Despite unique well characterized neuronal properties, such as extensive electrical coupling and low firing rates, the role of the inferior olive (IO), which is the source of the climbing fiber inputs to cerebellar Purkinje cells, is still controversial. We propose that the IO stochastically recodes the high-frequency information carried by its synaptic inputs into stochastic, low-rate spikes in its climbing fiber output. Computer simulations of realistic IO networks showed that moderate electrical coupling produced chaotic firing, which maximized the input-output mutual information. This ''chaotic resonance'' may allow rich error signals to reach individual Purkinje cells, even at low firing rates, allowing efficient cerebellar learning.O ver a century of cerebellar research has provided us with comprehensive understandings of cerebellar anatomy and physiology. It is well known, for instance, that the output neurons of the cerebellar cortex, the Purkinje cells, receive two major types of synaptic inputs: Ͼ100,000 parallel fibers and a single climbing fiber, an axon from an inferior olive (IO) neuron; whereas summation of parallel fiber inputs generate ''simple spikes,'' a single IO spike generates a ''complex spike'' through the powerful climbing fiber input. Furthermore, the major anatomical and electrophysiological properties of the IO neurons are well characterized (1). First, these neurons generate dendritic and somatic spikes at low firing rates in vivo (three spikes per sec at most) (2). Second, they are electrotonically coupled by gap junctions (3), more extensively than any other cells of the mammalian brain (4). Third, they exhibit subthreshold oscillations in vitro (5, 6). However, despite this detailed knowledge, we still lack a clear understanding of the function of the cerebellum, and two seemingly contradictory major hypotheses have been proposed, each based on a dramatically different view of the IO (7,8).According to the cerebellar learning hypothesis (9-13), when conjointly activated with parallel fibers, IO spikes modify cerebellar input-output transformations, in agreement with the known long-term depression (LTD) at the parallel fiberPurkinje cell synapse (14). Recent cerebellar motor learning theories (12, 15-19) make two further postulates relevant to this hypothesis. First, the IO neurons must fire at a low firing rate so that complex spikes encoding error signals do not interfere with simple spikes carrying motor control commands (12, 15). Second, the IO must transmit error signals (15, 20) with hightemporal resolution for cerebellar learning for efficient motor control.Alternatively, the cerebellar timing hypothesis states that the IO exerts its influence on motor control in real time via synchronous and rhythmic discharges (21). This hypothesis is in agreement with the facts that IO neurons are extensively electrically coupled via gap junctions (8) and that IO neurons fire with some degree of rhythmicity and pair-wise synchrony both in vitro (22) and in vivo (21). It is further support...

No abstract

No abstract

No abstract

<abstract> <p>Peaberries are a special type of coffee bean with an oval shape. Peaberries are not considered defective, but separating peaberries is important to make the shapes of the remaining beans uniform for roasting evenly. The separation of peaberries and normal coffee beans increases the value of both peaberries and normal coffee beans in the market. However, it is difficult to sort peaberries from normal beans using existing commercial sorting machines because of their similarities. In previous studies, we have shown the availability of image processing and machine learning techniques, such as convolutional neural networks (CNNs), support vector machines (SVMs), and k-nearest-neighbors (KNNs), for the classification of peaberries and normal beans using a powerful desktop PC. As the next step, assuming the use of our system in the least developed countries, this study was performed to examine their implementation in and the limitations of Raspberry Pi 3. To improve the performance, we modified the CNN architecture from our previous studies. As a result, we found that the CNN model outperformed both linear SVM and KNN on the use of Raspberry Pi 3. For instance, the trained CNN could classify approximately 13.77 coffee bean images per second with 98.19% accuracy of the classification with 64×64 pixel color images on Raspberry Pi 3. There were limitations of Raspberry Pi 3 for linear SVM and KNN on the use of large image sizes because of the system's small RAM size. Generally, the linear SVM and KNN were faster than the CNN with small image sizes, but we could not obtain better results with both the linear SVM and KNN than the CNN in terms of the classification accuracy. Our results suggest that the combination of the CNN and Raspberry Pi 3 holds the promise of inexpensive peaberries and a normal bean sorting system for the least developed countries.</p> </abstract>