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

Memory prosthesis: is it time for a deep neuromimetic approach?

Memory loss, one of the most dreaded afflictions of the human condition, presents considerable burden on the world’s health care system and it is recognized as a major challenge in the elderly. There are only a few neuro-modulation treatments for memory dysfunctions. Open loop deep brain stimulation is such a treatment for memory improvement, but with limited success and conflicting results. In recent years closed-loop neuropros-thesis systems able to simultaneously record signals during behavioural tasks and generate with the use of inter-nal neural factors the precise timing of stimulation patterns are presented as attractive alternatives and show promise in memory enhancement and restoration. A few such strides have already been made in both animals and humans, but with limited insights into their mechanisms of action. Here, I discuss why a deep neuromimetic computing approach linking multiple levels of description, mimicking the dynamics of brain circuits, interfaced with recording and stimulating electrodes could enhance the performance of current memory prosthesis systems, shed light into the neurobiology of learning and memory and accelerate the progress of memory prosthesis research. I propose what the necessary components (nodes, structure, connectivity, learning rules, and physi-ological responses) of such a deep neuromimetic model should be and what type of data are required to train/ test its performance, so it can be used as a true substitute of damaged brain areas capable of restoring/enhancing their missing memory formation capabilities. Considerations to neural circuit targeting, tissue interfacing, elec-trode placement/implantation and multi-network interactions in complex cognition are also provided

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

Υπολογιστική μελέτη του ρόλου των δενδριτών στην κατανομή μνήμης σε νευρώνες και συνάψεις

It is generally accepted that the brain stores memories in distributed neuronal representations. The long-term storage of memories is believed to take place through the strengthening and weakening of synaptic connections between neurons. Recent research has begun to probe the mechanisms that underlie these changes in synaptic connections and to identify the correlates of specific memories in the brain, which are known as memory engrams or traces. These studies have identified a number of different factors, which determine memory storage, from molecular processes to network electrophysiological phenomena. Despite this progress in identifying pieces of the puzzle of the mechanism of memory, we are still lacking a unified framework, which can explain the experimental findings with regards to memory, and can predict the structure of memory traces. In this thesis we present a novel modeling approach to memory acquisition which combines multiple experimentally observed phenomena. We apply this model in specific well-studied memory-related experimental protocols and we use it to predict the structure of the resulting memory traces in multiple spatial scales, from the synaptic to the neuronal network level. In order to create a unifying framework for studying memory formation we incorporate mechanisms related to the consolidation of long-term memories in the brain. These include the mechanisms for plasticity-related protein capture according to the synaptic tagging and capture model, the localized modulation of excitability, as well as the effects of homeostasis and inhibition. Importantly, we study the effect of dendritic compartmentalization, which is known to affect memory, by incorporating dendritic phenomena in the description of neurons. Using this model, we show that the sub-cellular structure of memories, which consists of the distribution of synapses to specific neurons and specific dendritic branches is dependent on the ability of neurons to synthesize plasticity proteins in dendrites, and on the activation history of the neuron, which affects its excitability. In addition, we find that synapses tend to potentiate in groups, a phenomenon known as synapse clustering. We extend the model to the case of multiple memories in order to examine their possible interactions and find that the neuronal populations and the synapses that represent time-related memories are intertwined. Using the model, we predict the overlapping components of different memories as a function of time. Finally, we examine the role of dendritic spine reorganization, which occurs constantly in the brain, in the storage of memories.Είναι γενικά αποδεκτό ότι ο οι μνήμες αποθηκεύονται με διάσπαρτο τρόπο στον εγκέφαλο. Η μακρόχρονη αποθήκευση της μνήμης πιστεύεται ότι λαμβάνει χώρα μέσω της ενδυνάμωσης και αποδυνάμωσης των συναπτικών συνδέσεων μεταξύ νευρώνων. Πρόσφατες έρευνες έχουν αρχίσει να εξερευνούν τους μηχανισμούς μέσω των οποίων γίνονται αυτές οι συνδέσεις, και να ταυτοποιούν το βιοφυσικό υπόστρωμα στο οποίο αποθηκεύονται συγκεκριμένες μνήμες, το ονομαζόμενο μνημονικό έγγραμμα ή ίχνος. Οι έρευνες αυτές έχουν ταυτοποιήσει ποικίλους διαφορετικούς μηχανισμούς, από το μοριακό ως το επίπεδο του νευρωνικού δικτύου, οι οποίοι καθορίζουν την μνημονική αποθήκευση. Παρ’ όλη την πρόσφατη πρόοδο για την ανακάλυψη των μηχανισμών μνήμης, δεν υπάρχει ακόμα μια ολοκληρωμένη θεωρία που να μπορεί να εξηγήσει τα πειραματικά ευρήματα σχετικά με τη μνήμη και να μπορεί να προβλέψει τη δομή των μνημονικών εγγραμμάτων.Σε αυτή την διατριβή παρουσιάζουμε μια πρότυπη προσέγγιση στην μοντελοποίηση της διαδικασίας της πρόσκτησης μνήμης που λαμβάνει υπόψιν της πολλαπλά πειραματικώς επιβεβαιωμένα φαινόμενα. Εφαρμόζουμε το μοντέλο αυτό σε συγκεκριμένα καλά μελετημένα πειραματικά πρωτόκολλα μνήμης και το χρησιμοποιούμε για να προβλέψουμε την εσωτερική δομή των εγγραμμάτων μνήμης σε πολλαπλές χωρικές κλίμακες, από την κλίμακα των συνάψεων ως την κλίμακα του νευρωνικού δικτύου. Προκειμένου να δημιουργήσουμε ένα ενοποιημένο υπόβαθρο για τη μελέτη της πρόσκτησης μνήμης, εισάγουμε πολλαπλούς μηχανισμούς και κανόνες σχετικούς με την παγιοποίηση της μακρόχρονης μνήμης. Σε αυτούς τους μηχανισμούς συγκαταλέγονται οι μηχανισμοί για την δέσμευση πρωτεϊνών από συνάψεις σύμφωνα με το μοντέλο "συναπτικής σήμανσης και δέσμευσης", ο μηχανισμός δενδριτικής αλλαγής της διεγερσιμότητας των νευρώνων, καθώς και οι συνέπειες των ομοιοστατικών μηχανισμών και η επίδραση των κατασταλτικών νευρώνων. Ένα σημαντικό χαρακτηριστικό είναι ότι μελετάμε τις συνέπειες της δενδριτικής διαμερισματοποίησης των νευρώνων, που είναι γνωστό ότι επηρεάζει τις ιδιότητες της μνήμης, υλοποιώντας κανόνες που αντιστοιχούν σε δενδριτικά φαινόμενα στους νευρώνες του μοντέλου. Αρχικά, χρησιμοποιώντας το μοντέλο, δείχνουμε ότι η υποκυτταρική δομή των εγγραμμάτων μνήμης, που αποτελείται από την κατανομή συνάψεων σε συγκεκριμένους νευρώνες και σε συγκεκριμένους δενδριτικούς κλάδους, εξαρτάται από την διαθεσιμότητα πρωτεϊνών στους δενδρίτες και από το ιστορικό ενεργοποίησης του νευρώνα, το οποίο επηρεάζει τη διεγερσιμότητά του. Επιπλέον, βρίσκουμε ότι οι συνάψεις τείνουν να ενδυναμώνονται κατά ομάδες δημιουργώντας συστάδες συνάψεων. Στη συνέχεια επεκτείνουμε το μοντέλο στη μελέτη περισσότερων από μία μνημών και βρίσκουμε ότι οι νευρωνικοί πληθυσμοί που αντιπροσωπεύουν τις διαφορετικές μνήμες είναι αλληλοεπικαλυπτόμενοι. Με χρήση του μοντέλου, προβλέπουμε την αλληλοεπικάλυψη ως συνάρτηση του χρόνου. Τέλος, εξετάζουμε το ρόλο της ανακατάταξης συναπτικών συνδέσεων (η οποία γίνεται συνεχώς στον εγκέφαλο) , στην μνημονική αποθήκευση

Linking memories across time via excitability and synaptic tagging

<p>Slides for the presentation</p

GABAergic interneurons with nonlinear dendrites: from neuronal computations to memory engrams

GABAergic interneurons are a highly diverse class of neurons in the mammalian brain with a critical role in orchestrating multiple cognitive functions and maintaining the balance of excitation/ inhibition across neuronal circuitries. In this perspective, we discuss recent findings regarding the ability of some interneuron subtypes to integrate incoming inputs in nonlinear ways within their dendritic branches. These recently discovered features may endow the specific interneurons with advanced computing capabilities, whose breadth and functional contributions remain an open question. Along these lines, we discuss theoretical and experimental evidence regarding the potential role of nonlinear interneuron dendrites in advancing single neuron computations and contributing to memory formation