Layer and rhythm specificity for predictive routing

In predictive coding, experience generates predictions that attenuate the feeding forward of predicted stimuli while passing forward unpredicted “errors.” Different models have suggested distinct cortical layers, and rhythms implement predictive coding. We recorded spikes and local field potentials from laminar electrodes in five cortical areas (visual area 4 V4], lateral intraparietal LIP], posterior parietal area 7A, frontal eye field FEF], and prefrontal cortex PFC]) while monkeys performed a task that modulated visual stimulus predictability. During predictable blocks, there was enhanced alpha (8 to 14 Hz) or beta (15 to 30 Hz) power in all areas during stimulus processing and prestimulus beta (15 to 30 Hz) functional connectivity in deep layers of PFC to the other areas. Unpredictable stimuli were associated with increases in spiking and in gamma-band (40 to 90 Hz) power/connectivity that fed forward up the cortical hierarchy via superficial-layer cortex. Power and spiking modulation by predictability was stimulus specific. Alpha/beta power in LIP, FEF, and PFC inhibited spiking in deep layers of V4. Area 7A uniquely showed increases in high-beta (∼22 to 28 Hz) power/connectivity to unpredictable stimuli. These results motivate a conceptual model, predictive routing. It suggests that predictive coding may be implemented via lower-frequency alpha/beta rhythms that “prepare” pathways processing-predicted inputs by inhibiting feedforward gamma rhythms and associated spiking.

The brain exploits predictability. It makes cortical processing more efficient. Visuomotor integration, visual/auditory speech perception, and visual perception all benefit when sensory inputs are predictable (1–3). The brain has an arsenal of mechanisms to tamp down and improve processing of familiar, repeated, or predictable inputs. One example is stimulus-specific adaptation. All over cortex, there is less spiking and smaller blood-oxygen-level-dependent (BOLD) responses when a stimulus is repeated (4–9). Responsiveness is recovered if the stimulus is changed or a pattern is violated (i.e., to “oddballs”) (10, 11). This can lead to fewer activated neurons but finer-tuned, more robust representations (8).But the brain does more than adapt to repeated inputs. A wide variety of evidence indicates that it makes mental models of the world that actively generate predictions, a process known as predictive coding (12–14). Moment-to-moment predictions are used to inhibit processing of expected inputs which, because they were expected, are not informative. Unexpected sensory inputs that deviate from a prediction, are “prediction errors” (PEs). They are informative and thus not inhibited, fed forward, processed, affect behavior, and are used to update the prediction models.Much of the work on the neural mechanisms of prediction and its violation has focused on spiking activity (2, 15–17). But there is mounting evidence that oscillatory dynamics play a role in regulating cortical processing and thus can also play a role, especially the gamma (40 to 90 Hz) and alpha/beta (10 to 30 Hz) bands (1, 18–25). A key observation is that, all across cortex, gamma power (>35 Hz)/spiking is higher during bottom-up sensory inputs. They are anticorrelated with alpha/beta (8 to 30 Hz) power (26–29), which is higher under conditions of top-down control (e.g., attention and response inhibition) (30–34). This suggests that top-down alpha/beta help regulate the processing of bottom-up inputs served by gamma and spiking. The idea is that alpha/beta carries the top-down predictions that inhibit the gamma/spiking that process expected inputs. This is consistent with gamma power being higher in the superficial, feedforward, cortical layers, and alpha/beta power being higher in the deep, feedback, cortical layers (26, 35–40). Indeed, superficial cortical layers have been hypothesized to be specialized for computing PEs and feeding PEs forward at gamma frequency (1, 19). In addition, computational modeling studies have shown the plausibility of superficial gamma circuits to engage in prediction error computations (38, 41, 42). Direct evidence for alpha/beta and gamma in predictive coding per se comes from observations of increased gamma power to stimuli that are prediction errors (22, 24, 25).How these rhythms (and their relation to spiking) differ with stimulus repetition/predictability as well as their stimulus specificity is not well known. Most neurophysiological studies of the effects of stimulus predictability have focused on spiking activity, often in a single area. And none of them to date have examined and compared activity in different cortical layers. We recorded local field potentials (LFPs) and spiking using multiarea, multilaminar recordings from a visual area (V4) and higher-order cortical areas (posterior parietal cortex and prefrontal cortex PFC]) simultaneously. Area V4 was selected as previous studies have shown this area to be a target of top-down signals such as attention (43, 44). Frontoparietal cortex was targeted because of its well-established role in top-down attention and working memory, cognitive processes that are engaged in the task employed here (30, 31). We manipulated the predictability of objects used in a working memory task. This revealed layer and frequency-specific associations with stimulus repetition/predictability as well as evidence for the direction of flow of these signals. The findings suggest an update of neural models of prediction and predictive coding.