1,721,083 research outputs found
A cerebellar learning model of vestibulo-ocular reflex adaptation in wild-type and mutant mice
No full textMechanisms of cerebellar motor learning are still poorly understood. The standard Marr-Albus-Ito theory posits that learning involves plasticity at the parallel fiber to Purkinje cell synapses under control of the climbing fiber input, which provides an error signal as in classical supervised learning paradigms. However, a growing body of evidence challenges this theory, in that additional sites of plasticity appear to contribute to motor adaptation. Here, we consider phase-reversal training of the vestibulo-ocular reflex (VOR), a simple form of motor learning for which a large body of experimental data is available in wild-type and mutant mice, in which the excitability of granule cells or inhibition of Purkinje cells was affected in a cell-specific fashion. We present novel electrophysiological recordings of Purkinje cell activity measured in naive wild-type mice subjected to this VOR adaptation task. We then introduce a minimal model that consists of learning at the parallel fibers to Purkinje cells with the help of the climbing fibers. Although the minimal model reproduces the behavior of the wild-type animals and is analytically tractable, it fails at reproducing the behavior of mutant mice and the electrophysiology data. Therefore, we build a detailed model involving plasticity at the parallel fibers to Purkinje cells' synapse guided by climbing fibers, feedforward inhibition of Purkinje cells, and plasticity at the mossy fiber to vestibular nuclei neuron synapse. The detailed model reproduces both the behavioral and electrophysiological data of both the wild-type and mutant mice and allows for experimentally testable predictions
Predictive plasticity in dendrites: from a computational principle to experimental data
Get PDFPlasticity of excitatory cortical synapses is thought to be the main mediator of mammalian learning. Due to the selective advantage that the ability to adapt to a changing environment grants, it is rea- sonable to assume that the processes governing these changes in connection strength are in some sense optimal. Yet, this has been difficult to reconcile with an “embarrassment of riches” of the LTP and LTD phenomenology. Here, we present an attempt at bridging this gap by showing that a mathematically derived model can exhibit some of these experimentally observed e↵ects, while still retaining functional capabilities under diverse learning paradigms.
Our work is based on a published plasticity model [3] that postulates that learning is driven by an intraneuronal prediction error where the weights of “student inputs” onto a dendritic compartment change in order to reproduce voltage changes imposed on a somatic compartment by “teacher inputs.” We show here that this two-compartment model of a pyramidal neuron can be extended in some simple ways, such as using conductance-based instead of current-based inputs, bringing it closer to the biophysics of pyramidal neurons. This allows us to reproduce a diverse set of experimental observations on cortical plasticity, such as di↵erent characteristics of the spike-timing dependence of plasticity.
Additionally, we show within a simple setup of a pattern recognition task that the extended model, while being less analytically tractable, can still perform well under unsupervised and reinforcement learning paradigms. Therefore, a single learning rule derived from the optimization of a well-defined cost function can be brought into correspondence with a large body of experimental evidence on synaptic plasticity, while still providing a diverse set of relevant functionality
Building new memories using the past: Interplay between semantic and episodic memory in brains and machines
No full textThis thesis explores how memories are built through the interaction between episodic
and semantic systems in the brain and in computational models. Episodic memory cap-
tures individual experiences, while semantic memory extracts structure and meaning
across them. Together, they allow both flexible adaptation and long-term stability, a
balance central to cognition. In Chapter 1, our Filopodium–Spine STDP model shows
how distinct synaptic structures support rapid learning and stable retention through
complementary competitive dynamics. In Chapter 2, we propose Homeostatic Binary
Networks, which demonstrate how semantics across concepts and selective represen-
tations can be extracted from highly overlapping representations. In Chapter 3, a
systems-level model reveals how semantic feedback enhances episodic recall, replay,
and compositional consolidation, suggesting a bidirectional dialogue between cortical
and subcortical networks. Across scales, these studies propose that memory formation
relies on cooperation between transient and stable mechanisms, offering insight into
how the brain and artificial systems build new knowledge from the past.Open Acces
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Synaptic plasticity: from single cell to microcircuit
Get PDFNeurons are the computational building blocks of our brains. They form complicated net- works that process the incoming sensory information, eventually leading to our actions. The connections between neurons can be altered, which is called synaptic plasticity. Synaptic plasticity is thought to underly learning and memory in the brain. In order to fully under- stand how our brain works, it is therefore crucial to understand these plasticity mechanisms. Moreover, neurons have unique morphologies with dendrites that can extend hundreds of micrometers, connecting different brain areas. In this thesis, we explored how dendrites influ- ence plasticity and how this translates to networks. In biophysically realistic neuron models, we investigated how spike-timing-dependent plasticity and dendritic-spike-dependent plas- ticity depend on the location of synapses across the dendritic tree. We then analysed the consequences of these dependencies on the connectivity between two neurons and showed how dendritic spikes can gate plasticity at other synapses. Furthermore, we explored how synaptic clusters can prolong memory retention. We then investigated this property in a net- work of simplified neurons, where we demonstrated how the learning of different associations easily depresses previously strengthened proximal synapses, but not distal synapses. Finally, we analysed how homeostasis can influence the connectivity in networks with excitatory and inhibitory neurons without dendrites. We showed how networks with similar connectivity to the one observed in primary visual cortex are more stable and can better discriminate between different activation patterns, compared to networks with a modified connectivity.Open Acces
Modelling and analysing of neural network dynamics
Get PDFNeurons are often considered as the basic functional units of the brain. Our brain contains about 100 billions of neurons, of many different types and connected among themselves in a multitude of different ways. Yet, from this overwhelming complexity, order can emerge in the form of neural dynamics, when cohorts of neurons show synchronous activity. For example, oscillations of neural activity can be observed in many brain regions and are thought to be associated with cognitive processes such as information transfer or memory consolidation. However, too much oscillation can be detrimental, as during epilepsy crisis. Therefore, there must be mechanisms regulating the dynamics of neural networks. Our aim in this thesis is to further our understanding of neural networks dynamics. First, we modelled a network of cortical neurons known to exhibit oscillations. In this network, GABAergic interneurons are connected via chemical and electrical synapses (also called gap junctions), which promote synchronisation of neural assemblies. We implemented a novel model of gap junction plasticity, based on recent experimental evidence that they alter their strength in an activity-dependent manner. We hypothesised that gap junction plasticity can regulate network-wide neural dynamics and we investigated functional implications on information transmission in cortex. We then considered a brain region rich in neuronal gap junctions, the thalamic reticular nucleus (TRN). The TRN is thought to be the source of patterns of waxing-and-waning oscillations called spindles. We hypothesised that gap junction plasticity could lead to spindles and we simulated their pharmacological manipulation. Finally, we analysed and modelled the relationship between neural activity and hemodynamic response from in vivo recordings. We hypothesised that this relationship can be modelled with a transfer function. We then investigated the transfer function dependency of location and brain states. At last, we studied the prediction of neural activity from hemodynamic response.Open Acces
Role of intrinsic neural properties in memory formation and maintenance
No full textWhile the mechanisms of synaptic plasticity underlying memory dynamics are well understood, the role of intrinsic neural properties has been largely unexplored, so far. Recent works studying memory dynamics over long timescales have unveiled mechanisms that synaptic models of memories have been unable to explain. First, neural ensembles representing individual memories of temporally close events tend to overlap in multiple brain regions. Moreover, memory representations are not static in time, and the neurons taking part of memory ensembles change over timescales of hours to days. Finally, the transcription of learning-related genes is altered in neurons taking part of memory ensembles, long after learning. Based on these recent findings, this thesis explores the role of intrinsic neural properties in memory dynamics, using a computational model. Overall, it provides predictions on the interplay between synaptic and intrinsic plasticity in memory formation and maintenance.Open Acces
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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