This paper introduces a kernel adaptive filter implemented with stochastic gradient on temporal differences, kernel Temporal Difference (TD)(λ), to estimate the state-action value function in reinforcement learning. The case λ=0 will be studied in this paper. Experimental results show the method's applicability for learning motor state decoding during a center-out reaching task performed by a monkey. The results are compared to the implementation of a time delay neural network (TDNN) trained with backpropagation of the temporal difference error. From the experiments, it is observed that kernel TD(0) allows faster convergence and a better solution than the neural network.

Electroencephalogram (EEG) has been traditionally used to determine which brain regions are the most likely candidates for resection in patients with focal epilepsy. This methodology relies on the assumption that seizures originate from the same regions of the brain from which interictal epileptiform discharges (IEDs) emerge. Preclinical models are very useful to find correlates between IED locations and the actual regions underlying seizure initiation in focal epilepsy. Rats have been commonly used in preclinical studies of epilepsy 1 ; hence, there exist a large variety of models for focal epilepsy in this particular species. However, it is challenging to record multichannel EEG and to perform brain source imaging in such a small animal. To overcome this issue, we combine a patented-technology to obtain 32-channel EEG recordings from rodents 2 and an MRI probabilistic atlas for brain anatomical structures in Wistar rats to perform brain source imaging. In this video, we introduce the procedures to acquire multichannel EEG from Wistar rats with focal cortical dysplasia, and describe the steps both to define the volume conductor model from the MRI atlas and to uniquely determine the IEDs. Finally, we validate the whole methodology by obtaining brain source images of IEDs and compare them with those obtained at different time frames during the seizure onset.

Background Discrimination of nasal cavity mass lesions is a challenging work requiring extensive experience. A deep learning‐based automated diagnostic system may help clinicians to classify nasal cavity mass lesions. We demonstrated the feasibility of a convolutional neural network (CNN)‐based diagnosis system for automatic detection and classification of nasal polyps (NP) and inverted papillomas (IP). Methods We developed a CNN‐based algorithm using a transfer learning strategy and trained it on nasal endoscopic images. A total of 99 nasal endoscopic images with normal findings, 98 images with NP, and 100 images with IP were analyzed using the developed CNN. Six otolaryngologists participated in clinical visual assessment. Image‐based classification performance was measured by calculating the accuracy and area under the receiver operating characteristic curve (AUC). The diagnostic performance was compared between the CNN and clinical visual assessment by human experts. Results The algorithm achieved an overall accuracy of 0.742 ± 0.058 with the following class accuracies: normal, 0.81± 0.14; IP, 0.57 ± 0.07; and NP, 0.83 ± 0.21. The AUC values for normal, IP, and NP were 0.91 ± 0.06, 0.82 ± 0.09, and 0.84 ± 0.06, respectively. The overall accuracy of the CNN model was comparable with the average performance of human experts (0.742 vs. 0.749; p = 0.11). Conclusions The trained CNN model appears to reliably classify NP and IP of the nasal cavity from nasal endoscopic images; it also yields a reliable reference for diagnosing nasal cavity mass lesions during nasal endoscopy. However, further studies with more test data are warranted to improve the diagnostic accuracy of our CNN model.

Introducing methods to estimate these physiological parameters would enhance our understanding of the neurovascular/metabolic coupling in epileptic brains and improve the localization accuracy on irritative zones and seizure-onset zones through neuroimaging techniques.

This paper introduces a kernel adaptive filter using the stochastic gradient on temporal differences, kernel TD(λ ), to estimate the state-action value function Q in reinforcement learning. Kernel methods are powerful for solving nonlinear problems, but the growing computational complexity and memory size limit their applicability on practical scenarios. To overcome this, the quantization approach introduced in [1] is applied. To help understand the behavior and illustrate the role of the parameters, we apply the algorithm on a 2-dimentional spatial navigation task. Eligibility traces are commonly applied in TD learning to improve data efficiency, so the relations of eligibility trace λ and step size and filter size are observed. Moreover, kernel TD (0) is applied to neural decoding of an 8 target center-out reaching task performed by a monkey. Results show the method can effectively learn the brain-state action mapping for this task.