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Simulation of a spiking neural network involves solving a large number of differential equations. This is a challenge even for modern computer systems, especially when simulating large-scale neural networks. To address this challenge, we design a neuron model: the Integer Quadratic Integrate-and-Fire (IQIF) neuron. Instead of computing on floating point numbers, as is typical with other spiking neuron models, the IQIF model is computed purely on integers. The IQIF model is a quantized and linearized version of the classic quadratic integrate-and-fire (QIF) model. The IQIF model retains all dynamic characteristics of the QIF model with much lower computation complexity, at the cost of a limited dynamic range of the membrane potential and the synaptic current. We compare IQIF to other spiking neuron models based on their simulation speeds and the number of neuronal behaviors they can perform. We further compare the performance of IQIF with the leaky integrate-and-fire model in a classical decision-making network that exhibits nonlinear attractor dynamics. Our results show that the IQIF neurons are capable of performing computation that other spiking neuron models can do while having the advantages of speed. Moreover, the IQIF model is digital hardware friendly due to its pure integer operation and is therefore easily to be implemented in custom-built neuromorphic systems.

Optical flow is the pattern of apparent motion in a visual scene produced by the relative movement between objects and an observer. Optical flow is used in many engineering applications such as optical odometry. A variety of optical-flow algorithms has been proposed in the past few decades; however, most of these algorithms involve complex computation, making them difficult to be implemented in neuromorphic systems that operate based on neural networks. Interestingly, studies have shown that insect visual systems are able to perform complex optical flow algorithms. Inspired by the classic Reichardt motion detection model proposed for insects, we designed a spatial-temporal filtering Reichardt (STR) model. This model computes optical flow based on simple filters in the spatial and temporal domains. The STR model is hardware friendly: it does not require time-consuming iteration processes nor computationally intensive multi-layer convolutional networks, which are typical in other optical flow algorithms. We systematically investigate the performance of the STR model with different parameters including: object size, speed, luminance, and filter forms. We also compare the performance of the STR model to the classical Farneback algorithm, and we demonstrate that the STR model is comparable to the classical algorithms while requiring much less computational power.

The inner operations of the human brain as a biological processing system remain largely a mystery. Inspired by the function of the human brain and based on the analysis of simple neural network systems in other species, such as Drosophila, neuromorphic computing systems have attracted considerable interest. In cellular-level connectomics research, we can identify the characteristics of biological neural network, called population, which constitute not only recurrent fully- connection in network, also an external-stimulus and self- connection in each neuron. Relying on low data bandwidth of spike transmission in network and input data, Spiking Neural Networks exhibit low-latency and low-power design. In this study, we proposed a configurable population-based digital spiking neuromorphic processor in 180nm process technology with two configurable hierarchy populations. Also, these neurons in the processor can be configured as novel models, integer quadratic integrate-and-fire neuron models, which contain an unsigned 8-bit membrane potential value. The processor can implement intelligent decision making for avoidance in real-time. Moreover, the proposed approach enables the developments of biomimetic neuromorphic system and various low-power, and low-latency inference processing applications with normalized energy efficiency of 13.2 pJ/SOP.

Energy-efficient always-on motion-detection (MD) sensors are in high demand and are widely used in machine vision applications. To achieve real-time and continuous motion monitoring, high-speed low-power temporal difference imagers with corresponding processing architectures are widely investigated [1–6]. Event-based dynamic vision sensors (DVS) have been reported to reduce the redundant data and power through the asynchronous timestamped event-address readout [1], [2]. However, DVS needs special data processing to collect enough events for information extraction, and suffers from noise and dynamic effects, which limits the advantages of low-latency pixel event reporting. Furthermore, low sensitivity (no integration) and lack of static information are also drawbacks of DVS. Frame-based MD rolling-shutter sensors [3], [4] were reported to reduce the data bandwidth and power by sub-sampling operation with the tradeoff of low resolution and motion blur. Global-shutter MD sensors were reported [5], [6] using in-pixel analog memory for reference image storage. However, such sensors require a special process technology for low off-state current device implementation. In a frame-based MD sensor, the required analog processing circuit and two successive frames for temporal difference operation comes at a cost in power, area, and speed. To address these drawbacks, we present a frame-based MD vision sensor featuring three operation modes: image-capture (IC), frame-difference (FD) with on/off event detection, and saliency-detection (SD). Using a low-voltage ping-pong PWM pixel and multi-mode operation, it achieves high-speed low-power full-resolution MD, consecutive event frame reporting, and image capture functions. Moreover, saliency detection by counting the block-level event number is also implemented for efficient optic flow extraction of the companion processing chip using simple neuromorphic circuits.

Drosophila melanogaster is one of the most important model animals in neurobiology owing to its manageable brain size, complex behaviour, and extensive genetic tools. However, without a comprehensive map of the brain-wide neural network, our ability to investigate brain functions at the systems level is seriously limited. In this study, we constructed a neuron-to-neuron network of the Drosophila brain based on the 28,573 fluorescence images of single neurons in the newly released FlyCircuit v1.2 (http://www.flycircuit.tw) database. By performing modularity and centrality analyses, we identified eight communities (right olfaction, left olfaction, olfactory core, auditory, motor, pre-motor, left vision, and right vision) in the brain-wide network. Further investigation on information exchange and structural stability revealed that the communities of different functions dominated different types of centralities, suggesting a correlation between functions and network structures. Except for the two olfaction and the motor communities, the network is characterized by overall small-worldness. A rich club (RC) structure was also found in this network, and most of the innermost RC members innervated the central complex, indicating its role in information integration. We further identified numerous loops with length smaller than seven neurons. The observation suggested unique characteristics in the information processing inside the fruit fly brain.

This study examined the returns on the Taiwan Capitalization Weighted Stock Index (TAIEX) and NIKKEI Stock Average Index (NIKKEI) and explored the volatility spillover effect between the Taiwanese and Japanese stock market. The results revealed cointegration between the two indices, suggesting a long-term, stable relationship between the two stock markets. An examination of inner-market effects showed that the returns on stock indices in both markets are greatly influenced by the returns of previous time periods. Additionally, a cross-market effect investigation showed that past returns on NIKKEI were found to affect the current returns on TAIEX significantly, while the past returns on TAIEX had no impact on the current returns on NIKKEI. A volatility analysis revealed the existence of an inner-market leverage effect, a negative cross-market volatility spillover effect, and a mutual price leading effect. According to the relative asymmetry analysis results, the two stock markets are more sensitive to falling than rising trends in the counterpart market. These results suggest that the two markets are more likely to crash due to a retreat in the counterpart market. The impact of previous volatility shocks on the current volatility of TAIEX and NIKKEI are 46.44 and 6.98 days, respectively.

A hallmark of flexible behavior is the brain's ability to dynamically adjust speed and accuracy in decision-making. Recent studies suggested that such adjustments modulate not only the decision threshold, but also the rate of evidence accumulation. However, the underlying neuronal-level mechanism of the rate change remains unclear. In this work, using a spiking neural network model of perceptual decision, we demonstrate that speed and accuracy of a decision process can be effectively adjusted by manipulating a top-down control signal with balanced excitation and inhibition [balanced synaptic input (BSI)]. Our model predicts that emphasizing accuracy over speed leads to reduced rate of ramping activity and reduced baseline activity of decision neurons, which have been observed recently at the level of single neurons recorded from behaving monkeys in speed-accuracy tradeoff tasks. Moreover, we found that an increased inhibitory component of BSI skews the decision time distribution and produces a pronounced exponential tail, which is commonly observed in human studies. Our findings suggest that BSI can serve as a top-down control mechanism to rapidly and parametrically trade between speed and accuracy, and such a cognitive control signal presents both when the subjects emphasize accuracy or speed in perceptual decisions.

SummaryThe Fruit Fly Brain Observatory (FFBO) is a collaborative effort between experimentalists, theorists and computational neuroscientists at Columbia University, National Tsing Hua University and Sheffield University with the goal to (i) create an open platform for the emulation and biological validation of fruit fly brain models in health and disease, (ii) standardize tools and methods for graphical rendering, representation and manipulation of brain circuits, (iii) standardize tools for representation of fruit fly brain data and its abstractions and support for natural language queries, (iv) create a focus for the neuroscience community with interests in the fruit fly brain and encourage the sharing of fruit fly brain structural data and executable code worldwide. NeuroNLP and NeuroGFX, two key FFBO applications, aim to address two major challenges, respectively: i) seamlessly integrate structural and genetic data from multiple sources that can be intuitively queried, effectively visualized and extensively manipulated, ii) devise executable brain circuit models anchored in structural data for understanding and developing novel hypotheses about brain function. NeuroNLP enables researchers to use plain English (or other languages) to probe biological data that are integrated into a novel database system, called NeuroArch, that we developed for integrating biological and abstract data models of the fruit fly brain. With powerful 3D graphical visualization, NeuroNLP presents a highly accessible portal for the fruit fly brain data. NeuroGFX provides users highly intuitive tools to execute neural circuit models with Neurokernel, an open-source platform for emulating the fruit fly brain, with full data support from the NeuroArch database and visualization support from an interactive graphical interface. Brain circuits can be configured with high flexibility and investigated on multiple levels, e.g., whole brain, neuropil, and local circuit levels. The FFBO is publicly available and accessible at http://fruitflybrain.org from any modern web browsers, including those running on smartphones.