Cartling B
Department of Theoretical Physics, Royal Institute of Technology, Stockholm, Sweden.
Biol Cybern. 1997 May;76(5):383-95. doi: 10.1007/s004220050352.
Generation and control of different dynamical modes of computational processes in a net of interconnected integrate-and-fire neurons are demonstrated. A net architecture resembling a generic cortical structure is formed from pairs of excitatory and inhibitory units with excitatory connections between and inhibitory connections within pairs. Integrate-and-fire model neurons derived from detailed conductance-based models of neocortical pyramidal cells and fast-spiking interneurons are employed for the excitatory and inhibitory units, respectively. Firing-rate adaptation is incorporated into the excitatory units based on the regulation of the slow afterhyperpolarization phase of action potentials by intracellular calcium ions. Saturation of synaptic conductances is implemented for the interconnections between units. It is shown that neuronal adaptation of the excitatory units can generate richer net dynamics than relaxation to fixed-point attractors-in a pattern space. At strong adaptivity, i.e. when the neuronal excitability is strongly influenced by the preceding activity, complex dynamics of either aperiodic or limit-cycle character are generated in both the pattern space and the phase space of all dynamical variables. This regime corresponds to an exploratory mode of the system, in which the pattern space can be searched. At weak adaptivity, the dynamics are governed by fixed-point attractors in the pattern space, and this corresponds to a mode for retrieval of a particular pattern. In the brain, neuronal adaptivity can be regulated by various neuromodulators. The results are in accordance with those recently obtained by means of more abstract models formulated in terms of mean firing rates. The increased realism makes the present model reveal more detailed mechanisms and strengthens the relevance of the conclusions to biological systems. The simplicity and realism of the coupled integrate-and-fire neurons make the present model useful for studies of systems in which the temporal aspects of neural coding are important.
本文展示了在相互连接的积分发放神经元网络中计算过程的不同动态模式的生成与控制。一个类似于通用皮质结构的网络架构由兴奋性和抑制性单元对构成，单元对之间存在兴奋性连接，而单元对内部存在抑制性连接。分别采用从新皮质锥体细胞和快速发放中间神经元的详细基于电导的模型推导而来的积分发放模型神经元作为兴奋性和抑制性单元。基于细胞内钙离子对动作电位缓慢超极化后阶段的调节，将发放率适应性纳入兴奋性单元。对单元之间的连接实现突触电导饱和。结果表明，兴奋性单元的神经元适应性能够在模式空间中产生比弛豫到定点吸引子更丰富的网络动态。在强适应性情况下，即当神经元兴奋性受到先前活动的强烈影响时，在所有动态变量的模式空间和相空间中都会产生非周期性或极限环特征的复杂动态。这种状态对应于系统的探索模式，在此模式下可以搜索模式空间。在弱适应性情况下，动态由模式空间中的定点吸引子控制，这对应于特定模式检索模式。在大脑中，神经元适应性可由各种神经调质调节。这些结果与最近通过基于平均发放率制定的更抽象模型获得的结果一致。更高的现实性使得本模型能够揭示更详细的机制，并加强了结论与生物系统的相关性。耦合积分发放神经元的简单性和现实性使得本模型对于研究神经编码时间方面很重要的系统很有用。
