Spike Timing-Dependent Plasticity in the Mouse Barrel Cortex Is Strongly Modulated by Sensory Learning and Depends on Activity of Matrix Metalloproteinase 9

被引:0
|
作者
Katarzyna Lebida
Jerzy W. Mozrzymas
机构
[1] Wroclaw Medical University,Laboratory of Neuroscience, Department of Biophysics
[2] Wroclaw University,Department of Animal Molecular Physiology, Institute of Experimental Biology
来源
Molecular Neurobiology | 2017年 / 54卷
关键词
Matrix metalloproteinases; Synaptic plasticity; Somatosensory cortex; Learning; Tonic inhibition;
D O I
暂无
中图分类号
学科分类号
摘要
Experience and learning in adult primary somatosensory cortex are known to affect neuronal circuits by modifying both excitatory and inhibitory transmission. Synaptic plasticity phenomena provide a key substrate for cognitive processes, but precise description of the cellular and molecular correlates of learning is hampered by multiplicity of these mechanisms in various projections and in different types of neurons. Herein, we investigated the impact of associative learning on neuronal plasticity in distinct types of postsynaptic neurons by checking the impact of classical conditioning (pairing whisker stroking with tail shock) on the spike timing-dependent plasticity (t-LTP and t-LTD) in the layer IV to II/III vertical pathway of the mouse barrel cortex. Learning in this paradigm practically prevented t-LTP measured in pyramidal neurons but had no effect on t-LTD. Since classical conditioning is known to affect inhibition in the barrel cortex, we examined its effect on tonic GABAergic currents and found a strong downregulation of these currents in the layer II/III interneurons but not in pyramidal cells. Matrix metalloproteinases emerged as crucial players in synaptic plasticity and learning. We report that the blockade of MMP-9 (but not MMP-3) abolished t-LTP having no effect on t-LTD. Moreover, associative learning resulted in an upregulation of gelatinolytic activity within the “trained” barrel. We conclude that LTP induced by spike timing-dependent plasticity (STDP) paradigm is strongly correlated with associative learning and critically depends on the activity of MMP-9.
引用
收藏
页码:6723 / 6736
页数:13
相关论文
共 50 条
  • [31] Oscillations, Phase-of-Firing Coding, and Spike Timing-Dependent Plasticity: An Efficient Learning Scheme
    Masquelier, Timothee
    Hugues, Etienne
    Deco, Gustavo
    Thorpe, Simon J.
    JOURNAL OF NEUROSCIENCE, 2009, 29 (43): : 13484 - 13493
  • [32] Spike timing-dependent plasticity under imbalanced excitation and inhibition reduces the complexity of neural activity
    Park, Jihoon
    Kawai, Yuji
    Asada, Minoru
    FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2023, 17
  • [33] Distinct mechanisms of spike timing-dependent LTD at vertical and horizontal inputs onto L2/3 pyramidal neurons in mouse barrel cortex
    Banerjee, Abhishek
    Gonzalez-Rueda, Ana
    Sampaio-Baptista, Cassandra
    Paulsen, Ole
    Rodriguez-Moreno, Antonio
    PHYSIOLOGICAL REPORTS, 2014, 2 (03):
  • [34] CELLULAR MECHANISM OF SPIKE TIMING-DEPENDENT LTD AT HORIZONTAL L2/3-L2/3 SYNAPSES IN MOUSE BARREL CORTEX.
    Andrade-Talavera, Y.
    Rodriguez-Moreno, A.
    ACTA PHYSIOLOGICA, 2014, 212 : 24 - 25
  • [35] Stochastic binary synapses having sigmoidal cumulative distribution functions for unsupervised learning with spike timing-dependent plasticity
    Yoshifumi Nishi
    Kumiko Nomura
    Takao Marukame
    Koichi Mizushima
    Scientific Reports, 11
  • [36] Spike timing-dependent plasticity: a learning rule for dendritic integration in rat CA1 pyramidal neurons
    Campanac, Emilie
    Debanne, Dominique
    JOURNAL OF PHYSIOLOGY-LONDON, 2008, 586 (03): : 779 - 793
  • [37] Incremental acquisition of behaviors and signs based on a reinforcement learning schemata model and a spike timing-dependent plasticity network
    Taniguchi, Tadahiro
    Sawaragi, Tetsuo
    ADVANCED ROBOTICS, 2007, 21 (10) : 1177 - 1199
  • [38] 2D co-ordinate transformation based on a spike timing-dependent plasticity learning mechanism
    Wu, QingXiang
    McGinnity, Thomas Martin
    Maguire, Liam
    Belatreche, Ammar
    Glackin, Brendan
    NEURAL NETWORKS, 2008, 21 (09) : 1318 - 1327
  • [39] Stochastic binary synapses having sigmoidal cumulative distribution functions for unsupervised learning with spike timing-dependent plasticity
    Nishi, Yoshifumi
    Nomura, Kumiko
    Marukame, Takao
    Mizushima, Koichi
    SCIENTIFIC REPORTS, 2021, 11 (01)
  • [40] A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback
    Legenstein, Robert
    Pecevski, Dejan
    Maass, Wolfgang
    PLOS COMPUTATIONAL BIOLOGY, 2008, 4 (10)