Behavioral strategy shapes activation of the Vip-Sst disinhibitory circuit in visual cortex

Bilateral integration in somatosensory cortex is controlled by behavioral relevance

Sensory perception requires the processing of stimuli from both sides of the body. Yet, how neurons bind stimulus information across the hemispheres to create a unified percept remains unknown. Here we perform large-scale recordings from neurons in the left and right primary somatosensory cortex (S1) in mice performing a task requiring active whisker touch to coordinate stimulus features across hemispheres. When mice touched reward-associated stimuli, their whiskers moved with greater bilateral symmetry, and synchronous spiking and enhanced spike-field coupling emerged between the hemispheres. This coordinated activity was absent in stimulus-matched naive animals, indicating that interhemispheric coupling involves a goal-directed, internal process. In S1 neurons, the addition of ipsilateral touch primarily facilitated the contralateral principal whisker response. This facilitation primarily emerged for reward-associated stimuli and was lost on trials where mice failed to respond. Silencing of callosal S1 signaling reduced bilateral facilitation and interhemispheric synchrony. These results reveal a state-dependent logic that augments the flow of tactile information through the corpus callosum.

Reveal the mechanism of brain function with fluorescence microscopy at single-cell resolution: from neural decoding to encoding

As a key pathway for understanding behavior, cognition, and emotion, neural decoding and encoding provide effective tools to bridge the gap between neural mechanisms and imaging recordings, especially at single-cell resolution. While neural decoding aims to establish an interpretable theory of how complex biological behaviors are represented in neural activities, neural encoding focuses on manipulating behaviors through the stimulation of specific neurons. We thoroughly analyze the application of fluorescence imaging techniques, particularly two-photon fluorescence imaging, in decoding neural activities, showcasing the theoretical analysis and technological advancements from imaging recording to behavioral manipulation. For decoding models, we compared linear and nonlinear methods, including independent component analysis, random forests, and support vector machines, highlighting their capabilities to reveal the intricate mapping between neural activity and behavior. By employing synthetic stimuli via optogenetics, fundamental principles of neural encoding are further explored. We elucidate various encoding types based on different stimulus paradigms-quantity encoding, spatial encoding, temporal encoding, and frequency encoding-enhancing our understanding of how the brain represents and processes information. We believe that fluorescence imaging-based neural decoding and encoding techniques have deepened our understanding of the brain, and hold great potential in paving the way for future neuroscience research and clinical applications.

Diversity of omission responses to visual images across brain-wide regions

An organism's survival depends on its ability to anticipate forthcoming events and detect discrepancies between the expected and actual sensory inputs. We analyzed data from mice performing a visual go/no-go change-detection task where the sequence of stimulus presentations was intermittently interrupted by omission of a stimulus. The omission of a visual stimulus did not elicit discernable spiking responses in visual cortical neurons. Instead, firing rates between image presentations, including the omission period, ramped linearly and without interruption at the time of the omitted image. Several neuron types in visual cortex neurons were identified with various responses to images and their omissions. A minority of cells in nonvisual areas, including the hippocampus, increased their firing rates at the omitted stimulus onset even when these neurons did not respond to the images. Our study elucidates the origin of omission responses in the visual cortex and sheds light on the role of hippocampal and subcortical circuits in omission detection.

Deciphering neuronal variability across states reveals dynamic sensory encoding

Influenced by non-stationary factors such as brain states and behavior, neurons exhibit substantial response variability even to identical stimuli. However, it remains unclear how their relative impact on neuronal variability evolves over time. To address this question, we designed an encoding model conditioned on latent states to partition variability in the mouse visual cortex across internal brain dynamics, behavior, and external visual stimulus. Applying a hidden Markov model to local field potentials, we consistently identified three distinct oscillation states, each with a unique variability profile. Regression models within each state revealed a dynamic composition of factors influencing spiking variability, with the dominant factor switching within seconds. The state-conditioned regression model uncovered extensive diversity in source contributions across units, varying in accordance with anatomical hierarchy and internal state. This heterogeneity in encoding underscores the importance of partitioning variability over time, particularly when considering the influence of non-stationary factors on sensory processing.

Basolateral amygdala oscillations enable fear learning in a biophysical model

The basolateral amygdala (BLA) is a key site where fear learning takes place through synaptic plasticity. Rodent research shows prominent low theta (~3-6 Hz), high theta (~6-12 Hz), and gamma (>30 Hz) rhythms in the BLA local field potential recordings. However, it is not understood what role these rhythms play in supporting the plasticity. Here, we create a biophysically detailed model of the BLA circuit to show that several classes of interneurons (PV, SOM, and VIP) in the BLA can be critically involved in producing the rhythms; these rhythms promote the formation of a dedicated fear circuit shaped through spike-timing-dependent plasticity. Each class of interneurons is necessary for the plasticity. We find that the low theta rhythm is a biomarker of successful fear conditioning. The model makes use of interneurons commonly found in the cortex and, hence, may apply to a wide variety of associative learning situations.

Coordinated changes in a cortical circuit sculpt effects of novelty on neural dynamics

Recent studies have found dramatic cell-type-specific responses to stimulus novelty, highlighting the importance of analyzing the cortical circuitry at this granularity to understand brain function. Although initial work characterized activity by cell type, the alterations in cortical circuitry due to interacting novelty effects remain unclear. We investigated circuit mechanisms underlying the observed neural dynamics in response to novel stimuli using a large-scale public dataset of electrophysiological recordings in behaving mice and a population network model. The model was constrained by multi-patch synaptic physiology and electron microscopy data. We found generally weaker connections under novel stimuli, with shifts in the balance between somatostatin (SST) and vasoactive intestinal polypeptide (VIP) populations and increased excitatory influences on parvalbumin (PV) and SST populations. These findings systematically characterize how cortical circuits adapt to stimulus novelty.