2008 年 46 巻 6 号 p. 667-674
In the brain, cooperative activity of many neurons is believed to play essential role in neuronal information processing. To evaluate correlation of neuronal activity, cross-correlation histogram (CCH) and cross-correlation coefficient (CCC) of spike timing of neuron have been widely used. The CCC is available to judge whether or not the two neurons have a synaptic connection. However, CCC cannot be used to evaluate the strength of the connection because the CCC is dependent on the firing rate of postsynaptic neurons ; even though the synaptic efficacy was invariant, increase in the firing rate of the postsynaptic neuron due to the increase in background inputs would result in decrease in CCC. In this paper, we propose a method to evaluate the strength of the neuronal connectivity (synaptic weight) and the excitability of postsynaptic neuron (threshold) . We assume a simple neuron model and stochastic property of background inputs. Both the weight and threshold are calculated based on the improved CCH method but the weight is unaffected by the firing rate of postsynaptic neuron. We applied this method to data recorded from hippocampal CA3 neurons in vitro to estimate the distribution of synaptic weight of CA3 neurons. It was found that the histogram of synaptic weight was well fitted by the unimodal gamma distribution, whereas the CCC was similar to the exponential distribution. According to the intracellular recording data, the histogram of excitatory postsynaptic potential amplitudes, corresponding to the synaptic weights, is a unimodal, gamma-like distribution. Therefore, it is suggested that our method provides a better estimation than the conventional method. Finally we investigated activity-dependent change in synaptic weight. It was found that the histogram unchanged after an application of conditioning stimulation facilitating synaptic modification, even thought individual synaptic weights changed. These results suggest a mechanism for regulating synaptic distribution in the neuron.