Buhmann J, Schulten K
Biol Cybern. 1986;54(4-5):319-35. doi: 10.1007/BF00318428.
We consider a neural network model in which the single neurons are chosen to closely resemble known physiological properties. The neurons are assumed to be linked by synapses which change their strength according to Hebbian rules on a short time scale (100 ms). The dynamics of the network--the time evolution of the cell potentials and the synapses--is investigated by computer simulation. As in more abstract network models (Cooper 1973; Hopfield 1982; Kohonen 1984) it is found that the local dynamics of the cell potentials and the synaptic strengths result in global cooperative properties of the network and enable the network to process an incoming flux of information and to learn and store patterns associatively. A trained net can associate missing details of a pattern, can correct wrong details and can suppress noise in a pattern. The network can further abstract the prototype from a series of patterns with variations. A suitable coupling constant connecting the dynamics of the cell potentials with the synaptic strengths is derived by a mean field approximation. This coupling constant controls the neural sensitivity and thereby avoids both extremes of the network state, the state of permanent inactivity and the state of epileptic hyperactivity.
我们考虑一种神经网络模型，其中单个神经元被设计成与已知的生理特性非常相似。假定神经元通过突触相连，这些突触在短时间尺度（100毫秒）内根据赫布规则改变其强度。通过计算机模拟研究网络的动力学——细胞电位和突触的时间演化。与更抽象的网络模型（库珀，1973年；霍普菲尔德，1982年；科霍宁，1984年）一样，发现细胞电位和突触强度的局部动力学导致网络的全局协同特性，并使网络能够处理传入的信息流，并以联想方式学习和存储模式。经过训练的网络可以关联模式中缺失的细节，可以纠正错误的细节，并可以抑制模式中的噪声。该网络可以进一步从一系列有变化的模式中抽象出原型。通过平均场近似推导出连接细胞电位动力学和突触强度的合适耦合常数。这个耦合常数控制神经敏感性，从而避免网络状态的两个极端，即永久不活动状态和癫痫性多动状态。
