Epileptic seizures can cause a variety of temporary changes in perception and behavior. In the human EEG they are reflected by multiple ictal patterns, where epileptic seizures typically become apparent as characteristic, usually rhythmic signals, often coinciding with or even preceding the earliest observable changes in behavior. Their detection at the earliest observable onset of ictal patterns in the EEG can, thus, be used to start more-detailed diagnostic procedures during seizures and to differentiate epileptic seizures from other conditions with seizure-like symptoms. Recently, warning and intervention systems triggered by the detection of ictal EEG patterns have attracted increasing interest. Since the workload involved in the detection of seizures by human experts is quite formidable, several attempts have been made to develop automatic seizure detection systems. So far, however, none of these found widespread application. Here, we present a novel procedure for generic, online, and real-time automatic detection of multimorphologic ictal-patterns in the human long-term EEG and its validation in continuous, routine clinical EEG recordings from 57 patients with a duration of approximately 43 hours and additional 1,360 hours of seizure-free EEG data for the estimation of the false alarm rates. We analyzed 91 seizures (37 focal, 54 secondarily generalized) representing the six most common ictal morphologies (alpha, beta, theta, and delta- rhythmic activity, amplitude depression, and polyspikes). We found that taking the seizure morphology into account plays a crucial role in increasing the detection performance of the system. Moreover, besides enabling a reliable (mean false alarm rate<0.5/h, for specific ictal morphologies<0.25/h), early and accurate detection (average correct detection rate>96%) within the first few seconds of ictal patterns in the EEG, this procedure facilitates the automatic categorization of the prevalent seizure morphologies without the necessity to adapt the proposed system to specific patients.

To understand the mechanisms of fast information processing in the brain, it is necessary to determine how rapidly populations of neurons can respond to incoming stimuli in a noisy environment. Recently, it has been shown experimentally that an ensemble of neocortical neurons can track a time-varying input current in the presence of additive correlated noise very fast, up to frequencies of several hundred hertz. Modulations in the firing rate of presynaptic neuron populations affect, however, not only the mean but also the variance of the synaptic input to postsynaptic cells. It has been argued that such modulations of the noise intensity (multiplicative modulation) can be tracked much faster than modulations of the mean input current (additive modulation). Here, we compare the response characteristics of an ensemble of neocortical neurons for both modulation schemes. We injected sinusoidally modulated noisy currents (additive and multiplicative modulation) into layer V pyramidal neurons of the rat somatosensory cortex and measured the trial and ensemble-averaged spike responses for a wide range of stimulus frequencies. For both modulation paradigms, we observed low-pass behavior. The cutoff frequencies were markedly high, considerably higher than the average firing rates. We demonstrate that modulations in the variance can be tracked significantly faster than modulations in the mean input. Extremely fast stimuli (up to 1 kHz) can be reliably tracked, provided the stimulus amplitudes are sufficiently high.

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Existing models of EEG have mainly focused on relations to network dynamics characterized by firing rates [L. and relate synchronicity and irregularity in the network to EEG states. We show that the transformation between network activity and EEG can be approximately mediated by linear kernel with the shape of an a-or g-function, allowing us a comparison between EEG states and network activity space. We find that the simulated EEG generated from asynchronous irregular type network activity is closely related to the human EEG recorded in the awake state, evaluated using power spectral density characteristics. r

We present preliminary results from our sixth placed entry to the Flatland international competition for train rescheduling, including two improvements for optimized reinforcement learning (RL) training efficiency, and two hypotheses with respect to the prospect of deep RL for complex real-world control tasks: first, that current state of the art policy gradient methods seem inappropriate in the domain of high-consequence environments; second, that learning explicit communication actions (an emerging machine-to-machine language, so to speak) might offer a remedy. These hypotheses need to be confirmed by future work. If confirmed, they hold promises with respect to optimizing highly efficient logistics ecosystems like the Swiss Federal Railways railway network.