Simulation of dopamine modulation-based memory model
Introduction
Memory is a psychological process of accumulating and preserving individual experiences in the brain. From the perspective of information processing, it is a process for the human brain to encode, store and retrieve the input information. Memory can explain a lot of problems, such as psychological problems of humans and patients with brain injury. And the capacity of memory plays an important role in the process of performing complex cognitive activities. People can use memory to store the information about the environment. This information is eventually used by human, so it is of great significance to study memory. According to the length of retention time, memory can be divided into transient, short-term and long-term. Transient memory refers to a memory in an extremely short time after the perception (such as one second or so), short-term memory refers to a memory in a relatively short time (less than one min) and long-term memory refers to a memory in a longer time (more than one min) [1], [2], [3]. Short-term and long-term memories are inter-related rather than mutually exclusive [4], [5]. Transformation of short-term memory to long-term memory relies on the transcription of cAMP-response element binding protein (CREB), which is a key regulatory molecule in the formation of long-term memory [6], [7]. Long-term potentiation (LTP) is also essential for the formation of memory. Formation and maintenance of LTP is a neural mechanism generated by pre-synaptic and post-synaptic combined effects, of which post-synaptic mechanism is dominant. The post-synaptic mechanism of LTP formation is closely related to the intracellular cascade after the activation of N-methyl-D-aspartic acid receptor (NMDA) [8], [9].
Several models have been proposed to describe the short-term memory(working memory) from different perspectives. For example, Reilly et al. [10] designed a working memory calculation model, which showed that the prefrontal cortex and basal ganglion were able to interact to complete a complex time expanding task, which was meant to maintain and update information by implementing a flexible working memory system with adaptive gating mechanism. The C-W model combined the recurrent neural network and the bistability of cells [11]. Another example is the -based working memory model [12]. Some studies investigated the roles of basal ganglia and dopamine in the working memory [13]. However, very few models investigated the effects of dopamine on short-term and long-term memories as well as the interactions of these two types of memories during their formation process.
As is known to all, dopamine is a neurotransmitter, whose absence in the brain may cause tremor, rigidity, bradykinesia and other Parkinson׳s symptoms [14]. There have been many models that can be used to explain the electrophysiological data and the results associated with the dopamine modulation, such as computational models of schizophrenia and dopamine modulation in the prefrontal cortex [15], And the model which verified the importance of combined effects of dopamine in the basal ganglia and the prefrontal cortex [16]. And the study also showed that, dopamine was found to affect movements and also played an important role in memory [17], [18], [19]. Diego et al. [20] revealed that activation of dopamine D1 receptor promoted the formation of cortical LTP, which was conducive to the formation of long-term memory. Regulatory effect of dopamine on protein expression is essential for neuronal plasticity. Dudman et al. [21] demonstrated that the dopamine D1 receptors mediated CREB phosphonation through the phosphonation of NMDAs, indirectly indicating that these receptors affect the formation of long-term memory.
This study aimed to investigate the effects of dopamine on short-term and long-term memories by integrating striatal dopamine and neuronal network models as well as explore the effects of stimulation duration and interference stimulation (which refers to a second short stimulation during the delayed period) on the network firing.
Section snippets
Striatal dopamine model
Dopaminergic neurons are mainly distributed in the mesencephalon and diencephalon, and are divided into six cell populations. Dopaminergic neuron pathways can be summarized into two systems: long dopaminergic neuron system and short dopaminergic neuron system. Striatal dopaminergic system in the mesencephalon (also known as the substantia nigra-striatal system), which belongs to the long dopaminergic neuron system, is the most important [22].
The striatal dopamine model is based on the
Conclusion
In this study, the effects of dopamine, stimulation duration and interference stimulation on long-term and short-term memories were investigated using an integrated striatal dopamine and neuronal network model. The results showed that the duration of neuronal firing was associated with γ, which meant that dopamine affected both short-term and long-term memories. Meanwhile, the stimulation duration could also affect the formation of memory, which was consistent with some conclusions in biology
Acknowledgment
This work is supported by the National Natural Science Foundation of China (Nos. 11232005 and 11472104) and The Ministry of Education Doctoral Foundation (No. 20120074110020).
Xiaoxia Yin is the Master Student of College of Information Science and Engineering in East China University of Science and Technology, China. She received Bachelor of Engineering in Nanhang Jincheng College; in 2013. Her current research interests include neural dynamics.
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Xiaoxia Yin is the Master Student of College of Information Science and Engineering in East China University of Science and Technology, China. She received Bachelor of Engineering in Nanhang Jincheng College; in 2013. Her current research interests include neural dynamics.
Professor Rubin Wang is a native of the People׳s Republic of China. He obtained his Ph.D. from the Department of Electronic-Mechanical Engineering of Nagoya University of Japan in 1998. Dr. Wang was awarded his Ph.D. degree one year early because of his excellent academic achievement. From April 1998 to March 2000 he becomes a postdoctoral fellow at Japan Society for the Promotion of Science (JSPS). In 2000 he was appointed as professor and advisor of doctoral student at Donghua University in Shanghai of China, and from 2001 to 2004 he was appointed as professor of Institute for Computational Science and Engineering at Ocean University of China. He visited Brain Science Institute (BSI) of Japan on 2004 and 2005. Starting from 2005 he is appointed as professor and advisor of doctoral student at East China University of Science and Technology in Shanghai (ECUST). He is a director of Institute for Cognitive Neurodynamics in ECUST. From July 2012 to May 2013 he was a visiting professor at Japan Society for the Promotion of Science (JSPS). And he is also a research professor in Brain Science Institute of Tamagawa University in Japan from 2012 to 2016. His interests include neuroinformatics and cognitive neurodynamics/dynamics of complex systems. He is an Editor-in-Chief of Cognitive Neurodynamics published by Springer. He is also a conference chair of ICCN2007, ICCN2009 and ICCN2015, a conference co-chair of ICCN2011 and ICCN2013. He is author or co-author of over 120 research papers publications in international journals.