Faghihi Faramarz, Moustafa Ahmed A
Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University Fairfax, VA, USA.
Department of Veterans Affairs, New Jersey Health Care System East Orange, NJ, USA ; School of Social Sciences and Psychology and Marcs Institute for Brain and Behavior, University of Western Sydney Sydney, NSW, Australia.
Front Cell Neurosci. 2015 Apr 28;9:164. doi: 10.3389/fncel.2015.00164. eCollection 2015.
Synapses act as information filters by different molecular mechanisms including retrograde messenger that affect neuronal spiking activity. One of the well-known effects of retrograde messenger in presynaptic neurons is a change of the probability of neurotransmitter release. Hebbian learning describe a strengthening of a synapse between a presynaptic input onto a postsynaptic neuron when both pre- and postsynaptic neurons are coactive. In this work, a theory of homeostatic regulation of neurotransmitter release by retrograde messenger and Hebbian plasticity in neuronal encoding is presented. Encoding efficiency was measured for different synaptic conditions. In order to gain high encoding efficiency, the spiking pattern of a neuron should be dependent on the intensity of the input and show low levels of noise. In this work, we represent spiking trains as zeros and ones (corresponding to non-spike or spike in a time bin, respectively) as words with length equal to three. Then the frequency of each word (here eight words) is measured using spiking trains. These frequencies are used to measure neuronal efficiency in different conditions and for different parameter values. Results show that neurons that have synapses acting as band-pass filters show the highest efficiency to encode their input when both Hebbian mechanism and homeostatic regulation of neurotransmitter release exist in synapses. Specifically, the integration of homeostatic regulation of feedback inhibition with Hebbian mechanism and homeostatic regulation of neurotransmitter release in the synapses leads to even higher efficiency when high stimulus intensity is presented to the neurons. However, neurons with synapses acting as high-pass filters show no remarkable increase in encoding efficiency for all simulated synaptic plasticity mechanisms. This study demonstrates the importance of cooperation of Hebbian mechanism with regulation of neurotransmitter release induced by rapid diffused retrograde messenger in neurons with synapses as low and band-pass filters to obtain high encoding efficiency in different environmental and physiological conditions.
突触通过不同的分子机制充当信息过滤器,这些机制包括影响神经元放电活动的逆行信使。逆行信使在突触前神经元中的一个众所周知的作用是改变神经递质释放的概率。赫布学习描述了当突触前和突触后神经元同时活跃时,突触前输入到突触后神经元之间的突触增强。在这项工作中,提出了一种关于逆行信使对神经递质释放的稳态调节以及神经元编码中的赫布可塑性的理论。针对不同的突触条件测量了编码效率。为了获得高编码效率,神经元的放电模式应取决于输入强度并显示低水平的噪声。在这项工作中,我们将放电序列表示为0和1(分别对应于一个时间间隔内的非放电或放电),作为长度等于3的单词。然后使用放电序列测量每个单词(这里有八个单词)的频率。这些频率用于测量不同条件下和不同参数值时的神经元效率。结果表明,当突触中同时存在赫布机制和神经递质释放的稳态调节时,具有充当带通滤波器的突触的神经元在编码其输入时表现出最高效率。具体而言,当向神经元施加高刺激强度时,突触中反馈抑制的稳态调节与赫布机制和神经递质释放的稳态调节的整合会导致更高的效率。然而,对于所有模拟的突触可塑性机制,具有充当高通滤波器的突触的神经元在编码效率上没有显著提高。这项研究证明了赫布机制与由快速扩散的逆行信使诱导的神经递质释放调节在具有低通和带通滤波器突触的神经元中合作的重要性,以便在不同的环境和生理条件下获得高编码效率。