How Do Neurons Look at the World?

W hen we consider the extraordinary abilities of our brains, we tend to focus on much-valued capabilities for the manipulation of abstract symbols and for the representation of the self. It is these capabilities that allow us to use language, do mathematics, create music, play chess, and maintain, as we do all this and much more, a unifi ed and continuous sense of who we are. As much as we delight in these features, most likely to characterize the human brain as somewhat unique within an evolutionary scale, a crucial function of our brains, as well as the brains of many other organisms, is to provide an interface with the external world. This interface has two fundamental components: the processing of sensory information and the control of movement. Sensory information is typically represented by the collective activity of large populations of neurons. Consider, for instance, neurons that fi re action potentials in response to visual inputs. The spiking activity of an individual neuron in this ensemble represents a reduced part of the visual world: the receptive fi eld. Within its receptive fi eld, the neuron is sensitive to the presence of a few specifi c features, such as an edge separating brightness from darkness, or an illuminated bar against a dark background. However, neurons are seldom all-or-nothing feature detectors; they respond not only to the presence or absence of features but also to their values. Neurons use their ability to produce graded responses by controlling the number of spikes they fi re, in order to encode the value of continuous features such as the location of an edge or the orientation of a bright bar. This ability to encode continuous features through graded responses leads to the concept of a tuning curve, which describes the average number of spikes fi red by a neuron in response to specifi c features of a visual stimulus. A population of neurons will therefore implement a distributed code in which each participating neuron responds best to certain feature values and less well to others. Each neuron will contribute to the ensemble information by responding to the visual input according to its own preferred values for the relevant features. Consider a visual input such as an illuminated bar: its location, orientation, and brightness are each described by a continuous variable, and n such variables are needed to describe n features. But neural responses are noisy: …