Learning and Memory (Neurosciences)

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RECEPTORS INVOLVED IN LEARNING AND MEMORY PROCESS: AN OVERVIEW

RECEPTORS INVOLVED IN LEARNING AND MEMORY PROCESS: AN OVERVIEW

Serotonergic projections modulate various aspects of learning and memory [14]. It is hypothesized that serotonergic neurotransmission on the whole may not be responsible for changes in memory, but rather disturbances in the functional balance between the components of this brain neurotransmitter system with other neurotransmitter causes changes in learning and memory [15]. It is likely that some 5-HT receptors act in opposition to other 5-HT receptors and/or neurotransmitter systems during learning. Every 5- HT receptor identified until now has been localized in the hippocampus, amygdale and cortex areas of brain which are involved in learning and memory. Multiple 5 HT serotonin receptor subtypes are reported to be involved during sensitization process in memory encoding in aplysia. 5 HT act within the sensory neurons through cAMP-PKA (cAMP protein kinase A) pathway, and also activates a variety of other protein kinases like extracellular signal-regulated kinases, protein kinase C and tyrosine kinases [16].
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Associative learning and memory duration of Trichogramma brassicae

Associative learning and memory duration of Trichogramma brassicae

Memory duration and learning are two entirely interdependent components. Memory acts as a safe in which learned information is stored. It will be affected by type and number of conditioning events and experiences. Memory type is classified into several forms based on the time that learned information can be recalled in the animal kingdom. After one single training, short-term memory (STM, also called anaesthesia-sensitive memory (ASM)) will be obtained. STM lasts only a few hours, is unstable, and will quickly be disrupted by other factors such as an application of anaesthesia shortly after learning (44). More durable memory forms of stabilized memory which are resistant to anaesthesia are generally organized after several training events in separated times (spaced training). After several consecutive experiences, LTM (long term memory) must
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Improvement of Learning and Memory of Mice by Plumbagin

Improvement of Learning and Memory of Mice by Plumbagin

Abstract: Roots of Plumbago zeylanica have been reported to improve learning and memory of mice, but the effect of plumbagin on learning and memory of mice and the possible mechanisms for its effect on memory have not been explored till date. So the present study was designed to evaluate the effect of plumbagin on learning and memory of Swiss young male albino mice. Plumbagin (4, 8, 16 mg/kg, p.o. ) and physostigmine (0.1 mg/kg, i.p. ) per se were administered for 15 successive days to separate groups of mice. Behavioral parameters of learning and memory were recorded using Morris water maze. Acetylcholinesterase activity was estimated in brain of mice. Effect of plumbagin on scopolamine (0.4 mg/kg, i.p. ) and diazepam (1 mg/kg, i.p. )-induced amnesia was also investigated. Locomotor activities of mice were also recorded. Plumbagin (8 and 16 mg/kg) and physostigmine significantly improved learning and memory of mice, as indicated by decrease in escape latency during training and increase in time spent in target quadrant of Morris water maze during retrieval. The drugs did not show any significant effect on locomotor activities of mice. Memory improving activity of plumbagin (16 mg/kg) was equivalent to physostigmine. Plumbagin significantly reversed scopolamine- and diazepam- induced amnesia in mice. Plumbagin and physostigmine also significantly reduced brain acetylcholinesterase activity of mice. In conclusion, plumbagin significantly improved memory of mice possibly through inhibition of brain acetylcholinesterase activity and through involvement of GABA-benzodiazepine pathway. Thus, plumbagin may be explored further for management of cognitive dysfunction.
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P300 correlates with learning & memory abilities and fluid intelligence

P300 correlates with learning & memory abilities and fluid intelligence

to assess fluid intelligence; (2) learning and memory tasks to assess learning ability and memory recall; and (3) the visual oddball task to assess brain-evoked potentials. On the basis of RAPM scores, the subjects were divided into two groups, (i) High ability (HA) group―subjects scored above the median; and (ii) Low ability (LA) group―sub- jects scored equaled or scored below the median. Such division of subjects on the median value of intelligence test was previously reported by Wronka et al. [7]. ERPs were extracted from the EEG recordings of thirty-four subjects as recorded while they undertook a visual oddball task which presented Standard (box) and Target (sphere) stim- uli. Each subject’s target and standard stimuli responses were averaged individually from which a collective re- sponse average was then calculated. The focus of this paper is on P3 component only. However, to investigate whether the differences between the two groups are spe- cific to P3 only, the P200 (P2) component was also ex- tracted and considered for analysis. The amplitude and latency of both P2 & P3 components were performed on the difference between target and standard responses from 20 electrodes (19 electrodes based on 10–20 system with additional Oz electrode). The difference of target and standard would show more strongly the P3 response at parietal regions and would cancel the ERP elicited by tar- get or standard stimuli at frontal and central regions. In addition, the grand averaged target and standard wave- forms as well as their respective difference of both groups with topographical variations from frontal regions to fronto-central, centro-parietal, and occipital regions were also observed and recorded. These variations in the P3 component reflected and described individual differences in learning and memory recall as well as fluid intelligence. This study’s contribution indicates that the P3 component may be a reliable adjunct to standard psychometric tests which are used to predict a person’s ability to learn new knowledge and recall memories.
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Pharmacological effects of cannabinoids on learning and memory in Lymnaea

Pharmacological effects of cannabinoids on learning and memory in Lymnaea

We know in Lymnaea that different stressors tweak the ability to learn and form LTM (Lukowiak et al., 2014a). Some stressors (e.g. predator detection) lead to enhanced memory formation (Orr and Lukowiak, 2008), whereas other stressors (e.g. crowding) lead to suppression of memory formation (de Caigny and Lukowiak, 2008). In addition, a combination of stressors (e.g. crowding and low environmental calcium) leads to complete obstruction of learning and all forms of memory (i.e. short, intermediate and long-term memory) (Dalesman et al., 2011). It is unclear at present how the stressors actually cause memory formation to be enhanced or suppressed. However, at least for enhanced memory formation following exposure to certain stressors (e.g. predator detection) and bioactive agents (e.g. methamphetamine), epigenetic changes (i.e. DNA methylation) have been shown to play a necessary role (Lukowiak et al., 2014b). From the data obtained here, we are confident that CBrs play a key role in both modulating learning and the subsequent formation of memory as the result of snails experiencing different stressors.
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Natural Remedies for Improving Learning and Memory-Review

Natural Remedies for Improving Learning and Memory-Review

A large number of chemicals have been isolated from T. cordifolia, belonging to different classes such as alkaloids, diterpenoid lactones, glycosides, steroids, sesquiterpenoid, phenolics, aliphatic compounds and polysaccharides. Leaves of this plant are rich in protein (11.2%), calcium and phosphorus 29 . Four new clerodane furano diterpene glucosides (amritosides A, B, C and D) have been isolated as their acetates from stems. Tinospora cordifolia has been extensively studied and reported to have potent immunostimulant action. T. cordifolia is claimed to be useful in treating leprosy, fever, asthma, anorexia, jaundice, gout, skin infections, diabetes, chronic diarrhea, dysentery, etc. 30 . Alcoholic and aqueous extracts of Tinospora cordifolia have been shown to produce a decrease in learning scores in Hebb William maze and retention memory, indicating enhancement of learning and memory 31 .
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Effects of Citalopram on Learning and Memory in the Mouse and Rat

Effects of Citalopram on Learning and Memory in the Mouse and Rat

Data on the effects of serotonin reuptake inhibitors on learning and memory processes are not consistent. In the present study, the effects of citalopram, a very potent and completely selective inhibitor of the serotonin reuptake on spatial discrimination in the T-maze and Morris water maze, were assessed in mice and/or rats. Animals received different doses of citalopram (1, 2, 4, 8 or 16 mg/kg, i.p.) or its vehicle (saline) 30 min before training each day. The results showed no significant effects of citalopram on T-maze discrimination task in mice and rats. However, there was dose-dependent increases in latencies to find the invisible platform and traveled distances in citalopram received groups compared to the control group with the peak effect at doses of 4 and 8 mg/kg in Morris task. Therefore, it appears that citalopram can cause learning deficits in complex spatial tasks. Iran. Biomed. J. 4: 21-29, 2000
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Effect of Ganoderma lucidum on memory and learning in mice

Effect of Ganoderma lucidum on memory and learning in mice

In Morris water maze, a decrease in escape latency (EL) during training and an increase in time spent in the target quadrant during retrieval indicated improvement of learning and memory respectively; and vice versa. Ganoderma lucidum did not produce any significant al- teration in locomotor functions of the animals when compared with distilled water treated control group, hence, the drug did not show any motor effects. There- fore, memory boosting activity of Ganoderma lucidum is specific and not false positive.

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Holographic Generative Memory: Neurally Inspired One-Shot Learning with Memory Augmented Neural Networks

Holographic Generative Memory: Neurally Inspired One-Shot Learning with Memory Augmented Neural Networks

One of the recurring themes in OSL literature is learning about a new object class by building on the knowledge of previously learned objects. With deep learning this often takes the form of using pre-trained networks to extract features (e.g., transfer learning). Using a network that was pre-trained on a large dataset allows us to gather more useful features from a smaller but related dataset (e.g., both sets are natural images). We are building on the pre-existing knowledge in the network to compose representations of new objects, but only in a general sense. The network does not make use of abstract reasoning, analogy, or any of the other tools typically associated with human memory. It only has a set of parameters that have been successfully biased toward a certain type of data via lengthy training.
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Learning and Knowledge Transfer with Memory Networks for Machine Comprehension

Learning and Knowledge Transfer with Memory Networks for Machine Comprehension

We attempt to improve the training procedure for Memory Networks in order to increase the perfor- mance for machine comprehension by QA with small scale datasets. Firstly, we introduce an im- proved training procedure for memory networks using curriculum learning which is termed as Cur- riculum Inspired Training (CIT) and offer details about this in Section 5.1. Thereafter, Section 5.2 explains joint-training method for knowledge transfer from an abundantly labelled dataset to an- other dataset with limited label information . 5.1 CIT: Curriculum Inspired Training Curriculum learning makes use of the fact that model performance can be significantly improved if the training samples are not presented randomly but in such a way so as to make the learning task gradually more difficult by presenting examples in an easy to hard ordering (Bengio et al., 2009). Such a training procedure allows the learner to waste less time with noisy or hard to predict data when the model is not ready to incorporate such samples. However, what remains unanswered and is left as a matter of further exploration is how to devise an effective strategy for a given task?
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Learning to Abstract for Memory augmented Conversational Response Generation

Learning to Abstract for Memory augmented Conversational Response Generation

Parameters of the query encoder and response en- coder are not shared; the two MLP components in M-Seq2Seq branch and CAE branch also do not share parameters. The dimension of all hidden vectors and embeddings are 620 and the batch size is 64. We employ the Adam optimizer (Kingma and Ba, 2014) with the initial learning rate 0.0001 and gradient clipping 5. For generation, we apply a beam search with the size of 10. The memory size K is 1000, and the loss factor λ is 0.1. We implement our model on PyTorch. The implemen- tation details can be found in our codes 3 .

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The behavioural effects of intracerebral cycloheximide on memory and learning in the chicken

The behavioural effects of intracerebral cycloheximide on memory and learning in the chicken

trained CXM-treated group may reflect inhibition of memory formation. In order to test the reality of the supposed CXM-induced amnesia, birds were deprived for 15 h (cf. 3h) before the retention test. Under these conditions the untrained CXM-treated birds were similar to the untrained saline-treated birds, even though the intra-group improvement of the untrained CXM-treated birds just failed to be significant. Increasing the deprivation level overcame most of the CXM-induced changes apparent in the behaviour of the untrained CXM-treated group after 3 h deprivation. Given that the non-specific effects of CXM are not of significance at this level of deprivation, more meaningful inferences can be made from the performance of the trained CXM-treated group about the effect of CXM on the formation of memory. The trained CXM-treated birds performed as if they remembered as well as the trained saline- treated group. This effect was not due simply to rapid re-learning after increasing the level of deprivation. If this had been the case the untrained birds would have demonstrated the same behaviour.
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Do Not Forget: Full Memory in Memory Based Learning of Word Pronunciation

Do Not Forget: Full Memory in Memory Based Learning of Word Pronunciation

The experiments in this paper show t h a t op- timi~tion of memory use in memory-based learning while preserving generalisation accuracy can only be performed by i replacing instance tok[r]

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Spatial learning and memory in the tortoise (Geochelone carbonaria)

Spatial learning and memory in the tortoise (Geochelone carbonaria)

A single individual of the species Geochelone carbonaria was trained in an eight-arm radial maze, with the apparatus and general procedures modelled on those used to demonstrate spatial learning in rats. The tortoise learned to perform reliably above chance, preferentially choosing baited arms, rather than returning to arms previously visited on a trial. Test sessions that examined control by olfactory cues revealed that these did not affect

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Learning Memory and Thinking1

Learning Memory and Thinking1

Unconditioned Stimulus- stimulus that causes an automatic response, not learned.?. Unconditioned Response- automatic..[r]

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Learning memory processes in the classroom

Learning memory processes in the classroom

The value af'a model of memory, such as that presented here, to the study of memory processes and strategies used by pupils in remembering written classroom material is:.. it points to t[r]

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A Computational Model of Memory, Attention, and Word Learning

A Computational Model of Memory, Attention, and Word Learning

Much remains to be investigated in our model. For example, most human experiments examine the effect of frequency of presentations of target items. Also, the range of retention intervals that has been studied is greater than what we have considered here. In the future, we plan to study the effect of these two parameters. In addition, with our current model, the amount of time an item is presented to the learner does not play a role. We can also re- formulate our alignment mechanism to incorporate a notion of the amount of time to consider an item to be learned. Another interesting future direction, especially in the context of word learning, is to de- velop a more complete attentional mechanism, that considers different parameters such as social cues and linguistic cues. Finally, we will study the role of forgetting and attention in modelling other rele- vant experimental data (e.g., Kachergis et al., 2009; Vlach and Sandhofer, 2010).
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Memory-Augmented Temporal Dynamic Learning for Action Recognition

Memory-Augmented Temporal Dynamic Learning for Action Recognition

Human actions captured in video sequences contain two cru- cial factors for action recognition, i.e., visual appearance and motion dynamics. To model these two aspects, Convo- lutional and Recurrent Neural Networks (CNNs and RNNs) are adopted in most existing successful methods for recog- nizing actions. However, CNN based methods are limited in modeling long-term motion dynamics. RNNs are able to learn temporal motion dynamics but lack effective ways to tackle unsteady dynamics in long-duration motion. In this work, we propose a memory-augmented temporal dynamic learning network, which learns to write the most evident information into an external memory module and ignore irrelevant ones. In particular, we present a differential memory controller to make a discrete decision on whether the external memory module should be updated with current feature. The discrete memory controller takes in the memory history, context em- bedding and current feature as inputs and controls informa- tion flow into the external memory module. Additionally, we train this discrete memory controller using straight-through estimator. We evaluate this end-to-end system on benchmark datasets (UCF101 and HMDB51) of human action recogni- tion. The experimental results show consistent improvements on both datasets over prior works and our baselines.
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The calcium/calmodulin regulated kinase cascade in learning and memory

The calcium/calmodulin regulated kinase cascade in learning and memory

It is important to note that the distinction between STM and LTM derived from the latter studies differs from the neuropsychological concept described above. Here, the key distinction between STM and LTM is that new memories are initially in a labile form for a short time, after which the memory trace is transferred into LTM stabilized by a change in synaptic structure. STM can last for seconds to hours, is sensitive to disruption and does not require protein synthesis or transcription. LTM lasts for days or longer, is stable and does require synthesis o f new proteins and mRNA. This concept could best be described as a cellu la r c o n so lid a tio n theory (fig. 1-1). In the neuropsychological concept o f memory consolidation STM and LTM refer to how brain systems function, not to sequential events at individual synapses. Because the present study is concerned with molecular mechanisms underlying LTM formation, LTM will refer to the protein synthesis dependent stage o f memory formation, whereas STM will describe the protein synthesis independent stage. LTM will be categorized according to the neural substrates involved, that is the hippocampal formation, the amygdala, or other brain regions.
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MoL 2017 22: 
  Turing Learning with Nash Memory

MoL 2017 22: Turing Learning with Nash Memory

This thesis extended Parallel Nash memory and introduced Universal Nash Memory (UNM). We studied the integration of UNM with Turing Learning for efficient reverse engineering of agent behaviors. As a first step, we tested the correctness of our first implementation on the Intransitive Number Game (ING). Then we presented TuringLearner, the first Turing Learning platform together with test cases of Percentage Number Game (PNG) and Real Number Game (RNG). Finally, we implemented the aggregation game together with its two variants, Backwards Walking Game (BWG) and Safe Walking Game (SWG). We investigated the balance of strategies in UNM and studied how UNM impact Turing Learning when facing changes and uncertainty in interaction. Our experiments indicate that this new approach can reduce the computation time to 35.4% and results in faster convergence for the aggregation game. Discussion and possible future work were also presented.
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