Dale Nicholas
Department of Biological Sciences, University of Warwick, Coventry CV4 7AL, United Kingdom.
J Comput Neurosci. 2003 Jan-Feb;14(1):55-70. doi: 10.1023/a:1021176301776.
The spinal motor circuits of the Xenopus embryo have been simulated in a 400-neuron network. To explore the consequences of differing patterns of synaptic connectivity within the network for the generation of the motor rhythm, a system of biologically plausible rules was devised to control synapse formation by three parameters. Each neuron had an intrinsic probability of synapse formation (P(soma), specified by a space constant lambda) that was a monotonically decreasing function of its soma location in the rostro-caudal axis of the simulated network. The neurons had rostral and caudal going axons of specified length (L(axon)) associated with a probability of synapse formation (P(axon)). The final probability of synapse formation was the product of P(soma) and P(axon). Realistic coordinated activity only occurred when L(axon) and the probabilities of interconnection were sufficiently high. Increasing the values of the three network parameters reduced the burst duration, cycle period, and rostro-caudal delay and increased the reliability with which the network functioned as measured by the coefficient of variance of these parameters. Whereas both L(axon) and P(axon) had powerful and consistent effects on network output, the effects of lambda on burst duration and rostro-caudal delay were more variable and depended on the values of the other two parameters. This network model can reproduce the rostro-caudal coordination of swimming without using coupled oscillator theory. The changes in network connectivity and resulting changes in activity explored by the model mimic the development of the motor pattern for swimming in the real embryo.
非洲爪蟾胚胎的脊髓运动回路已在一个包含400个神经元的网络中进行了模拟。为了探究网络内不同突触连接模式对运动节律产生的影响,设计了一套生物学上合理的规则,通过三个参数来控制突触形成。每个神经元都有一个突触形成的内在概率(P(soma),由空间常数λ指定),它是其胞体在模拟网络的头 - 尾轴上位置的单调递减函数。神经元有特定长度(L(axon))的向头侧和尾侧延伸的轴突,与突触形成概率(P(axon))相关。突触形成的最终概率是P(soma)和P(axon)的乘积。只有当L(axon)和互连概率足够高时,才会出现现实的协调活动。增加这三个网络参数的值会缩短爆发持续时间、周期,并减少头 - 尾延迟,同时提高网络功能的可靠性,这通过这些参数的方差系数来衡量。虽然L(axon)和P(axon)对网络输出都有强大且一致的影响,但λ对爆发持续时间和头 - 尾延迟的影响更具变异性,并且取决于其他两个参数的值。该网络模型无需使用耦合振荡器理论就能再现游泳时的头 - 尾协调。模型所探索的网络连接变化以及由此导致的活动变化模拟了真实胚胎中游泳运动模式的发育过程。