Testing for nonlinearity in time series: the method of surrogate data
Conference proceedings on Interpretation of time series from nonlinear mechanical systems
Synchrony in excitatory neural networks
Neural Computation
Dynamical cell assembly hypothesis—theoretical possibility of spatio-temporal coding in the cortex
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
Dynamics of Strongly Coupled Spiking Neurons
Neural Computation
Ergodicity of spike trains: when does trial averaging make sense?
Neural Computation
Self-organizing dual coding based on spike-time-dependent plasticity
Neural Computation
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Interspike intervals of spikes emitted from an integrator neuron model of sensory neurons can encode input information represented as a continuous signal from a deterministic system. If a real brain uses spike timing as a means of information processing, other neurons receiving spatiotemporal spikes from such sensory neurons must also be capable of treating information included in deterministic interspike intervals. In this article, we examine functions of neurons modeling cortical neurons receiving spatiotemporal spikes from many sensory neurons. We show that such neuron models can encode stimulus information passed from the sensory model neurons in the form of interspike intervals. Each sensory neuron connected to the cortical neuron contributes equally to the information collection by the cortical neuron. Although the incident spike train to the cortical neuron is a superimposition of spike trains from many sensory neurons, it need not be decomposed into spike trains according to the input neurons. These results are also preserved for generalizations of sensory neurons such as a small amount of leak, noise, inhomogeneity in firing rates, or biases introduced in the phase distributions.