The MNIST dataset has become a standard benchmark for learning, classification and computer vision systems. Contributing to its widespread adoption are the understandable and intuitive nature of the task, the relatively small size and storage requirements and the accessibility and ease-of-use of the database itself. The MNIST database was derived from a larger dataset known as the NIST Special Database 19 which contains digits, uppercase and lowercase handwritten letters. This paper introduces a variant of the full NIST dataset, which we have called Extended MNIST (EMNIST), which follows the same conversion paradigm used to create the MNIST dataset. The result is a dataset that constitutes a more challenging classification task involving letters and digits, and one that shares the same image structure and parameters as the original MNIST task, allowing for direct compatibility with all existing classifiers and systems. Benchmark results using an online ELM algorithm are presented along with a validation of the conversion process through the comparison of the classification results on NIST digits and the MNIST digits.

Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain–machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.

In this paper, we present an analog integrated circuit containing a matched pair of silicon cochleae and an address event interface. Each section of the cochlea, modeled by a second-order low-pass filter, is followed by a simplified inner hair cell circuit and a spiking neuron circuit. When the neuron spikes, an address event is generated on the asynchronous data bus. We present the results of the chip characterization and the results of an interaural time difference based sound localization experiment using the address event representation (AER) EAR. The chip was fabricated in a 3-metal 2-poly 0.5-mum CMOS process

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. This article presents NeuroBench, a benchmark framework for neuromorphic algorithms and systems, which is collaboratively designed from an open community of researchers across industry and academia. NeuroBench introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent and hardware-dependent settings. For latest project updates, visit the project website (neurobench.ai).

This study measured the accuracy with which human listeners can localize spoken words. A broadband $(300\mathrm{Hz}–16\mathrm{kHz})$ corpus of monosyllabic words was created and presented to listeners using a virtual auditory environment. Localization was examined for 76 locations on a sphere surrounding the listener. Experiment 1 showed that low-pass filtering the speech sounds at $8\mathrm{kHz}$ degraded performance, causing an increase in polar angle errors associated with the cone of confusion. In experiment 2 it was found that performance in fact varied systematically with the level of the signal above $8\mathrm{kHz}$ . Although the lower frequencies (below $8\mathrm{kHz}$ ) are known to be sufficient for accurate speech recognition in most situations, these results demonstrate that natural speech contains information between 8 and $16\mathrm{kHz}$ that is essential for accurate localization.