Mechanisms of heterosynaptic metaplasticity

Hulme, Sarah R.; Jones, Owen D.; Raymond, Clarke R.; Sah, Pankaj; Abraham, Wickliffe C.

doi:10.1098/rstb.2013.0148

Cited by 84 publications

(83 citation statements)

References 90 publications

(135 reference statements)

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“…Another important question for the interpretation of heterosynaptic plasticity is whether it causes mostly synaptic depression similar to LTD or if it rather prevents or even resets early LTP through depotentiation at the unstimulated pathway [144]. Finally, the role of heterosynaptic metaplasticity [143] remains largely elusive.…”

Section: Discussionmentioning

confidence: 99%

Hebbian plasticity requires compensatory processes on multiple timescales

Zenke

Gerstner

2017

Phil. Trans. R. Soc. B

165

209

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We review a body of theoretical and experimental research on Hebbian and homeostatic plasticity, starting from a puzzling observation: while homeostasis of synapses found in experiments is a slow compensatory process, most mathematical models of synaptic plasticity use rapid compensatory processes (RCPs). Even worse, with the slow homeostatic plasticity reported in experiments, simulations of existing plasticity models cannot maintain network stability unless further control mechanisms are implemented. To solve this paradox, we suggest that in addition to slow forms of homeostatic plasticity there are RCPs which stabilize synaptic plasticity on short timescales. These rapid processes may include heterosynaptic depression triggered by episodes of high postsynaptic firing rate. While slower forms of homeostatic plasticity are not sufficient to stabilize Hebbian plasticity, they are important for fine-tuning neural circuits. Taken together we suggest that learning and memory rely on an intricate interplay of diverse plasticity mechanisms on different timescales which jointly ensure stability and plasticity of neural circuits.This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.

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Section: Discussionmentioning

confidence: 99%

Hebbian plasticity requires compensatory processes on multiple timescales

Zenke

Gerstner

2017

Phil. Trans. R. Soc. B

165

209

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“…Adaptations in hippocampal synaptic plasticity, such as LTP and LTD, underlie changes in spatial learning and memory processing [50]. The disruption in the balance between LTP and LTD in the MetS group is likely a primary event for the observed failure in learning and memory functions [51]. It is notable that these events were detected in the hippocampus, the locus of associative memory [52], which is heavily compromised during aging and AD.…”

Section: Discussionmentioning

confidence: 99%

Fructose consumption reduces hippocampal synaptic plasticity underlying cognitive performance

Cisternas

Salazar

Serrano

et al. 2015

Biochimica et Biophysica Acta (BBA) - Molecular Basis of Diseas

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Metabolic syndrome (MetS) is a global epidemic, which involves a spectrum of metabolic disorders comprising diabetes and obesity. The impact of MetS on the brain is becoming to be a concern, however, the poor understanding of mechanisms involved has limited the development of therapeutic strategies. We induced a MetS-like condition by exposing mice to fructose feeding for 7 weeks. There was a dramatic deterioration in the capacity of the hippocampus to sustain synaptic plasticity in the forms of long-term potentiation (LTP) and long-term depression (LTD). Mice exposed to fructose showed a reduction in the number of contact zones and the size of postsynaptic densities (PSDs) in the hippocampus, as well as a decrease in hippocampal neurogenesis. There was an increase in lipid peroxidation likely associated with a deficiency in plasma membrane excitability. Consistent with an overall hippocampal dysfunction, there was a subsequent decrease in hippocampal dependent learning and memory performance, i.e., spatial learning and episodic memory. Most of the pathological sequel of MetS in the brain was reversed three month after discontinue fructose feeding. These results are novel to show that MetS triggers a cascade of molecular events, which disrupt hippocampal functional plasticity, and specific aspects of learning and memory function. The overall information raises concerns about the risk imposed by excessive fructose consumption on the pathology of neurological disorders.

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“…As shown in Figure 12a, the normal approach to study synaptic plasticity is to modulate the synaptic weight upon applying a learning action stimulus. [197] In 2016, Tan, et al [112] first demonstrated metaplasticity in the Pt/WO 3−x /Pt memristive device performing bipolar analog switching with forgetting effect. [31] This priming stimulus might not change the synaptic weight, but it does affect the synaptic response (i.e., the sign and size of weight change) evoked by the following learning activities, which highlight the importance of the previous history of activities on subsequent synaptic plasticity.…”

Section: Wwwadvelectronicmatdementioning

confidence: 99%

Memristive Synapses and Neurons for Bioinspired Computing

Yang

Huang

Guo

2019

Adv Elect Materials

154

127

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and constant data movement are needed while working. In the human brain, huge and complex neural networks composed of gigantic amounts of neurons massively interconnected by an even larger number of synapses are in charge of computing and memory. Unlike a digital computer, information storage and processing happen at the same time in the brain. [3] Artificial intelligence (AI) that could rival biological neural networks is being continuously pursued by human being. There are two approaches to implement AI: one is the software simulation of neural networks with the aid of digital computers, another is developing hardware-integrated circuits of neural networks. In fact, AI based on the software simulation is evolving very fast thanks to the advances in the development of algorithm in recent years, and it even outperforms the human brain in specific complex tasks, such as playing the game of Go. [4] However, software-based AI suffers from critical issues of enormous power consumption and massive data throughput. Hardware-based AI systems are more efficient in terms of speed and power consumption; however, complementary metal oxide semiconductor (CMOS) transistors are inherently different from the basic building blocks of biological neural networks, neurons, and synapses, in terms of operation dynamic and behavior. A circuit consisting of at least ten transistors are required to realize the function of one biological synapse, which is impractical for the implementation of large networks, not to mention the human brain (roughly 10 11 neurons interconnected by ≈10 15 synapses). [5,6] Fortunately, the emergence of memristive devices with innate dynamics resembling biological synapses and neurons provides a feasible and simple way to build hardware-based bio-inspired computing systems. [7,8] For instance, only one compact memristive device with a metalinsulator-metal (MIM) sandwich structure is sufficient to reproduce some functions of a biological synapse. Moreover, the vector-matrix multiplication, which is believed to be the most data-intensive work in neural networks, can be naturally performed in a cross-bar array of memristive devices on the physical level based on Ohm and Kirchhoff laws. [9,10] These features endow the memristive devices ideally suited to realize highly efficient bioinspired computing systems in hardware.To realize highly efficient neuromorphic computing that is comparable to biological counterparts, bioinspired computing systems, consisting of biorealistic artificial synapses and neurons, are developed with memristive devices with native dynamics resembling biological synapses and neurons. Tremendous materials and devices have been successfully used to emulate diverse functions of synapses, as well as neurons, in the last decade. Herein, approaches to realize certain synaptic or neuronal functions are introduced with state-of-art experimental demonstrations. First, the dynamics and working principles of biological synapses and neurons are briefly presented to provide guidance for developing biore...

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Mechanisms of heterosynaptic metaplasticity

Cited by 84 publications

References 90 publications

Hebbian plasticity requires compensatory processes on multiple timescales

Hebbian plasticity requires compensatory processes on multiple timescales

Fructose consumption reduces hippocampal synaptic plasticity underlying cognitive performance

Memristive Synapses and Neurons for Bioinspired Computing

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