Scientific Understanding of Consciousness
Nature Neuroscience, published online 22 October 2006 one
Bayesian inference with probabilistic population codes
Wei Ji Ma1'3, Jeffrey M Beck1'3, Peter E Latham2 & Alexandre Pongee
1Department of Brain and Cognitive Sciences, Meliora Hall, University of Rochester, Rochester, New York 14627, USA. 2Gatsby Computational Neuroscience Unit, 17 Queen Square, London WC1N 3AR, UK. 3
Recent psychophysical experiments indicate that humans perform near-optimal Bayesian inference in a wide variety of tasks, ranging from cue integration to decision making to motor control. This implies that neurons both represent probability distributions and combine those distributions according to a close approximation to Bayes' rule. At first sight, it would seem that the high variability in the responses of cortical neurons would make it difficult to implement such optimal statistical inference in cortical circuits. We argue that, in fact, this variability implies that populations of neurons automatically represent probability distributions over the stimulus, a type of code we call probabilistic population codes. Moreover, we demonstrate that the Poisson-like variability observed in cortex reduces a broad class of Bayesian inference to simple linear combinations of populations of neural activity. These results hold for arbitrary probability distributions over the stimulus, for tuning curves of arbitrary shape and for realistic neuronal variability.
Virtually all computations performed by the nervous system are subject to uncertainty, and taking this into account is critical for making inferences about the outside world. For instance, imagine hiking in a forest and having to jump over a stream. To decide whether or not to jump, you could compute the width of the stream and compare it to your internal estimate of your jumping ability. If, for example, you can jump 2 m and the stream is 1.9 m wide, then you might choose to jump. The problem with this approach, of course, is that you ignored the uncertainty in the sensory and motor estimates. If you can jump 2 ± 0.4 m and the stream is 1.9 ± 0.5 m wide, jumping over it is very risky—and even life-threatening if it is filled with, say, piranhas.
Behavioral studies have confirmed that human observers not only take uncertainty into account in a wide variety of tasks, but do so in a way that is nearly optimal" (where 'optimal' is used in a Bayesian sense). This has two important implications. First, neural circuits must represent probability distributions. For instance, in our example, the width of the stream could be represented in the brain by a Gaussian distribution with mean 1.9 m and s.d. 0.5 m. Second, neural circuits must be able to combine probability distributions nearly optimally, a process known as Bayesian inference.
Although it is clear experimentally that human behavior is nearly Bayes-optimal in a wide variety of tasks, very little is known about the neural basis of this optimality. In particular, we do not know how probability distributions are represented in neuronal responses, nor how neural circuits implement Bayesian inference. At first sight, it would seem that cortical neurons are not well suited to this task, as their responses are highly variable: the spike count of cortical neurons in response to the same sensory variable (such as the direction of motion of a visual stimulus) or motor command varies greatly from trial to trial, typically with Poisson-like statistics. It is critical to realize, however, that variability and uncertainty go hand in hand: if neuronal variability did not exist, that is, if neurons were to fire in exactly the same way every time you saw the same object, then you would always know with certainty what object was presented. Thus, uncertainty about the width of the river in the above example is intimately related to the fact that neurons in the visual cortex do not fire in exactly the same way every time you see a river that is 2 m wide. This variability is partly due to internal noise (like stochastic neurotransmitter release), but the potentially more important component arises from the fact that rivers of the same width can look different, and thus give rise to different neuronal responses, when viewed from different distances or vantage points.
Neural variability, then, is not incompatible with the notion that humans can be Bayes-optimal; on the contrary, as we have just seen, neural variability is expected when subjects experience uncertainty. What it not clear, however, is exactly how optimal inference is achieved given the particular type of noise—Poisson-like variability—observed in the cortex. Here we show that Poisson-like variability makes a broad class of Bayesian inferences particularly easy. Specifically, this variability has a unique property: it allows neurons to represent probability distributions in a format that reduces optimal Bayesian inference to simple linear combinations of neural activities.
Our notion of probabilistic population codes offers a new perspective on the role of Poisson-like variability. The presence of such variability throughout the cortex suggests that the entire cortex represents probability distributions, not just estimates, which is precisely what would be expected from a Bayesian perspective. We propose that these distributions are collapsed onto estimates only when decisions are needed, a process that may take place in motor cortex or in subcortical structures. Notably, our previous work shows that attractor dynamics in these decision networks could perform this step optimally by computing maximum a posteriori estimates.