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Although neurons are biological entities, the term neural network has come to be used as a shorthand for artificial neural network, a class of models of parallel information processing that is inspired by biological neural networks but commits to several further major simplifications.Īlthough spiking models have an important place in the computational literature, the models discussed here are nonspiking and do not capture dendritic computation, other processes within each neuron (e.g., Gallistel & King 2011), and distinct contributions from different types of neurons. Note that this paradigm leaves aside many biological complexities, including functional contributions of neurochemical diffusion processes, glial cells, and hemodynamics ( Moore & Cao 2008). More broadly, the term evokes a particular paradigm for understanding brain function, in which neurons are the essential computational units, and computation is explained in terms of network interactions. The term neural network originally refers to a network of biological neurons. Now deep neural networks provide a framework for engaging complex cognitive tasks and predicting both brain and behavioral responses. At this level of rigor, however, we have not been able to engage complex real-world computational challenges and higher-level brain representations. Computational neuroscience uses fully explicit and biologically plausible computational models to predict neurophysiological and behavioral data.

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Our hands full with the new challenges of analyzing complex brain imaging data, however, our theoretical sophistication slipped back to the stage of cognitive psychology, and we began (perhaps reasonably) by mapping box-and-arrow models onto brain regions. Cognitive neuroscience brought neurophysiological data into investigations of complex brain information processing. As a result, neural networks did not initially live up to their promise as AI systems, and in cognitive science, modeling was restricted to toy problems. However, neural network technology was not sufficiently advanced to take on real-world tasks such as object recognition from photographs. Connectionism within cognitive science offered a neurobiologically plausible computational framework. However, it lacked constraints from neurophysiological data, making it difficult to adjudicate between multiple alternative models consistent with the behavioral data. Cognitive science made information processing theory fully explicit.

However, it lacked fully explicit computational models. Cognitive psychology attempted to illuminate behaviorism's black box with theories of information processing. Each field combined a different set of elements required for understanding how the brain works ( Table 1). The cognitive and brain sciences have gone through a sequence of transformations, with different fields dominating each period. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. However, the current models are designed with engineering goals, not to model brain computations. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Human-level visual recognition abilities are coming within reach of artificial systems. Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications.

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