spiking neural networks by

Neural Circuits This paper reviews a system capable of performing multiple cognitive functions using a combination of biologically plausible spiking neurons, and an architecture that mimics the organization, function, and representational resources used in the mammalian brain. By Terrence C. Stewart and Chris Eliasmith ABSTRACT | In this paper, we review the theoretical and software tools used to construct Spaun, the first (and so far only) brain model capable of performing cognitive tasks. This tool set allowed us to configure 2.5 million simple nonlinear components (neurons) with 60 billion connections between them (synapses) such that the resulting model can perform eight different perceptual, motor, and cognitive tasks. To reverse-engineer the brain in this way, a method is needed that shows how large numbers of simple components, each of which receives thousands of inputs from other components, can be organized to perform the desired computations. We achieve this through the neural engineering framework (NEF), a mathematical theory that provides methods for systematically generating biologically plausible spiking networks to imple-ment nonlinear and linear dynamical systems. On top of this, we propose the semantic pointer architecture (SPA), a hypoth-esis regarding some aspects of the organization, function, and representational resources used in the mammalian brain. We conclude by discussing Spaun, which is an example model that uses the SPA and is implemented using the NEF. Throughout, we discuss the software tool Neural ENGineering Objects (Nengo), which allows for the synthesis and simulation of neural models efficiently on the scale of Spaun, and provides support for constructing models using the NEF and the SPA. The resulting NEF/SPA/Nengo combination is a general tool set for both evaluating hypotheses about how the brain works, and for building systems that compute particular functions using neuron-like components. KEYWORDS | Neural computation; neural engineering frame-work (NEF); neural modeling; neuromorphic engineering; semantic pointer architecture (SPA); Spaun; spiking neural networks In this paper, we describe the methodology and tools we have developed for building large-scale systems from simulated spiking neurons. In particular, we describe two