Rapid bidirectional reorganization of cortical microcircuits.

Mature neocortex adapts to altered sensory input by changing neural activity in cortical circuits. The underlying cellular mechanisms remain unclear. We used blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) to show reorganization in somatosensory cortex elicited by altered whisker sensory input. We found that there was rapid expansion followed by retraction of whisker cortical maps. The cellular basis for the reorganization in primary somatosensory cortex was investigated with paired electrophysiological recordings in the periphery of the expanded whisker representation. During map expansion, the chance of finding a monosynaptic connection between pairs of pyramidal neurons increased 3-fold. Despite the rapid increase in local excitatory connectivity, the average strength and synaptic dynamics did not change, which suggests that new excitatory connections rapidly acquire the properties of established excitatory connections. During map retraction, entire excitatory connections between pyramidal neurons were lost. In contrast, connectivity between pyramidal neurons and fast spiking interneurons was unchanged. Hence, the changes in local excitatory connectivity did not occur in all circuits involving pyramidal neurons. Our data show that pyramidal neurons are recruited to and eliminated from local excitatory networks over days. These findings suggest that the local excitatory connectome is dynamic in mature neocortex

Analysis of Wide and Deep Echo State Networks for Multiscale Spatiotemporal Time Series Forecasting

Echo state networks are computationally lightweight reservoir models inspired by the random projections observed in cortical circuitry. As interest in reservoir computing has grown, networks have become deeper and more intricate. While these networks are increasingly applied to nontrivial forecasting tasks, there is a need for comprehensive performance analysis of deep reservoirs. In this work, we study the influence of partitioning neurons given a budget and the effect of parallel reservoir pathways across different datasets exhibiting multi-scale and nonlinear dynamics.Comment: 10 pages, 10 figures, Proceedings of the Neuro-inspired Computational Elements Workshop (NICE '19), March 26-28, 2019, Albany, NY, US

Multi-Kernel Capsule Network for Schizophrenia Identification

Schizophrenia seriously affects the quality of life. To date, both simple (e.g., linear discriminant analysis) and complex (e.g., deep neural network) machine learning methods have been utilized to identify schizophrenia based on functional connectivity features. The existing simple methods need two separate steps (i.e., feature extraction and classification) to achieve the identification, which disables simultaneous tuning for the best feature extraction and classifier training. The complex methods integrate two steps and can be simultaneously tuned to achieve optimal performance, but these methods require a much larger amount of data for model training. To overcome the aforementioned drawbacks, we proposed a multi-kernel capsule network (MKCapsnet), which was developed by considering the brain anatomical structure. Kernels were set to match with partition sizes of brain anatomical structure in order to capture interregional connectivities at the varying scales. With the inspiration of widely-used dropout strategy in deep learning, we developed capsule dropout in the capsule layer to prevent overfitting of the model. The comparison results showed that the proposed method outperformed the state-of-the-art methods. Besides, we compared performances using different parameters and illustrated the routing process to reveal characteristics of the proposed method. MKCapsnet is promising for schizophrenia identification. Our study first utilized capsule neural network for analyzing functional connectivity of magnetic resonance imaging (MRI) and proposed a novel multi-kernel capsule structure with consideration of brain anatomical parcellation, which could be a new way to reveal brain mechanisms. In addition, we provided useful information in the parameter setting, which is informative for further studies using a capsule network for other neurophysiological signal classification

The Effect of Superstar Software on Hardware Sales in System Markets

Systems are composed of complementary products (e.g., video game systems are composed of the video game console and video games). Prior literature on indirect network effects argues that, in system markets, sales of the primary product (often referred to as "hardware") largely depend on the availability of complementary products (often referred to as "software"). Mathematical and empirical analyses have almost exclusively operationalized software availability as software quantity. However, while not substantiated with empirical evidence, case illustrations show that certain “superstar†software titles of very high quality (e.g., Super Mario 64) may have had disproportionately large effects on hardware unit sales (e.g., Nintendo N64 console sales). In the context of the U.S. home video game console market, we show that the introduction of a superstar increases video game console sales on average by 14%, over a period of 5 months. Software type does not consistently alter this effect. Our findings imply that scholars who study the relationship between software availability and hardware sales, need to account for superstar returns, and their decaying effect over time, over and above a mere software quantity effect. Hardware firms should maintain a steady flow of superstar introductions, as the positive effect of a superstar only lasts for 5 months, and make, if need be, side-payments to software firms, as superstars dramatically increase hardware sales. Obtaining exclusivity over superstars, by hardware firms, does not provide an extra boost to their own sales, but it does take away an opportunity for competing systems to increase their sales.indirect network effects;new product introductions;superstars;system markets;video game industry;software;hardware