A Model of Synaptic Reconsolidation

Wulfram Gerstner, David Barak Kastner, Tilo Schwalger, Lorric Ziegler
Frontiers Research Foundation, 2016
Article

Reconsolidation of memories has mostly been studied at the behavioral and molecular level. Here, we put forward a simple extension of existing computational models of synaptic consolidation to capture hippocampal slice experiments that have been interpreted as reconsolidation at the synaptic level. The model implements reconsolidation through stabilization of consolidated synapses by stabilizing entities combined with an activity-dependent reservoir of stabilizing entities that are immune to protein synthesis inhibition (PSI). We derive a reduced version of our model to explore the conditions under which synaptic reconsolidation does or does not occur, often referred to as the boundary conditions of reconsolidation. We find that our computational model of synaptic reconsolidation displays complex boundary conditions. Our results suggest that a limited resource of hypothetical stabilizing molecules or complexes, which may be implemented by protein phosphorylation or different receptor subtypes, can underlie the phenomenon of synaptic reconsolidation.

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Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience

Emmanouil Angelidis, Alain Destexhe, Egidio Falotico, Emanuele Formento, Marc-Oliver Gewaltig, Auke Ijspeert, Silvestro Micera, Maria Pasquini, Shravan Tata Ramalingasetty, Lorenzo Vannucci, Axel von Arnim

Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.

FRONTIERS MEDIA SA2020

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Perceptual learning: why mice do not play backgammon

Elisa Tartaglia

Perceptual learning is the ability to modify perception through practice. As a form of brain plasticity, perceptual learning has been studied for more than thirty years in different fields including psychology, neurophysiology and computational neuroscience. Thanks to its simple nature, perceptual learning is often considered a basic form of learning, and an optimal starting point to understand more complex forms of plasticity. From a computational perspective, perceptual learning is usually described with simple neural network architectures. Here I demonstrate by empirical results and theoretical considerations why current computational models are inadequate to describe perceptual learning. Paradoxically, simple computational models often largely outperform human performance. While very simple feed-forward neural networks can learn any stimulus-response mapping, the human brain cannot. Common neural networks consider the learning process a mere computational problem, primarily focusing on stimulus response mappings. However, I will show that perceptual learning is a more complex phenomenon. For example, while neural networks can learn only one task per network, in real learning situations, human observers often have to learn multiple tasks by using the very same neurons. Hence, more plausible computational approaches that account for the complex learning situations of human observers, need to be developed.

EPFL2009

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Plasticité synaptique

La plasticité synaptique, en neurosciences, désigne la capacité des synapses à moduler, à la suite d'un événement particulier - une augmentation ou une diminution ponctuelle et significative de leur a

Memory consolidation

Memory consolidation is a category of processes that stabilize a memory trace after its initial acquisition. A memory trace is a change in the nervous system caused by memorizing something. Consolida

Computational model

A computational model uses computer programs to simulate and study complex systems using an algorithmic or mechanistic approach and is widely used in a diverse range of fields spanning from physics,