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

Brain’s Internal Models of its Environment


Science 7 January 2011: Vol. 331 no. 6013 pp. 83-87

Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment

Pietro Berkes1, Gergő Orbán1,2,3, Máté Lengyel3 and József Fiser1

1Volen National Center for Complex Systems, Brandeis University, Waltham, MA 02454, USA.

2Department of Biophysics, Research Institute for Particle and Nuclear Physics, Hungarian Academy of Sciences, H-1121 Budapest, Hungary.

3Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK.

4Department of Psychology and the Neuroscience Program, Brandeis University, Waltham, MA 02454, USA.

5Collegium Budapest Institute for Advanced Study, Szentháromság utca 2, Budapest H-1014, Hungary.



The brain maintains internal models of its environment to interpret sensory inputs and to prepare actions. Although behavioral studies have demonstrated that these internal models are optimally adapted to the statistics of the environment, the neural underpinning of this adaptation is unknown. Using a Bayesian model of sensory cortical processing, we related stimulus-evoked and spontaneous neural activities to inferences and prior expectations in an internal model and predicted that they should match if the model is statistically optimal. To test this prediction, we analyzed visual cortical activity of awake ferrets during development. Similarity between spontaneous and evoked activities increased with age and was specific to responses evoked by natural scenes. This demonstrates the progressive adaptation of internal models to the statistics of natural stimuli at the neural level.

Our percepts rely on an internal model of the environment, relating physical processes of the world to inputs received by our senses, and thus their veracity critically hinges upon how well this internal model is adapted to the statistical properties of the environment. For example, internal models in vision are used to extract the features, such as low-level oriented edges or high-level objects, that gave rise to the retinal image. This requires that the internal model is adapted to the cooccurrence statistics of visual features in the environment and the way they jointly determine natural images. Several aspects of perception, motor control, decision making, and higher cognitive reasoning are governed by such statistically optimal internal models. Yet identifying the neural correlates of optimal internal models has remained a challenge.

We addressed this problem by relating evoked and spontaneous neural activity (EA and SA, respectively) to two key aspects of Bayesian computations performed with the internal model. The first key aspect is that a statistically optimal internal model needs to represent its inferences as a probability distribution, the Bayesian posterior P(features|input, model) describing the inferred probability that a particular combination of features may underlie the input. Thus, under the general assumption that the visual cortex implements such an optimal internal model, EA should represent the posterior probability distribution for a given input image, and SA should represent the posterior distribution elicited by a blank stimulus. The second key aspect of a statistically optimal internal model, under only mild assumptions about its structure, is that the posterior represented by SA converges to the prior distribution, which describes prior expectations about the frequency with which any given combination of features may occur in the environment, P(features|model). This is because as the brightness or contrast of the visual stimulus is decreased, inferences about the features present in the input will be increasingly dominated by these prior expectations (for a formal derivation, see supporting online text). This effect has been demonstrated in behavioral studies, and it is also consistent with data on neural responses in the primary visual cortex (V1). Relating EA and SA to the posterior and prior distributions provides a complete, data-driven characterization of the internal model without making strong theoretical assumptions about its precise nature.

Our results suggest that V1 implements an internal model that is adapted gradually during development to the statistical structure of the natural visual environment and that SA reflects prior expectations of this internal model. Although these findings do not address the degree to which statistical adaptation in the cortex is driven by visual experience or by developmental programs, they set useful constraints for both dynamical and functional models of sensory processing. We expect our approach to extend to other brain areas and to provide a general, quantitative way to test future proposals for computational strategies used by the cortex.

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