A central challenge for cognitive and systems neuroscience is to relate the incredibly complex behavior of animals to the equally complex activity of their brains. Recently described, large-scale neural models have not bridged this gap between neural activity and biological function. In this work, we present a 2.5-million-neuron model of the brain (called "Spaun") that bridges this gap by exhibiting many different behaviors. The model is presented only with visual image sequences, and it draws all of its responses with a physically modeled arm. Although simplified, the model captures many aspects of neuroanatomy, neurophysiology, and psychological behavior, which we demonstrate via eight diverse tasks.

Neuroscience currently lacks a comprehensive theory of how cognitive processes can be implemented in a biological substrate. The Neural Engineering Framework (NEF) proposes one such theory, but has not yet gathered significant empirical support, partly due to the technical challenge of building and simulating large-scale models with the NEF. Nengo is a software tool that can be used to build and simulate large-scale models based on the NEF; currently, it is the primary resource for both teaching how the NEF is used, and for doing research that generates specific NEF models to explain experimental data. Nengo 1.4, which was implemented in Java, was used to create Spaun, the world's largest functional brain model (Eliasmith et al., 2012). Simulating Spaun highlighted limitations in Nengo 1.4's ability to support model construction with simple syntax, to simulate large models quickly, and to collect large amounts of data for subsequent analysis. This paper describes Nengo 2.0, which is implemented in Python and overcomes these limitations. It uses simple and extendable syntax, simulates a benchmark model on the scale of Spaun 50 times faster than Nengo 1.4, and has a flexible mechanism for collecting simulation results.

The stiffness and tensile strength of biopolymers (e.g., polylactic acid (PLA)) are less than desirable for load-bearing applications in their neat form. The use of natural fibers as reinforcements for composites (for large-scale three-dimensional (3D) printing) has expanded rapidly, attributable to their low weight, low cost, high stiffness, and renewable nature. Silane and acid/alkali are typically used to modify the surface of natural fibers to improve the fiber/polymer interfacial adhesion. In this study, a simple method of impregnation was developed to modify pine fibers (loblolly, mesh size of 90–180 μm, 30 wt %) with a solvent-borne epoxy to reinforce PLA. As a benefit of the epoxy modification (0.5–10 wt %), the tensile strengths and Young’s moduli of the epoxy-modified pine/PLA composites increased by up to 20 and 82%, respectively, as compared to that of neat PLA. The epoxy-modified pine/PLA composites, with an optimum epoxy modification (1.0 wt %), had fewer voids on the fracture surface as compared with pine/PLA composites without the modification of pine fibers via epoxy. Results confirmed that epoxy partially penetrated the pore/hollow inner structures of pine fibers and improved the fiber/matrix interfacial adhesion. Epoxy modification is found to be a simple and effective technique to improve the properties of biocomposites.

Inductive reasoning is a fundamental and complex aspect of human intelligence. In particular, how do subjects, given a set of particular examples, generate general descriptions of the rules governing that set? We present a biologically plausible method for accomplishing this task and implement it in a spiking neuron model. We demonstrate the success of this model by applying it to the problem domain of Raven's Progressive Matrices, a widely used tool in the field of intelligence testing. The model is able to generate the rules necessary to correctly solve Raven's items, as well as recreate many of the experimental effects observed in human subjects.

NengoDL is a software framework designed to combine the strengths of neuromorphic modelling and deep learning. NengoDL allows users to construct biologically detailed neural models, intermix those models with deep learning elements (such as convolutional networks), and then efficiently simulate those models in an easyto-use, unified framework. In addition, NengoDL allows users to apply deep learning training methods to optimize the parameters of biological neural models. In this paper we present basic usage examples, benchmarking, and details on the key implementation elements of NengoDL. More details can be found at https://www.nengo.ai/nengo-dl.