Moldakarimov Samat B, McClelland James L, Ermentrout G Bard
Department of Mathematics, University of Pittsburgh, Pittsburgh, PA 15260, USA.
Proc Natl Acad Sci U S A. 2006 Oct 31;103(44):16526-31. doi: 10.1073/pnas.0607589103. Epub 2006 Oct 18.
Experience with transient stimuli leads to stronger neural responses that also rise and fall more sharply in time. This sharpening enhances the processing of transients and may be especially relevant for speech perception. We consider a learning rule for inhibitory connections that promotes this sharpening effect by adjusting these connections to maintain a target homeostatic level of activity in excitatory neurons. We analyze this rule in a recurrent network model of excitatory and inhibitory units. Strengthening inhibitory-->excitatory connections along with excitatory-->excitatory connections is required to obtain a sharpening effect. Using the homeostatic rule, we show that repeated presentations of a transient signal will "teach" the network to respond to the signal with both higher amplitude and shorter duration. The model also captures reorganization of receptive fields in the sensory hand area after amputation or peripheral nerve resection.
对瞬态刺激的体验会导致更强的神经反应，这种反应在时间上的上升和下降也更为急剧。这种锐化增强了对瞬态的处理，可能对语音感知尤为重要。我们考虑一种抑制性连接的学习规则，该规则通过调整这些连接来促进这种锐化效应，以维持兴奋性神经元中目标稳态活动水平。我们在兴奋性和抑制性单元的循环网络模型中分析此规则。为了获得锐化效应，需要加强抑制性→兴奋性连接以及兴奋性→兴奋性连接。使用稳态规则，我们表明瞬态信号的重复呈现将“教导”网络以更高的幅度和更短的持续时间对信号做出反应。该模型还捕捉了截肢或周围神经切除后感觉手部区域感受野的重组。
