The secret life of predictive brains: what's spontaneous activity for?

Abstract

Brains at rest generate dynamical activity that is highly structured in space and time. We suggest that spontaneous activity, as in rest or dreaming, underlies top-down dynamics of generative models. During active tasks, generative models provide top-down predictive signals for perception, cognition, and action. When the brain is at rest and stimuli are weak or absent, top-down dynamics optimize the generative models for future interactions by maximizing the entropy of explanations and minimizing model complexity. Spontaneous fluctuations of correlated activity within and across brain regions may reflect transitions between 'generic priors' of the generative model: low dimensional latent variables and connectivity patterns of the most common perceptual, motor, cognitive, and interoceptive states. Even at rest, brains are proactive and predictive.

Keywords: generative models; predictive brains; resting state; spontaneous activity.

Figure 1.. Two examples of spontaneous brain dynamics.

(A) An example of resting state spontaneous activity measured with fMRI. Static connectivity: a map of the ’static’ (time-averaged) temporal correlation of the blood oxygenation level dependent (BOLD) signal between a region in medial parietal cortex (black arrowhead), and the rest of the cortex over many minutes. Inset: fluctuations of the BOLD signal in two cortical networks: DMN-default mode network, and DAN-dorsal attention network. Dynamic connectivity: the maps represent the main patterns of correlated activity across the brain as identified through a sliding window analysis, a winner-take-all classification, and the projection on the cortical surface of the first eigenvector. The fluctuations (time-course) have a frequency of about 1/10 seconds (0.1 Hz). Low frequency activity is also evident in single cell recordings from rat neocortex, where they appear to encode behaviorally relevant information (Figure 2A).(B) A schematic illustration of internally generated hippocampal sequences. The middle part of the figure shows a (fictive) spatiotemporal sequence of spikes from seven hippocampal place cells (represented by different colors), whose place fields are located in different portions of the corridor. These sequences are visible within the hippocampal theta rhythm, while the animal navigates through the corridor. However, sequential activity from the same ensemble – sometimes in the same (or reverse) order as during navigation – can be decoded during animal sleep or awake rest before ("preplays") and after ("replays") navigation, respectively. These internally generated hippocampal sequences are often embedded in network events called sharp-wave ripple (SWR) complexes [–45].

Figure 2.. Supporting evidence and schematic of the novel proposal.

(A) Similarity of spontaneous activity in visual cortex and low dimensional behavioral components of mouse movements. Calcium imaging recordings of 10,000 neurons from mouse visual cortex during natural exploration (from courtesy of Matteo Carandini). Facial movements are summarized by a few patterns of correlated movements of whiskers, facial expressions, eye movements, and pupil area. Bottom shows raster plots of activity in visual cortex without visual stimulation (spontaneous). Note slow correlated fluctuations (~1/10 seconds). The intermediate panel shows how the first principal component of activity (violet timeseries) correlates with behavioral components (green timeseries). (B) Spontaneously emerging patterns in human visual cortex and their functional connectivity are linked to the patterns evoked by visual stimuli. Visual objects (faces, scenes, bodies, words) produce multivariate patterns of activity recorded with fMRI in visual association cortex. The left inset shows the multivariate pattern in a scene-processing region of human cortex. At rest, scene specific patterns, i.e. yielding strong spatial correlation with the task pattern—see time-course of correlation values—occur more frequently than patterns representing other objects or null patterns. The same occur in other regions of visual cortex, e.g. more face patterns at rest in face specific regions and so on. Critically, object specific patterns emerge at rest in a synchronized manner across multiple regions [77]. (C) Resting networks as connectional priors of task networks, from [107]. Reorganization of cortical regions and networks when observers go from visual fixation (rest) to movie watching. Top: Spring embedded representation of the temporal correlation strength of the BOLD fMRI signal between individual regions of RSNs according to the parcellation of [129]. Note that networks that are segregated at rest combine during tasks. Bottom: data driven hierarchical clustering. Two large resting communities (task-positive, task-negative) split in multiple communities during movie watching. (D) Schematic illustration of the novel hypothesis. Brain generative models continuously incorporate the statistical history of brain co-activation patterns experienced during behavioral experience. The endogenous regeneration of brain co-activation patterns at rest supports the offline optimization (e.g., compression) of the brain‘s generative model, and the preparation of "generic spatiotemporal priors" for future tasks. These comprise both representations and connectivity patterns; and become apparent as transitions between low-dimensional states of the network. These are shown in the right part of the figure a series of brain co-activation patterns (which possibly average across multiple episodes) nested within low-frequency fluctuations of the resting brain. The inset suggests that a large variety of task representations can be summarized in low dimensional states.

Figure 3.. Hierarchical generative neural networks.

(A) Deep Boltzmann Machines (DBMs) are deep neural networks of symmetrically coupled stochastic neurons organized in a hierarchy of multiple layers. The DBM learns a hierarchical generative model of the input (e.g., sensory) data presented on the "visible layer". "Hidden layers" contain neurons that encode latent causes of the data: when trained on images (here, handwritten digits) they become tuned to visual features that are increasingly more complex in deeper layers (examples of receptive fields of individual neurons are shown in the right panels [130]). Learning is unsupervised (i.e., it does not require external teaching or reward signal): its objective is to learn a probability distribution that approximates the true probability distribution of the training data. Recurrent connections convey the information sampled from the upper layers downstream to generate data on the visible layer, in a top-down fashion. The divergence between real input and its top-down reconstruction drives the change of connection weights during learning, using Hebbian rules. After learning, recurrent interactions support stochastic inference that leads to denoising, completion or "filling in" of ambiguous (or missing) inputs, in the same sensory modality or in different modalities if the architecture is multimodal (e.g., learns visual and linguistic inputs). Discriminative tasks (here, digit classification) can be learned by adding a layer of neurons representing the class labels. During learning, the model acquires "generic priors": here, prototypical digit shapes that abstract away from many input details [57,130]. (B) After learning, sampling can be conditioned by the class labels to generate prototypical digit shapes "spontaneously", i.e., in the absence of sensor inputs.