Memory In Human Brain Research Articles

Signatures of Electrical Stimulation Driven Network Interactions in the Human Limbic System.

Stimulation-evoked signals are starting to be used as biomarkers to indicate the state and health of brain networks. The human limbic network, often targeted for brain stimulation therapy, is involved in emotion and memory processing. Previous anatomic, neurophysiological, and functional studies suggest distinct subsystems within the limbic network (Rolls, 2015). Studies using intracranial electrical stimulation, however, have emphasized the similarities of the evoked waveforms across the limbic network. We test whether these subsystems have distinct stimulation-driven signatures. In eight patients (four male, four female) with drug-resistant epilepsy, we stimulated the limbic system with single-pulse electrical stimulation. Reliable corticocortical evoked potentials (CCEPs) were measured between hippocampus and the posterior cingulate cortex (PCC) and between the amygdala and the anterior cingulate cortex (ACC). However, the CCEP waveform in the PCC after hippocampal stimulation showed a unique and reliable morphology, which we term the "limbic Hippocampus-Anterior nucleus of the thalamus-Posterior cingulate, HAP-wave." This limbic HAP-wave was visually distinct and separately decoded from the CCEP waveform in ACC after amygdala stimulation. Diffusion MRI data show that the measured end points in the PCC overlap with the end points of the parolfactory cingulum bundle rather than the parahippocampal cingulum, suggesting that the limbic HAP-wave may travel through fornix, mammillary bodies, and the anterior nucleus of the thalamus (ANT). This was further confirmed by stimulating the ANT, which evoked the same limbic HAP-wave but with an earlier latency. Limbic subsystems have unique stimulation-evoked signatures that may be used in the future to help network pathology diagnosis.SIGNIFICANCE STATEMENT The limbic system is often compromised in diverse clinical conditions, such as epilepsy or Alzheimer's disease, and characterizing its typical circuit responses may provide diagnostic insight. Stimulation-evoked waveforms have been used in the motor system to diagnose circuit pathology. We translate this framework to limbic subsystems using human intracranial stereo EEG (sEEG) recordings that measure deeper brain areas. Our sEEG recordings describe a stimulation-evoked waveform characteristic to the memory and spatial subsystem of the limbic network that we term the "limbic HAP-wave." The limbic HAP-wave follows anatomic white matter pathways from hippocampus to thalamus to the posterior cingulum and shows promise as a distinct biomarker of signaling in the human brain memory and spatial limbic network.

Production Data Management of Smart Farming Based on Shili Theory

The development of smart farming comes with a lot of data problems. Studies have shown this is due to insufficient cognition of the structural relationship between data and events. Shili Theory is an attractive concept. To embed intelligent agricultural technology in events and the natural environment, especially to unify and standardize agricultural production data, firstly, this paper has defined the concept of Shili Theory which researches the natural regularity of the event by Shili Mirrored Structure. Secondly, this paper has proposed a Shili Mirrored Structure based on the technology development path (from the human brain memory mechanism to the information storage mechanism to intelligent technology). Finally, the structure has been applied to develop an intelligent system of agricultural production data management. In rice production of Jilin Province, it forms the event chain of the whole plant 5T (seed, seeding, paddy shoot, grain, product period operation) and grain period 5T (harvesting, field stacking, drying, warehousing, storing). The system application shows that this management structure can reduce data flow, improve data utilization, and enhance the correlation between data and events. It can realize the quality improvement of the agricultural production process, especially revealing the 8.83% significant latent loss in rice harvest.

Learning Factors Knowledge Tracing Model Based on Dynamic Cognitive Diagnosis

This paper mainly studies the influence of dynamic cognitive diagnosis on personalized learning. Considering the influence of knowledge correlation factors and human brain memory factors on learning activities, a knowledge tracing model integrating learning factors is proposed. Firstly, based on the exercise-knowledge association information, the model maps learners and exercises to the knowledge space with clear meaning. Then, the evolution process of learners’ knowledge learning is quantitatively modeled in the knowledge space by integrating the classical learning curve and forgetting curve theory of pedagogy. On the other hand, considering the influence of topic semantics in the learning process, a knowledge tracing model integrating topic semantics is proposed in this paper. Firstly, the model designs a dynamic enhanced memory network to store the common information of knowledge and describes the learners’ dynamic mastery of knowledge. Secondly, the depth representation method of exercise resources is proposed to mine the text personality information and integrate it into the process of learners’ knowledge change modeling. Through a large number of experiments on exercise records, it is verified that the proposed model has accurate prediction performance and knowledge tracing interpretability.

Synaptic metaplasticity emulation in a freestanding oxide-based neuromorphic transistor with dual in-plane gates

Synaptic plasticity is a basic characteristic of synapses and plays an important role in the computation, learning and memory of human brain. Metaplasticity is a higher-order form of synaptic plasticity, which regulates the ability of synapses to generate synaptic plasticity and has a great regulating effect on later learning, memory and coping behaviors. At present, there are rarely reports on the emulation of synaptic metaplasticity in synaptic transistor. In this article, flexible dual-gate indium-zinc-oxide neuromorphic devices on freestanding solid-state proton conducting chitosan electrolyte membrane are designed for metaplasticity emulation. The key synaptic plasticity functions including excitatory postsynaptic current, synaptic paired-pulse response and synaptic pulse train response can be effectively regulated by the priming pulse stimuli. Besides, configurable synaptic depression and synaptic potentiation effect can be realized in such device. These results can expand the potential applications of the multi-terminal electrolyte-gated oxide transistors for flexible dynamic neuromorphic platforms.

A collaborative filtering recommendation system with dynamic time decay

The collaborative filtering (CF) technique has been widely utilized in recommendation systems due to the precise prediction of users' interests. Most prior CF methods adapted overall ratings to make predictions by collecting preference information from other users. However, in real applications, people’s preferences usually vary with time; the traditional CF could not properly reveal the change in users’ interests. In this paper, we propose a novel CF-based recommendation, dynamic decay collaborative filtering (DDCF), which captures the preference variations of users and includes the concept of dynamic time decay. We extend the idea of human brain memory to specify the level of a user’s interests (i.e., instantaneous, short-term, or long-term). According to different interest levels, DDCF dynamically tunes the decay function based on users’ behaviors. The experimental results show that DDCF with the integration of the dynamic decay concept performs better than traditional CF. In addition, we conduct experiments on real-world datasets to demonstrate the practicability of the proposed DDCF.

Implementation of PPI with Nano Amorphous Oxide Semiconductor Devices for Medical Applications.

BackgroundElectronic devices which mimic the functionality of biological synapses are a large step to replicate the human brain for neuromorphic computing and for numerous medical research investigations. One of the representative synaptic behaviors is paired-pulse facilitation (PPF). It has been widely investigated because it is regarded to be related to biological memory. However, plasticity behavior is only part of the human brain memory behavior.MethodsHere, we present a phenomenon which is opposite to PPF, i.e., paired-pulse inhibition (PPI), in nano oxide devices for the first time. The research here suggests that rather than being enhanced, the phenomena of memory loss would also be possessed by such electronic devices. The device physics mechanism behind memory loss behavior was investigated. This mechanism is sustained by historical memory and degradation manufactured by device trauma to regulate characteristically stimulated origins of artificial transmission behaviors.ResultsUnder the trauma of a memory device, both the signal amplitude and signal time stimulated by a pulse are lower than the first signal stimulated by a previous pulse in the PPF, representing a new scenario in the struggle for memory. In this way, more typical human brain behaviors could be simulated, including the effect of age on latency and error generation, cerebellar infarct, trauma and memory loss pharmacological actions (such as those caused by hyoscines and nitrazepam).ConclusionThus, this study developed a new approach for implementing the manner in which the brain works in semiconductor devices for improving medical research.

Synchronous dynamics in neural system coupled with memristive synapse

To study the collective behavior and the regulation mechanism of memristor, the Hindmarsh–Rose neuron cells are selected as the units to construct a coupled system by using the cubic flux-controlled memristor as a connecting synapses, both the firing mode and synchronous behavior in this coupled system are investigated. In this paper, a coefficient is introduced to describe the effect of electromagnetic induction of membrane potential, and the weights of synaptic connection between cells are selected as an adjustable parameter. We have found that, on the one hand, for the certain external current, the firing pattern of neurons could maintain normal state induced by proper electromagnetic induction, and the transition between different firing states could also be observed. On the other hand, both the synchronous effects could be effectively enhanced and the domain of synchronous parameters could be also expanded by tuning the electromagnetic parameters. Particularly, when the coupled system is in the synchronization state, one can see that the firing behavior of neurons will simultaneously change with the varying of the memristance. The similar phenomenon could also be found by introducing the external stimulus signal with a certain frequency. It means that the plasticity of biological synapses could be effectively mimicked by using the memristive synapse and thus the effective memory function of the memristor to the external stimulus signal may be realized. Above results may not only provide some useful clues for understanding the dynamic behavior of neural system coupled with memristive synapses, but also afford us some inspiration to simulate human brain memory, forgetting or some other functions by using the memristor neural network in the future.

The effect of Quran murottal’s audio on short term memory

In all activities, people need memory as information storage. A good memory will make it easier for people to remember so they can do their job well. One of the things that affect memory is the sound of music. Music is commonly used as a stress treatment or as one of the stimuli of human brain memory. Not only classical music can be one of the stimuli of brain memory, but listening to the Quran can also be one of the brain memory stimulants. Several studies have proven that by listening to the Qur'an, the ability of the brain can be increased significantly. In this study used the variables pre and posted Qur'an listening to 50 respondents. Respondents consisted of 26 men and 24 women aged 19-22 years old. All respondents will get two treatments. The first treatment is without listening to murottal and the second treatment is done after listening to the Quran. All respondents will do memory test using the program running memory in software design tools. The results showed that the average ability of short-term memory without listening to murottal 11.8 and the average ability of short-term memory by listening to murottal is 12.62. Based on these results it is known that by listening to the Quran provides a significant increase in the ability of the brain.

To the question about brain memory nature

The study of alcoholic influence, joint alcoholic and mental influence as well as the one of further exposition on structural and informational properties of water was carried out with the method of laser emission scattering. Experimental findings imply that adding alcohol to water results in formation of coarser cluster structure compared to the initial one. Degree and character of such restructuring are defined by duration of alcohol influence, its dosage, and the initial water structure. The mechanism of memory deterioration of water with alcohol is defined. It lies in essential abatement of its sensitivity to the influencing information compared to the one of the blank sample. The loss of memory in water containing alcohol is determined by high mechanical durability of combined clusters that consist of molecules of water and pure alcohol formed by alcohol influence which hinders their spin restructuring under informational influence. Character of changes in water structure that contains alcohol corresponds to abatement of human brain memory under consumption of alcohol and testifies to veracious conclusion of torsional nature of brain memory. It also confirms that physical carrier of the latter is spin configurations of tetravalent atoms of oxygen in water clusters that neurons contain.

How Does the Sparse Memory "Engram" Neurons Encode the Memory of a Spatial-Temporal Event?

Episodic memory in human brain is not a fixed 2-D picture but a highly dynamic movie serial, integrating information at both the temporal and the spatial domains. Recent studies in neuroscience reveal that memory storage and recall are closely related to the activities in discrete memory engram (trace) neurons within the dentate gyrus region of hippocampus and the layer 2/3 of neocortex. More strikingly, optogenetic reactivation of those memory trace neurons is able to trigger the recall of naturally encoded memory. It is still unknown how the discrete memory traces encode and reactivate the memory. Considering a particular memory normally represents a natural event, which consists of information at both the temporal and spatial domains, it is unknown how the discrete trace neurons could reconstitute such enriched information in the brain. Furthermore, as the optogenetic-stimuli induced recall of memory did not depend on firing pattern of the memory traces, it is most likely that the spatial activation pattern, but not the temporal activation pattern of the discrete memory trace neurons encodes the memory in the brain. How does the neural circuit convert the activities in the spatial domain into the temporal domain to reconstitute memory of a natural event? By reviewing the literature, here we present how the memory engram (trace) neurons are selected and consolidated in the brain. Then, we will discuss the main challenges in the memory trace theory. In the end, we will provide a plausible model of memory trace cell network, underlying the conversion of neural activities between the spatial domain and the temporal domain. We will also discuss on how the activation of sparse memory trace neurons might trigger the replay of neural activities in specific temporal patterns.

A biologically inspired model mimicking the memory and two distinct pathways of face perception

In this paper, we propose a face perception model to mimic the biological mechanism of face perception and memory in human brain. We are mainly inspired by the fact that there are two functionally and neurologically distinct pathways after the early face perception and they both interact to process the changeable features of faces. Accordingly, our model consists of three perception parts, facial structure perception, facial expression perception and facial identity perception, which are all component-based. The structure perception has a feed-forward projection to the expression and identity perception, while the expression affects the identity perception with a modulation process. We embody the three parts referring to three bio-inspired computational models. For the facial structure perception, we utilize a cascaded-CNN (convolutional neural networks) approach to estimate the center locations of key facial components. For the facial expression perception, we propose a novel approach which exploits convolutional deep belief networks (CDBN) to spontaneously locate the places containing the most discriminative information and synchronously complete the feature learning and feature selection. For the facial identity perception, we propose an approach which adopts the hierarchical max-pooling (HMAX) model to encode notable characteristics of facial components and utilizes a new memory formation integrating the preliminary decision, expression modulation and final decision process. We evaluate our model through a series of experiments and the experimental results demonstrate its rationality and effectiveness.

Twin Neurons for Efficient Real-World Data Distribution in Networks of Neural Cliques: Applications in Power Management in Electronic Circuits.

Associative memories are data structures that allow retrieval of previously stored messages given part of their content. They, thus, behave similarly to the human brain's memory that is capable, for instance, of retrieving the end of a song, given its beginning. Among different families of associative memories, sparse ones are known to provide the best efficiency (ratio of the number of bits stored to that of the bits used). Recently, a new family of sparse associative memories achieving almost optimal efficiency has been proposed. Their structure, relying on binary connections and neurons, induces a direct mapping between input messages and stored patterns. Nevertheless, it is well known that nonuniformity of the stored messages can lead to a dramatic decrease in performance. In this paper, we show the impact of nonuniformity on the performance of this recent model, and we exploit the structure of the model to improve its performance in practical applications, where data are not necessarily uniform. In order to approach the performance of networks with uniformly distributed messages presented in theoretical studies, twin neurons are introduced. To assess the adapted model, twin neurons are used with the real-world data to optimize power consumption of electronic circuits in practical test cases.

Huffman Coding for Storing Non-Uniformly Distributed Messages in Networks of Neural Cliques

Associative memories are data structures that allow retrieval of previously stored messages given part of their content. They thus behave similarly to human brain's memory that is capable for instance of retrieving the end of a song given its beginning. Among different families of associative memories, sparse ones are known to provide the best efficiency (ratio of the number of bits stored to that of bits used). Nevertheless, it is well known that non-uniformity of the stored messages can lead to dramatic decrease in performance. Recently, a new family of sparse associative memories achieving almost-optimal efficiency has been proposed. Their structure induces a direct mapping between input messages and stored patterns. In this work, we show the impact of non-uniformity on the performance of this recent model and we exploit the structure of the model to introduce several strategies to allow for efficient storage of non-uniform messages. We show that a technique based on Huffman coding is the most efficient.

An Algorithm of Auto-Update Threshold for Singularity Analysis of Pipeline Pressure

A precise auto-update threshold algorithm (AUTA) which imitates the short-term memory of human brain is proposed to search singularities in pipeline pressure signal. According to the characteristics of the pressure signal, the pressure can be divided into two states known as nonsteady state and steady state. The AUTA can distinguish these two states and then choose corresponding method to calculate the dynamic thresholds of pressure variation in real time. Then, the parameters of AUTA are analyzed to determine their values or ranges. Finally, in the simulations to the actual pressure signal from oil pipelines, we verified the effectiveness of AUTA in estimating the dynamic threshold value of pressure.

Memory-Based Multiagent Coevolution Modeling for Robust Moving Object Tracking

The three-stage human brain memory model is incorporated into a multiagent coevolutionary process for finding the best match of the appearance of an object, and a memory-based multiagent coevolution algorithm for robust tracking the moving objects is presented in this paper. Each agent can remember, retrieve, or forget the appearance of the object through its own memory system by its own experience. A number of such memory-based agents are randomly distributed nearby the located object region and then mapped onto a 2D lattice-like environment for predicting the new location of the object by their coevolutionary behaviors, such as competition, recombination, and migration. Experimental results show that the proposed method can deal with large appearance changes and heavy occlusions when tracking a moving object. It can locate the correct object after the appearance changed or the occlusion recovered and outperforms the traditional particle filter-based tracking methods.

A study on striatum involved in associative learning memory by functional magnetic resonance imaging technique and clinical observation

Objective To examine whether the striatum are involved in associative learning and memory cognitive function of human brain by functional magnetic resonance imaging (fMRI) technique and clinical observation.Methods 14 right-handed normal volunteers participated in a test of paired-word associative learning and memory ,while the fMRI data were recorded.Control tasks were performed for the block-design.SPM 99 was used to analyze the data and to obtain the activated brain regions.14 patients with striatum damages and 14 matched normal volunteers participated in a test of paired-word associative learning and memory.Results When the threshold was set as P＜0.005 ,using a one-sample t -test ,the left occipital lobe (t=13.87)were activated remarkably during the encoding process of the paired-word associative learning and memory task.However,during the retrieval process,the prominently activated brain regions were the left parietal lobe (t=8.73).The striatum (t=7.02/7.47) were also obviously activated during these two stages.The scores of the paired-word associative learning and memory test on the patients with striatum damages and the healthy volunteers were respectively 11.53±1.39 and 19.18±2.89 ,the difference was significant (P ＜ 0.001).Conclusion The results of this study reveal that the subcortical structures such as the striatum as well as the cerebral cortex are involved in the associative learning and memory in human brain ,and the damages of striatum would show the disorder of associative learning and memory cognitive function. Key words: Functional magnetic resonance imaging (fMRI) ; Associative learning and memory; Cognitive function; Striatum; Human brain

Brain text processing model: A new approach based on conceptual dependency stories

Event Abstract Back to Event Brain text processing model: A new approach based on conceptual dependency stories Petr Kratochvil1* and Jana Kleckova1 1 University of West Bohemia, Department of Computer Science and Engineering, Czechia One of the possibilities how to model text processing by the human brain is to model story processing, whereas these stories include verbal communication. Interesting model of story structure and format was introduced by Roger C. Schank [1]. This model and similar ones are described also in [2]. This problem is connected with "Artificial Intelligence: Conceptual Dependency".The aim of this article is to find and test a new experimental text processing in human brain model. There is a program that allows teaching computer simple stories. These stories consist of sequence of events in virtual world and of text communication. In this virtual world there are intelligent software agents controlled by program and lifeless objects. Events and virtual world are added for better simulation of word semantics. Following problems are partially solved: data model simulating human brain memory, saving stories from scripts to data model, imitation of similar stories and data model and stories visualization.Stories are stored in semantic network. This network is composed of nodes and oriented edges with weights. The type of nodes can be special or standard. Special nodes are predefined at the beginning of the program runtime and represent letters and attributes. Standard nodes are created by story learning and each such node is defined by another nodes. Whole network is divided into a few parts where each part is divided into layouts. Whole network represents neural network of memory in human brain, nodes represent neurons, edges represent synapses and the division of the network represent division of a brain memory.The special feature of this solution is used. The oriented edges only connect nodes and don't carry semantic information as in the Shank's model and as in the many others models. The weight of each edge will be just one scalar value. The meaning of each node is defined only by edges connecting this node to other nodes and by attributes as if a node is one of a sequence or trustworthiness, etc. The meaning is defined by a connection with special nodes. The next special feature is universality. Special nodes can represent letters as well as frequences or a position of pixel in pixel matrix or a position and type of chess figure on chessboard. The program at this time can process events, changes of internal states and also the text communication between agents in the simple virtual world. Later the program will be extended to support fuzzy logic, trustworthiness, language discrimination, etc.Test of the quality of story processing is as follows: at the beginning the agent will be trained with a few stories and in next step we will test on how many stories the agent suggests right finishing of story chopped in the middle. At this time methods for story finishing are in the development state.