and settling position of motor neurons to the spatial arrangement and biomechanical features of limb muscles. To this day, these findings have provided a structural foundation for analysis of the neural control of movement and serve as?a guide for studies to explore mechanisms that direct the wiring of spinal motor circuits. In this brief essay we outline the?core of Romanes's findings and place them in the context of recent studies that begin to provide insight?into molecular programs that assign motor pool position and to resolve how motor neuron position shapes circuit assembly. Romanes's findings reveal how and why neuronal positioning contributes to sensory-motor connectivity and may have relevance to circuit organization in other regions of the central nervous system.     

Progress in neural plasticity

The year 2009 marks the tenth anniversary of the founding of Institute of Neuroscience (ION) in the Shanghai campus of Chinese Academy of Sciences.     

Molecular cloning and characterization of Polygalacturonase-Inhibiting Protein and Cinnamoyl-Coa Reductase genes and their association with fruit storage conditions in blueberry (Vaccinium corymbosum)

Blueberry is a widely grown and easily perishable fruit crop. An efficient post-harvest handling is critical, and for that purpose gene technology methods have been part of ongoing programmes to improve crops with high food values such as blueberry. Here we report the isolation, cloning, characterization and differential expression levels of two cDNAs encoding Polygalacturonase-Inhibitor Protein (PGIP) and Cinnamoyl-Coa Reductase (CCR) from blueberry fruits in relation to various storage conditions. The open reading frame of PGIP and CCR encodes a polypeptide of 329 and 347 amino acids, respectively. To assess changes in the expression of blueberry PGIP and CCR after harvest, a storage trial was initiated. The northern blots hybridization showed a clear differential expression level of PGIP and CCR between freshly harvested and stored fruits as well as between fruits stored under various storage conditions. Although the prospects of exploiting such a strategy for crop improvement are limited, the results provide further insight into the control of the quality over the storage period at the molecular level.     

Odor Memory Stability after Reinnervation of the Olfactory Bulb

The olfactory system, particularly the olfactory epithelium, presents a unique opportunity to study the regenerative capabilities of the brain, because of its ability to recover after damage. In this study, we ablated olfactory sensory neurons with methimazole and followed the anatomical and functional recovery of circuits expressing genetic markers for I7 and M72 receptors (M72-IRES-tau-LacZ and I7-IRES-tau-GFP). Our results show that 45 days after methimazole-induced lesion, axonal projections to the bulb of M72 and I7 populations are largely reestablished. Furthermore, regenerated glomeruli are re-formed within the same areas as those of control, unexposed mice. This anatomical regeneration correlates with functional recovery of a previously learned odorant-discrimination task, dependent on the cognate ligands for M72 and I7. Following regeneration, mice also recover innate responsiveness to TMT and urine. Our findings show that regeneration of neuronal circuits in the olfactory system can be achieved with remarkable precision and underscore the importance of glomerular organization to evoke memory traces stored in the brain.     

Mesoscopic Organization Reveals the Constraints Governing Caenorhabditis elegans Nervous System

One of the biggest challenges in biology is to understand how activity at the cellular level of neurons, as a result of their mutual interactions, leads to the observed behavior of an organism responding to a variety of environmental stimuli. Investigating the intermediate or mesoscopic level of organization in the nervous system is a vital step towards understanding how the integration of micro-level dynamics results in macro-level functioning. The coordination of many different co-occurring processes at this level underlies the command and control of overall network activity. In this paper, we have considered the somatic nervous system of the nematode Caenorhabditis elegans, for which the entire neuronal connectivity diagram is known. We focus on the organization of the system into modules, i.e., neuronal groups having relatively higher connection density compared to that of the overall network. We show that this mesoscopic feature cannot be explained exclusively in terms of considerations such as, optimizing for resource constraints (viz., total wiring cost) and communication efficiency (i.e., network path length). Even including information about the genetic relatedness of the cells cannot account for the observed modular structure. Comparison with other complex networks designed for efficient transport (of signals or resources) implies that neuronal networks form a distinct class. This suggests that the principal function of the network, viz., processing of sensory information resulting in appropriate motor response, may be playing a vital role in determining the connection topology. Using modular spectral analysis we make explicit the intimate relation between function and structure in the nervous system. This is further brought out by identifying functionally critical neurons purely on the basis of patterns of intra- and inter-modular connections. Our study reveals how the design of the nervous system reflects several constraints, including its key functional role as a processor of information.     

Place Cell Rate Remapping by CA3 Recurrent Collaterals

Episodic-like memory is thought to be supported by attractor dynamics in the hippocampus. A possible neural substrate for this memory mechanism is rate remapping, in which the spatial map of place cells encodes contextual information through firing rate variability. To test whether memories are stored as multimodal attractors in populations of place cells, recent experiments morphed one familiar context into another while observing the responses of CA3 cell ensembles. Average population activity in CA3 was reported to transition gradually rather than abruptly from one familiar context to the next, suggesting a lack of attractive forces associated with the two stored representations. On the other hand, individual CA3 cells showed a mix of gradual and abrupt transitions at different points along the morph sequence, and some displayed hysteresis which is a signature of attractor dynamics. To understand whether these seemingly conflicting results are commensurate with attractor network theory, we developed a neural network model of the CA3 with attractors for both position and discrete contexts. We found that for memories stored in overlapping neural ensembles within a single spatial map, position-dependent context attractors made transitions at different points along the morph sequence. Smooth transition curves arose from averaging across the population, while a heterogeneous set of responses was observed on the single unit level. In contrast, orthogonal memories led to abrupt and coherent transitions on both population and single unit levels as experimentally observed when remapping between two independent spatial maps. Strong recurrent feedback entailed a hysteretic effect on the network which diminished with the amount of overlap in the stored memories. These results suggest that context-dependent memory can be supported by overlapping local attractors within a spatial map of CA3 place cells. Similar mechanisms for context-dependent memory may also be found in other regions of the cerebral cortex.