The neural dynamics of feedforward and feedback interactions in predictive processing

Cortical areas are reciprocally coupled via feedforward (FF) and feedback (FB) connections that have distinct laminar profiles. Recurrent interactions between FF and FB streams may underlie context-dependent, flexible processing of sensory stimuli and the formation of predictions. Hierarchical predictive coding theories postulate that the communication of FF sensory input signals and FB predictions uses distinct frequency channels, namely gamma and alpha/beta-frequencies. Our review calls for an update of this dual-frequency theory: Empirical evidence suggests that both gamma and beta rhythms emerge during sensory and motor states characterized by high spatial and temporal predictability, in which case horizontal and top-down FB can explain away FF inputs. By contrast, broadband increases in gamma power for unpredicted stimuli likely result from increased firing rates. Recent evidence further indicates that distinct rhythms emerge in specific networks and are not consistently associated with either FF or FB influences, or with corresponding laminar compartments. Accordingly, we discuss potential functions and mechanisms of rhythms in the local circuit related to efficient coding and synaptic plasticity, rather than the inter-areal communication of FF error and FB prediction signals

Cell-type-specific propagation of visual flicker

Rhythmic flicker stimulation has gained interest as a treatment for neurodegenerative diseases and a method for frequency tagging neural activity in human EEG/MEG recordings. Yet, little is known about the way in which flicker-induced synchronization propagates across cortical levels and impacts different cell types. Here, we used Neuropixels to simultaneously record from LGN, V1, and CA1 while presenting visual flicker stimuli at different frequencies. LGN neurons showed strong phase locking up to 40Hz, whereas phase locking was substantially weaker in V1 units and absent in CA1 units. Laminar analyses revealed an attenuation of phase locking at 40Hz for each processing stage, with substantially weaker phase locking in the superficial layers of V1. Gamma-rhythmic flicker predominantly entrained fast-spiking interneurons. Optotagging experiments showed that these neurons correspond to either PV+ or narrow-waveform Sst+ neurons. A computational model could explain the observed differences in phase locking based on the neurons’ capacitative low-pass filtering properties. In summary, the propagation of synchronized activity and its effect on distinct cell types strongly depend on its frequency

Distinct roles of PV and Sst interneurons in visually-induced gamma oscillations

Sensory processing relies on interactions between excitatory and inhibitory neurons, which are often coordinated by 30-80Hz gamma oscillations. However, the specific contributions of distinct interneurons to gamma synchronization remain unclear. We performed high-density recordings from V1 in awake mice and used optogenetics to identify PV+ (Parvalbumin) and Sst+ (Somatostatin) interneurons. PV interneurons were highly phase-locked to visually-induced gamma oscillations. Sst cells were heterogeneous, with only a subset of narrow-waveform cells showing strong gamma phase-locking. Interestingly, PV interneurons consistently fired at an earlier phase in the gamma cycle (≈6ms or 60 degrees) than Sst interneurons. Consequently, PV and Sst activity showed differential temporal relations with excitatory cells. In particular, the 1st and 2nd spikes in burst events, which were strongly gamma phase-locked, shortly preceded PV and Sst activity, respectively. These findings indicate a primary role of PV interneurons in synchronizing excitatory cells and suggest that PV and Sst interneurons control the excitability of somatic and dendritic neural compartments with precise time delays coordinated by gamma oscillations

Self-relevance predicts the aesthetic appeal of real and synthetic artworks generated via neural style transfer

What determines the aesthetic appeal of artworks? Recent work suggests that aesthetic appeal can to some extent be predicted from a visual artwork’s image features. Yet, a large fraction of variance in aesthetic ratings remains unexplained and may relate to individual preferences. We hypothesized that an artwork’s aesthetic appeal depends strongly on self-relevance. In a first experiment, observers viewed real artworks and rated them for aesthetic appeal and self-relevance. Aesthetic appeal was positively predicted by self-relevance. In a second experiment, we developed a method to create synthetic, self-relevant artworks, by using deep neural networks that transferred the style of exist- ing artworks to photographs. Style transfer was applied to self-relevant photographs which were identified based on autobiographical memories, self-identity, interests, common activities and pref- erences. Self-relevant, synthetic artworks were rated as more aesthetically appealing than matched control images, at a level similar to real artworks. Thus, self-relevance is a key determinant of aesthetic appeal, independent of artistic skill and image features

Principles of large-scale neural interactions

What mechanisms underlie flexible inter-areal communication in the cortex? We consider four mechanisms for temporal coordination and their contribution to communication: (1) Oscillatory synchronization (communication-through-coherence); (2) communication-through-resonance; (3) non-linear integration; (4) linear signal transmission (coherence-through-communication). We discuss major challenges for communication-through-coherence based on layer- and cell-type-specific analyses of spike phase-locking, heterogeneity of dynamics across networks and states, and computational models for selective communication. We argue that resonance and non-linear integration are viable alternative mechanisms that facilitate computation and selective communication in recurrent networks. Finally, we consider communication in relation to cortical hierarchy, and critically examine the hypothesis that feedforward and feedback communication use fast (gamma) and slow (alpha/beta) frequencies, respectively. Instead, we propose that feedforward propagation of prediction errors relies on the non-linear amplification of aperiodic transients, whereas gamma and beta rhythms represent rhythmic equilibrium states that facilitate sustained and efficient information encoding and amplification of short-range feedback via resonance.This project was supported by ERC Starting Grant to MV (SPATEMP); a BMF Grant to MV (Bundesministerium fuer Bildung und Forschung, Computational Life Sciences, project BINDA, 031L0167); a BIAL Foundation Grant (ID: A-29477) to ACJ

Predictive coding of natural images by V1 firing rates and rhythmic synchronization

Predictive coding is an important candidate theory of self-supervised learning in the brain. Its central idea is that sensory responses result from comparisons between bottom-up inputs and contextual predictions, a process in which rates and synchronization may play distinct roles. We recorded from awake macaque V1 and developed a technique to quantify stimulus predictability for natural images based on self-supervised, generative neural networks. We find that neuronal firing rates were mainly modulated by the contextual predictability of higher-order image features, which correlated strongly with human perceptual similarity judgments. By contrast, V1 gamma (γ)-synchronization increased monotonically with the contextual predictability of low-level image features and emerged exclusively for larger stimuli. Consequently, γ-synchronization was induced by natural images that are highly compressible and low-dimensional. Natural stimuli with low predictability induced prominent, late-onset beta (β)-synchronization, likely reflecting cortical feedback. Our findings reveal distinct roles of synchronization and firing rates in the predictive coding of natural images

Data from: Surface color and predictability determine contextual modulation of V1 firing and gamma oscillations

The integration of direct bottom-up inputs with contextual information is a core feature of neocortical circuits. In area V1, neurons may reduce their firing rates when their receptive field input can be predicted by spatial context. Gamma-synchronized (30–80 Hz) firing may provide a complementary signal to rates, reflecting stronger synchronization between neuronal populations receiving mutually predictable inputs. We show that large uniform surfaces, which have high spatial predictability, strongly suppressed firing yet induced prominent gamma synchronization in macaque V1, particularly when they were colored. Yet, chromatic mismatches between center and surround, breaking predictability, strongly reduced gamma synchronization while increasing firing rates. Differences between responses to different colors, including strong gamma-responses to red, arose from stimulus adaptation to a full-screen background, suggesting prominent differences in adaptation between M- and L-cone signaling pathways. Thus, synchrony signaled whether RF inputs were predicted from spatial context, while firing rates increased when stimuli were unpredicted from context

Dataset 3

Please see readme and description of dataset 3 in publication

Dataset 4

Please see readme and description of dataset 4 in publication