Mémoire à court terme | EPFL Graph Search

La mémoire à court terme (MCT) désigne en psychologie le type de mémoire qui permet de retenir et de réutiliser une quantité limitée d'informations pendant un temps relativement court, environ une demi-minute. Un grand nombre de recherches en psychologie cognitive ont cherché à déterminer les caractéristiques (capacité, durée, fonctionnement) et le rôle de la mémoire à court terme dans la cognition. Introduction et historique Le concept de mémoire à court terme est assez ancien en psychologie scientifique. William James dans ses Principles of psychology (1890) distingue déjà ce qu'il appelle la mémoire primaire de la mémoire secondaire. Cette mémoire primaire serait capable de retenir un petit nombre d'informations, immédiatement présentes à l'esprit, alors que la mémoire secondaire contient un nombre pratiquement illimité de connaissances qui nécessitent cependant un effort pour être rappelées. À la même époque cette distinction se retrouve aussi dans les travaux de l'A

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

In this dissertation, I develop theory and evidence to argue that new technologies are central to how firms organize to create and capture value. I use computational methods such as reinforcement learning and probabilistic topic modeling to investigate three topics: the automation of routines, the organization of artificial intelligence (AI), and the evaluation of technology risk. Overall, I argue that new technologies are not a panacea for the firm but require deliberate strategic planning to manage the potential downsides of myopic automation, AI interdependencies, and the disclosure of technology risks.In the first essay, I argue that while automation can increase productivity by reducing the costs of coordinating individuals, the automation of routines can also incur an indirect opportunity cost due to slow adaptation to environmental change. I develop a reinforcement learning simulation to model the impact of automation on the returns from the division of labor in dynamic environments and to show how automation incurs opportunity costs through lost learning and slow adaptation. Moreover, automation can be suboptimal when it brings about myopic behavior, i.e. high returns from the division of labor in the short term, but negative returns in the long term. Given the simulation results, I argue that firms need dynamic routines to simultaneously balance learning and automation. I open-source the simulation platform as OrgSim-RL on GitHub.In the second essay, I argue that a data-driven culture - what I define as a Data Clan - can help to coordinate complex interdependencies between AI components within a firm. I analyze in-depth semi-structured interview data with a hierarchical stochastic block model (hSBM) and hand-coding to find that managers focus primarily on building a strong culture and establishing high-quality data assets when allocating resources to AI initiatives. Given the results, I inductively develop implications for theory and argue that the emergence of a Data Clan can be a governance mechanism to reduce coordination frictions and build a competitive advantage in the age of AI.In the third essay, I argue that investors require a higher initial return to take on more technology risk disclosure during an IPO. I quantify the magnitude of disclosed risk and the risk disclosure topics based on a latent Dirichlet allocation (LDA) topic model of IPO prospectus text and find a return-for-risk association between text-based technology risk disclosure and underpricing. The study also finds evidence that owning granted patents is associated with a lower return-for-risk association, suggesting that intellectual property allows the disclosure of risk without losing the competitive advantage. I open-source the code for quantifying risk disclosure as RiskyData-LDA on GitHub.In summary, this dissertation develops theory and finds evidence across three essays to argue that leveraging new technologies requires deliberate strategic planning to manage potential downsides of new technologies, such as the opportunity costs of automation, coordination costs, and costs associated with raising capital. The results suggest three mitigating solutions: dynamic routines to balance learning and automation, a Data Clan to improve coordination, and disclosure through patents to reduce underpricing.

EPFL2022

Strontium Substituted Tricalcium Phosphate Bone Cement: Short and Long-Term Time-Resolved Studies and In Vitro Properties

Marta Di Fabrizio

Due to a significant influence of strontium (Sr) on bone regeneration, Sr substituted beta-tricalcium phosphate (Sr-TCP) cement is prepared and investigated by short- and long-term time-resolved techniques. For short-term investigations, energy-dispersive X-ray diffraction, infrared spectroscopy, and, for the first time, terahertz time-domain spectroscopy techniques are applied. For long-term time-resolved studies, angular dispersive X-ray diffraction, scanning electron microscopy, mechanical tests, and behavior in Ringer solution are carried out. After 45 min of the cement setting, the Sr-TCP phase is no longer detectable. During this time period, an appearance and constant increase of the final brushite phase are registered. The compressive strength of the Sr-TCP cement increases from 4.5 MPa after 2 h of setting and reaches maximum at 13.3 MPa after 21 d. After cement soaking for 21 d in Ringer solution, apatite final product, with an admixture of brushite and TCP phases is detected. The cytotoxicity aspects of the prepared cement are investigated using NCTC 3T3 fibroblast cell line, and the cytocompatibility-by human dental pulp mesenchymal stem cells. The obtained results allow to conclude that the developed Sr-TCP cement is promising for biomedical applications for bone tissue.

WILEY2022

Elucidating the role of neuron-astrocyte metabolic coupling in learning and memory

Monika Tadi

The brain has very high energy demands that are mainly met by the circulating blood glucose to ensure its proper functioning. Thus, it is not surprising that though the human brain weighs only 2- 3% of the body weight, it consumes approximately 25% of total body glucose. “Neuro-energetic coupling" is a unique feature of the brain and refers to the existence of the tight coupling between (neuronal) synaptic brain activity and energy metabolism. The main excitatory neurotransmitter in the brain is glutamate and most of the energy requirements at the cortical level are met through glutamate-mediated neurotransmission, suggesting that there is a close relationship between brain activity, glutamatergic neurotransmission and energy metabolism. Until the 80s, blood-borne glucose was thought to be the sole energy substrate of the brain cells and it was assumed that glucose is metabolized by neurons and astrocytes (a type of glial cell) independently. It is only during the last two decades that the concept of compartmentalization of energy metabolism between neurons and astrocytes has become more obvious. The transfer of lactate from astrocytes to neurons termed as the “astrocyte-neuron lactate shuttle (ANLS)” model proposed by Dr. Pellerin and Prof. Magistretti posits that glutamate released during brain activation activates glycolysis (one of glucose metabolic pathways) in astrocytes leading to lactate (a monocarboxylate) release in the extracellular space, and this astrocytic lactate is then captured and used as an energy substrate by the activated neurons to meet their energy requirements. It includes a complex chain of events involved in neuro-metabolic coupling and supports the preferential use of lactate by activated neurons under conditions of increased energy demands. Higher brain functions such as learning a new task are associated with structural changes in brain and are metabolically demanding necessitating the need of an additional energy supply to meet the neurons increased energy requirement. In this context, the current thesis project aimed to elucidate the contribution of metabolic coupling between astrocytes and neurons, known as “neuron-astrocyte metabolic coupling” to learning and memory formation. Using an in vivo approach combined with behavioral, molecular and gene manipulation techniques in mice, we set out to investigate the role of neuron-astrocyte metabolic coupling, particularly ANLS in cognition. The project was divided into two main parts: In the first part, the expression of glucose metabolism-related genes was investigated during long term memory (LTM) formation in fear-motivated inhibitory avoidance (IA) learning paradigm. Using (14C) 2-Deoxyglucose (2-DG) technique, we first defined the brain areas that are involved in IA LTM formation. This technique provides an indirect measurement of brain activity by measuring glucose uptake in different brain areas during the task. The results of brain metabolic mapping show an increase in glucose utilization in the hippocampus, amygdala, anterior cingulate cortex and pre-limbic and infra-limbic cortical areas. Results from the quantitative analysis of mRNA levels in the dorsal hippocampus at different time point’s following IA task highlight a time dependent, learning specific change in the expression of genes related to glucose metabolism. Specific set of genes involved in the synthesis and degradation of glycogen (brain glucose reserve present only in astrocytes), pyruvate metabolism and pentose phosphate shunt as well as the ANLS were induced following the IA task. These early observations indicate that the metabolic interactions between astrocytes and neurons undergo modifications during a learning process and suggest that these adaptations may play a key role in the establishment of long-term memory. In the second part of research project, we focused on investigating the behavioral consequences of down regulating the expression of Monocarboxylate Transporter 1 (MCT1), a key ANLS related gene, on cognition and higher brain functions. Mice heterozygous for the MCT1 gene (MCT1+/-) were characterized in numerous behavioral paradigms such as general wellbeing, reflexive and motor capabilities. After having established that MCT1+/- mice have no alterations in general emotional state or in locomotion, a battery of behavioral tests was performed to study cognition in these mice. Compared with wild-type control mice, the MCT1+/- mice display normal muscle strength and motor coordination. However, when tested for higher brain functions, the MCT1+/- mice exhibit learning and memory deficits in several learning paradigms/tasks that are hippocampus and / or amygdala dependent. The cognitive impairment observed in the MCT1 heterozygous mice further highlights the importance of ANLS in memory and suggest that disruption of lactate transfer from astrocytes to neurons via the MCT1 results in cognitive impairment. The overall results in this thesis demonstrate that cerebral energy metabolism, and in particular the transfer of lactate from astrocytes to neurons not only provides temporal adaptations in the learning process, but it also plays a fundamental role in memory formation.

EPFL2013

Elucidating the role of neuron-astrocyte metabolic coupling in learning and memory

Monika Tadi

EPFL2013

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

EPFL2022