Long-term memory | EPFL Graph Search

Long-term memory (LTM) is the stage of the Atkinson–Shiffrin memory model in which informative knowledge is held indefinitely. It is defined in contrast to short-term and working memory, which persist for only about 18 to 30 seconds. LTM is commonly labelled as "explicit memory" (declarative), as well as "episodic memory," "semantic memory," "autobiographical memory," and "implicit memory" (procedural memory). Stores The idea of separate memories for short- and long-term storage originated in the 19th century. A model of memory developed in the 1960s assumed that all memories are formed in one store and transfer to others store after a small period of time. This model is referred to as the "modal model", most famously detailed by Shiffrin. The model states that memory is first stored in sensory memory, which has a large capacity but can only maintain information for milliseconds. A representation of that rapidly decaying memory is moved to short-term memory. Short-term mem

Epigenetic memory aids: Synaptic and molecular effects of HDAC inhibition that support memory formation

Allison Marie Burns

Learning and memory rely on synaptic communication in which intracellular signals are transported to the nucleus to stimulate transcriptional activation. Memory induced transcriptional increases are accompanied by alterations to the epigenetic landscape and can be pharmacologically mimicked to ameliorate memory in both healthy and cognitively impaired animals. Of particular interest in this regard is histone acetylation, as this epigenetic modification is enriched during memory formation and is readily amenable to pharmacological manipulation by means of histone deacetylase inhibitors (HDACis). Although multiple studies have shown that systemic HDACi administration can improve memory formation, their mode of action is not yet fully understood. In this study, we tested whether HDACis â given systemically â augment epigenetic, transcriptional and electrophysiological responses that are induced by learning, thus amplifying naturally occurring responses via cognitive epigenetic priming. To do this, we combined a system-wide HDACi treatment with a sub-threshold contextual fear conditioning (CFC) paradigm, a weakened Pavlovian modal that, alone, does not lead to long term memory formation. We found that, in combination, HDACi and CFC improve recent contextual memory that is accompanied by enhanced long-term potentiation in the hippocampus. Conversely, combined HDACi-CFC treatment induced no such synaptic strengthening in the striatum, a brain region that is not directly activated by fear conditioning. We then used bulk and single nuclear RNA-sequencing to show that HDACi activates unique transcriptional pathways, both between the two brain regions and between cell types within the hippocampus, indicating that HDACi does not act indiscriminatingly, but instead supports cellular processes that are already occurring in response to the conditioning. Finally, we show that HDACi treatment paired with contextual fear conditioning enriches H3K27ac at enhancers of known memory-related genes within the hippocampus but that not all these genes are necessarily more transcriptionally active at the same time point. These results indicate that systemic HDACi administration acts as a cognitive enhancer by amplifying brain-region specific processes that are naturally induced during memory formation but that these results may not be a direct effect of enhancer histone acetylation. These findings shed light on the mechanisms of HDACi-mediated cognitive epigenetic priming and help to pave the way for potential new therapies for memory-related disorders.

EPFL2022

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

Video Prediction Recalling Long-term Motion Context via Memory Alignment Learning

Hakgu Kim

Our work addresses long-term motion context issues for predicting future frames. To predict the future precisely, it is required to capture which long-term motion context (e.g., walking or running) the input motion (e.g., leg movement) belongs to. The bottlenecks arising when dealing with the long-term motion context are: (i) how to predict the long-term motion context naturally matching input sequences with limited dynamics, (ii) how to predict the long-term motion context with high-dimensionality (e.g., complex motion). To address the issues, we propose novel motion context-aware video prediction. To solve the bottleneck (i), we introduce a long-term motion context memory (LMC-Memory) with memory alignment learning. The proposed memory alignment learning enables to store long-term motion contexts into the memory and to match them with sequences including limited dynamics. As a result, the long-term context can be recalled from the limited input sequence. In addition, to resolve the bottleneck (ii), we propose memory query decomposition to store local motion context (i.e., low-dimensional dynamics) and recall the suitable local context for each local part of the input individually. It enables to boost the alignment effects of the memory. Experimental results show that the proposed method outperforms other sophisticated RNN-based methods, especially in long-term condition. Further, we validate the effectiveness of the proposed network designs by conducting ablation studies and memory feature analysis. The source code of this work is available(dagger).

IEEE COMPUTER SOC2021