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.

Highlights d A distinct neuron type with thin spikes and high burst propensity in monkey V1 d These neurons are present in both Old World and New World monkeys, but not in mice d They show relatively strong gamma (30-80 Hz) rhythmicity and stimulus selectivity d Firing suppression contributes to their orientation tuning and gamma rhythmicity

The ability to plan and execute appropriately timed responses to external stimuli is based on a well-orchestrated balance between movement initiation and inhibition. In impulse control disorders involving the prefrontal cortex (PFC) [1], this balance is disturbed, emphasizing the critical role that PFC plays in appropriately timing actions [2-4]. Here, we employed optogenetic and electrophysiological techniques to systematically analyze the functional role of five key subareas of the rat medial PFC (mPFC) and orbitofrontal cortex (OFC) in action control [5-9]. Inactivation of mPFC subareas induced drastic changes in performance, namely an increase (prelimbic cortex, PL) or decrease (infralimbic cortex, IL) of premature responses. Additionally, electrophysiology revealed a significant decrease in neuronal activity of a PL subpopulation prior to premature responses. In contrast, inhibition of OFC subareas (mainly the ventral OFC, i.e., VO) significantly impaired the ability to respond rapidly after external cues. Consistent with these findings, mPFC activity during response preparation predicted trial outcomes and reaction times significantly better than OFC activity. These data support the concept of opposing roles of IL and PL in directing proactive behavior and argue for an involvement of OFC in predominantly reactive movement control. By attributing defined roles to rodent PFC sections, this study contributes to a deeper understanding of the functional heterogeneity of this brain area and thus may guide medically relevant studies of PFC-associated impulse control disorders in this animal model for neural disorders [10-12].

A mechanism for inter-areal coherence through communication based on connectivity and oscillatory power Highlights d Synaptic projections from a sending to a receiving area explain long-range coherence d Inter-areal coherence can be predicted by power and connectivity d Power explains major changes in long-range coherence across behavioral states d Coherence emerges without spiking entrainment due to afferent synaptic inputs

The integration of direct bottom-up inputs with contextual information is a canonical motif in neocortical circuits. In area V1, neurons may reduce their firing rates when the (classical) receptive field input can be predicted by the spatial context. We previously hypothesized that gamma-synchronization (30-80Hz) provides a complementary signal to rates, encoding whether stimuli are predicted from spatial context by preferentially synchronizing neuronal populations receiving predictable inputs. Here we investigated how rates and synchrony are modulated by predictive context. Large uniform surfaces, which have high spatial predictability, strongly suppressed firing yet induced prominent gamma-synchronization, but only when they were colored. Yet, chromatic mismatches between center and surround, breaking predictability, strongly reduced gamma-synchronization while increasing firing rates. Differences between colors, including strong gamma-responses to red, arose because of stimulus adaptation to a full-screen background, with a prominent difference in adaptation between M-and L-cone signaling pathways. Thus, synchrony signals whether RF inputs are predicted from spatial context and may encode relationships across space, while firing rates increase when stimuli are unpredicted from the context.

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Feedforward deep neural networks for object recognition are a promising model of visual processing and can accurately predict firing-rate responses along the ventral stream. Yet, these networks have limitations as models of various aspects of cortical processing related to recurrent connectivity, including neuronal synchronization and the integration of sensory inputs with spatio-temporal context. We trained self-supervised, generative neural networks to predict small regions of natural images based on the spatial context (i.e. inpainting). Using these network predictions, we determined the spatial predictability of visual inputs into (macaque) V1 receptive fields (RFs), and distinguished low- from high-level predictability. Spatial predictability strongly modulated V1 activity, with distinct effects on firing rates and synchronization in gamma- (30-80Hz) and beta-bands (18-30Hz). Furthermore, firing rates, but not synchronization, were accurately predicted by a deep neural network for object recognition. Neural networks trained to specifically predict V1 gamma-band synchronization developed large, grating-like RFs in the deepest layer. These findings suggest complementary roles for firing rates and synchronization in self-supervised learning of natural-image statistics.

Behavioral studies suggest attention-related performancefluctuates at a 3-8Hz rhythm (Landau and Fries, 2012;Fiebelkorn et al., 2013). Brookshire (2022) argues that theseprevious studies failed to distinguish periodic from aperiodicprocesses, which led to the spurious detection of rhythms. Theargument is based on simulations of accuracy time courses(ATCs) by autoregressive models of order 1 (AR(1)), which is aknown aperiodic process. Brookshire shows that for this aperi-odic process, conventional methods often detect spurious peaksin the 3-8Hz range. Here, we argue that stationary stochasticAR(1) processes are invalid models of ATCs in general as ATCsare deterministic, non-stationary signals. When AR(1) realiza-tions are taken as models for deterministic signals, they do notconstitute per definition aperiodic processes. It is therefore notclear whether previous methods indeed detected rhythms spuri-ously. Furthermore, we show that because Brookshire’s methodrelies on AR(1) model fits, it can have very low sensitivity whennon-rhythmic signals are superimposed onto rhythmic signals.Here, we argue that an appropriate model of an ATCs is a de-terministic signal (akin to an impulse response or event-relatedpotential) with additive white noise due to random sampling ofhits and misses. Such a deterministic ATC can be either peri-odic or aperiodic. We show that when the deterministic ATC istruly aperiodic, detrending and random sampling lead to spu-rious rhythm detection. This supports the general point thatprevious studies did not adequately distinguish periodic fromaperiodic processes. Our analysis naturally suggests an effec-tive alternative method based on cross-validation which avoidsthe use of AR(1) models.