Attractors in Song

Abstract

This paper summarizes our recent attempts to integrate action and perception within a single optimization framework. We start with a statistical formulation of Helmholtz's ideas about neural energy to furnish a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring the causes of our sensory inputs and learning regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain's organization and responses. We will demonstrate the brain-like dynamics that this scheme entails by using models of birdsongs that are based on chaotic attractors with autonomous dynamics. This provides a nice example of how non-linear dynamics can be exploited by the brain to represent and predict dynamics in the environment.

Repetition suppression and its contextual determinants in predictive coding

“…The effects of stimulus probability, on the other hand, might rely on hierarchically higher expectations about the sequence structure or likelihood of stimuli, induced by learning of the statistical regularities of the sequence. This pattern of results is consistent with a cascade of prediction errors that update predictions at progressively higher levels of the processing hierarchy, as reflected in the hierarchically distinct generators of the early and late components of the evoked response ( Friston and Kiebel, 2009 , Garrido et al., 2009b ). Interestingly, at even later latencies (200–500 msec) repetition and stimulus probability showed an interaction effect, replicating the modulatory effects of expectation observed in fMRI.…”

“…Many known characteristics of repetition suppression emerge in simulations of predictive coding. This has been previously shown in a formal model of an artificial brain perceiving sequences of sensory events, simulated using attractor dynamics ( Friston & Kiebel, 2009 ) ( Fig. 3 ).…”

“… Simulating brain-like dynamics to perform perceptual inference and categorisation . The predictive coding scheme was used to simulate brain-like dynamics in an artificial avian brain ( Friston & Kiebel, 2009 ). The goal of the agent was to perceive and categorise streams of auditory input, an example of which is shown in the lower inset.…”

Evaluating the neurophysiological evidence for predictive processing as a model of perception

“…Although this represents a considerable challenge using conventional paradigms and neurophysiological techniques, methodological advances in highresolution fMRI, optogenetics, calcium imaging, and serial single-unit recordings at multiple levels of the processing hierarchy are providing powerful new opportunities to trace neural markers of hierarchical PP dynamics. 41,128,158,196,209,210 Pairing these increasingly sophisticated neural assays with anatomical models, computational modeling, and simulations 137,211 will enable researchers to derive fine-grained a priori hypotheses and compare model evidence for variant architectures and also for near-variant ones that share much with the core PP picture but differ in their conceptions of the encoding, flow, or use of prediction errors (e.g., Refs. 2, 10 and 11; see Ref.…”

Modelling Trial-by-Trial Changes in the Mismatch Negativity

“…This representation is not motivated by sparseness, but by computational efficiency: It replaces the problem of computing the (potentially very high-dimensional) posterior probability density by optimizing the free-energy with respect to a small set of sufficient statistics. This variational Bayesian optimization rests on free-energy minimization [37] and proposes the minimization of prediction error as an explanation for stimulus-evoked transient neuronal responses such as the MMN [3], [63], [81]. The work presented in this paper is a step towards linking models of probabilistic neural coding and inference to neuronal signals that can be measured non-invasively in humans.…”