Coding strategy for surface luminance switches in the primary visual cortex of the awake monkey

Both surface luminance and edge contrast of an object are essential features for object identification. However, cortical processing of surface luminance remains unclear. In this study, we aim to understand how the primary visual cortex (V1) processes surface luminance information across its different layers. We report that edge-driven responses are stronger than surface-driven responses in V1 input layers, but luminance information is coded more accurately by surface responses. In V1 output layers, the advantage of edge over surface responses increased eight times and luminance information was coded more accurately at edges. Further analysis of neural dynamics shows that such substantial changes for neural responses and luminance coding are mainly due to non-local cortical inhibition in V1’s output layers. Our results suggest that non-local cortical inhibition modulates the responses elicited by the surfaces and edges of objects, and that switching the coding strategy in V1 promotes efficient coding for luminance.

Mesoscale cortex-wide neural dynamics predict self-initiated actions in mice several seconds prior to movement

Volition - the sense of control or agency over one's voluntary actions - is widely recognized as the basis of both human subjective experience and natural behavior in non-human animals. To date, several human studies have found peaks in neural activity preceding voluntary actions, e.g. the readiness potential (RP), and some have shown upcoming actions could be decoded even before awareness. These findings remain controversial with some suggesting they pose a challenge to traditional accounts of human volition while others proposing that random processes underlie pre-movement neural activity. Here we seek to address part of this controversy by evaluating whether pre-movement neural activity in mice contains structure beyond that expected from random processes. Implementing a self-initiated water-rewarded lever pull paradigm in mice while recording widefield [Ca++] neural activity we find that cortical activity changes in variance seconds prior to movement and that upcoming lever pulls or spontaneous body movements could be predicted between 1 second to more than 10 seconds prior to movement, similar to but even earlier than in human studies. We show that mice, like humans, are biased towards initiation of voluntary actions during specific phases of neural activity oscillations but that the pre-movement neural code in mice changes over time and is widely distributed as behavior prediction improved when using all vs single cortical areas. These findings support the presence of structured multi-second neural dynamics preceding voluntary action beyond that expected from random processes. Our results also suggest that neural mechanisms underlying self-initiated voluntary action could be preserved between mice and humans.

Local dendritic balance enables learning of efficient representations in networks of spiking neurons

How can neural networks learn to efficiently represent complex and high-dimensional inputs via local plasticity mechanisms? Classical models of representation learning assume that feedforward weights are learned via pairwise Hebbian-like plasticity. Here, we show that pairwise Hebbian-like plasticity works only under unrealistic requirements on neural dynamics and input statistics. To overcome these limitations, we derive from first principles a learning scheme based on voltage-dependent synaptic plasticity rules. Here, recurrent connections learn to locally balance feedforward input in individual dendritic compartments and thereby can modulate synaptic plasticity to learn efficient representations. We demonstrate in simulations that this learning scheme works robustly even for complex high-dimensional inputs and with inhibitory transmission delays, where Hebbian-like plasticity fails. Our results draw a direct connection between dendritic excitatory–inhibitory balance and voltage-dependent synaptic plasticity as observed in vivo and suggest that both are crucial for representation learning.

Mindfulness Promotes Control of Brain Network Dynamics for Self-Regulation and Discontinues the Past from the Present

Mindfulness is characterized by attentiveness to the present experience with nonjudgmental awareness and acceptance. Practicing mindfulness alters brain function to support the executive regulation of thoughts, feelings, and behavior. While early stages of practice are thought to require greater "neural effort" for later efficiency, current evidence relies on circular definitions of effort based on functional activity magnitude. Here we used network control theory as a model of how external control inputs, which operationalize effort, can distribute changes in neural activity across the macro-scale structural brain network. Further, we inferred the intrinsic timescale of activity to operationalize present-centered activity as shorter momentary timescales that discontinue the past and update the present. To explain effects of mindful regulation on alcohol consumption, we applied these methods to a randomized controlled intervention study with resting-state and task fMRI data. The task primed participants to either mindfully respond or naturally react to alcohol cues. Mobile text interventions and measurements of alcohol consumption were administered using ecological momentary assessments during the subsequent 4 weeks. We hypothesized that neural states of mindfulness require greater effort to enact and sustain. This effort may support deautomatized habitual natural reactions, discontinued processing, and updated present-centered neural dynamics. We found that mindful regulation of alcohol cues, compared to the natural reactions of the benchmark group, involved more effortful control of neural dynamics across cognitive control and attention networks. This effort persisted in the natural reactions of the mindful group compared to the benchmark group. Using resting-state fMRI, we found that more effortful neural states tended to occur over shorter timescales than less effortful states. Our findings provide an explanation for how neural dynamics with altered effort and stability, such as mindful states, tend to center the present experience.