Synapses are plastic and can be modified by changes in spike timing. Whereas most studies of long-term synaptic plasticity focus on excitation, inhibitory plasticity may be critical for controlling information processing, memory storage, and overall excitability in neural circuits. Here we examine spike-timing-dependent plasticity (STDP) of inhibitory synapses onto layer 5 neurons in slices of mouse auditory cortex, together with concomitant STDP of excitatory synapses. Pairing pre- and postsynaptic spikes potentiated inhibitory inputs irrespective of precise temporal order within ∼10 ms. This was in contrast to excitatory inputs, which displayed an asymmetrical STDP time window. These combined synaptic modifications both required NMDA receptor activation and adjusted the excitatory-inhibitory ratio of events paired with postsynaptic spiking. Finally, subthreshold events became suprathreshold, and the time window between excitation and inhibition became more precise. These findings demonstrate that cortical inhibitory plasticity requires interactions with co-activated excitatory synapses to properly regulate excitatory-inhibitory balance.
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http://dx.doi.org/10.1016/j.neuron.2015.03.014 | DOI Listing |
Song acquisition behavior observed in the songbird system provides a notable example of learning through trial- and-error which parallels human speech acquisition. Studying songbird vocal learning can offer insights into mechanisms underlying human language. We present a computational model of song learning that integrates reinforcement learning (RL) and Hebbian learning and agrees with known songbird circuitry.
View Article and Find Full Text PDFAt cellular and circuit levels, drug addiction is considered a dysregulation of synaptic plasticity. In addition, dysfunction of the glutamate transporter 1 (GLT-1) in the nucleus accumbens (NAc) has also been proposed as a mechanism underlying drug addiction. However, the cellular and synaptic impact of GLT-1 alterations in the NAc remain unclear.
View Article and Find Full Text PDFBiomed Eng Lett
January 2025
Department of Computer Engineering, Kwangwoon University, Seoul, 01897 Republic of Korea.
Robotic systems rely on spatio-temporal information to solve control tasks. With advancements in deep neural networks, reinforcement learning has significantly enhanced the performance of control tasks by leveraging deep learning techniques. However, as deep neural networks grow in complexity, they consume more energy and introduce greater latency.
View Article and Find Full Text PDFBMC Neurol
December 2024
Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, 02115, USA.
Parkinson's disease (PD) is a neurodegenerative disease affecting millions of people around the world. Conventional PD detection algorithms are generally based on first and second-generation artificial neural network (ANN) models which consume high energy and have complex architecture. Considering these limitations, a time-varying synaptic efficacy function based leaky-integrate and fire neuron model, called SEFRON is used for the detection of PD.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, 55905, USA.
Alcohol use disorder (AUD) is a chronic relapsing brain disorder characterized by an impaired ability to stop or control alcohol consumption despite adverse social, occupational, or health consequences. AUD affects nearly one-third of adults at some point during their lives, with an associated cost of approximately $249 billion annually in the U.S.
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