Cartling B
Department of Theoretical Physics, Royal Institute of Technology, Stockholm, Sweden.
Int J Neural Syst. 1994 Sep;5(3):181-94. doi: 10.1142/s0129065794000207.
A recent neural network model of cortical associative memory incorporating neuronal adaptation by a simplified description of its underlying ionic mechanisms is extended towards more realistic network units and architecture. Excitatory units correspond to groups of adapting pyramidal neurons and inhibitory units to groups of nonadapting interneurons. The network architecture is formed from pairs of one pyramidal and one interneuron unit each with inhibitory connections within and excitatory connections between pairs. The degree of adaptability of the pyramidal units controls the character of the network dynamics. An intermediate adaptability generates limit cycles of transitions between stored patterns and regulates oscillation frequencies in the range of theta rhythms observed in the brain. In particular, neuronal adaptation can impose a direction of transitions between overlapping patterns also in a symmetrically connected network. The model permits a detailed analysis of the transition mechanisms. Temporal sequences of patterns thus formed may constitute parts of associative processes, such as recall of stored sequences or search of pattern subspaces. As a special case, neuronal adaptation can accomplish pattern segmentation by which overlapping patterns are temporally resolved. The type of limit cycles produced by neuronal adaptation may also be of significance for central pattern generators, also for networks involving motor neurons. The applied learning rule of Hebbian type is compared to a modified version also common in neural network modelling. It is also shown that the dependence of the network dynamic behaviour on neuronal adaptability, from fixed point attractors at weak adaptability towards more complex dynamics of limit cycles and chaos at strong adaptability, agrees with that recently observed in a more abstract version of the model. The present description of neuronal adaptation is compared to models based on dynamic firing thresholds.
最近，一个通过对其潜在离子机制进行简化描述来纳入神经元适应性的皮质联想记忆神经网络模型，被扩展到更现实的网络单元和架构。兴奋性单元对应于适应性锥体神经元组，抑制性单元对应于非适应性中间神经元组。网络架构由一对锥体神经元和一对中间神经元组成，每组内部有抑制性连接，两组之间有兴奋性连接。锥体单元的适应程度控制着网络动力学的特征。中等适应能力会产生存储模式之间转换的极限环，并调节大脑中观察到的θ节律范围内的振荡频率。特别是，神经元适应性还可以在对称连接的网络中为重叠模式之间的转换施加一个方向。该模型允许对转换机制进行详细分析。这样形成的模式时间序列可能构成联想过程的一部分，例如存储序列的回忆或模式子空间的搜索。作为一个特殊情况，神经元适应性可以完成模式分割，从而在时间上解析重叠模式。神经元适应性产生的极限环类型对于中枢模式发生器也可能具有重要意义，对于涉及运动神经元的网络也是如此。将应用的赫布型学习规则与神经网络建模中也常见的一个修改版本进行了比较。研究还表明，网络动态行为对神经元适应性的依赖性，从弱适应性时的定点吸引子到强适应性时更复杂的极限环和混沌动力学，与最近在该模型的一个更抽象版本中观察到的情况一致。将目前对神经元适应性的描述与基于动态发放阈值的模型进行了比较。
