How does a single spiking neuron process information? This question is a long lasting one, which has been constantly posed and pursued by many researchers from different perspectives. In this paper, we tackle this issue from the perspective of reservoir computing using a single Izhikevich neuron as a model system. To prepare reservoir nodes from the response of a single Izhikevich neuron, we used of a technique called time multiplexing, which exploits a time-scale difference between input-output series and the transient dynamics of the single neuron. Based on this scheme, we evaluated the information processing capability of a single Izhikevich neuron using a standard benchmark task. Furthermore, we measured its memory capacity and showed its characteristic memory profile in various parameter settings. Finally, the relationships between the dynamical properties of the Izhikevich neuron and its memory capacity are discussed in detail.

Our previous study showed that embodied cultured neural networks and spiking neural networks with spike-timing dependent plasticity (STDP) can learn a behavior as they avoid stimulation from outside. In a sense, the embodied neural network can autonomously change their activity to avoid external stimuli. In this paper, as a result of our experiments using cultured neurons, we find that there is also a second property allowing the network to avoid stimulation: if the network cannot learn to avoid the external stimuli, it tends to decrease the stimulus-evoked spikes as if to ignore the input neurons. We also show such a behavior is reproduced by spiking neural networks with asymmetric-STDP. We consider that these properties can be regarded as autonomous regulation of self and non-self for the network.