Scientific Understanding of Consciousness
Consciousness as an Emergent Property of Thalamocortical Activity

Model of the Functioning Brain



Science 30 November 2012: Vol. 338 no. 6111 pp. 1202-1205

A Large-Scale Model of the Functioning Brain

Chris Eliasmith, Terrence C. Stewart, Xuan Choo, Trevor Bekolay, Travis DeWolf, Yichuan Tang, Daniel Rasmussen

Centre for Theoretical Neuroscience, University of Waterloo, Waterloo, ON N2J 3G1, Canada.


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.

Although impressive scaling has been achieved, no previous large-scale spiking neuron models have demonstrated how such simulations connect to a variety of specific observable behaviors. The focus of this past work has been on scaling to larger numbers of neurons and more detailed neuron models. Unfortunately, simulating a complex brain alone does not address one of the central challenges for neuroscience: explaining how complex brain activity generates complex behavior. In contrast, we present here a spiking neuron model of 2.5 million neurons that is centrally directed to bridging the brain-behavior gap. Our model embodies neuroanatomical and neurophysiological constraints, making it directly comparable to neural data at many levels of analysis. Critically, the model can perform a wide variety of behaviorally relevant functions. We show results on eight different tasks that are performed by the same model, without modification.

All inputs to the model are 28 by 28 images of handwritten or typed characters. All outputs are the movements of a physically modeled arm that has mass, length, inertia, etc. For convenience, we refer to the model as “Spaun” (Semantic Pointer Architecture Unified Network). Many of the tasks we have chosen are the subject of extensive modeling in their own right [e.g., image recognition, serial working memory (WM), and reinforcement learning (RL), and others] demonstrate abilities that are rare for neural network research and have not yet been demonstrated in spiking networks

The network implementing the Spaun model consists of three compression hierarchies, an action-selection mechanism, and five subsystems. Components of the model communicate using spiking neurons that implement neural representations that we call “semantic pointers,” using various firing patterns.

The specific dynamics of Spaun’s responses to a wide variety of tasks is governed by four parameters, each of which is set empirically (the time constants of the neurotransmitters); Thus, without fitting, the model is consistent with dynamics from single cells and behavior and is able to switch between a wide variety tasks quickly and robustly.

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