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Whether temporal code and rate code have different rates of representational drift over extended periods is not fully understood. Using ultraflexible electrodes, here authors show that temporal codes extracted from fast spiking patterns reduce visual representational drift compared to firing rates over 15 consecutive days in mice.

The MICrONS mouse visual cortex dataset shows that neurons with similar response properties preferentially connect, a pattern that emerges within and across brain areas and layers, and independently emerges in artificial neural networks where these ‘like-to-like’ connections prove important for task performance.

A foundation model trained on neural activity of visual cortex from multiple mice accurately predicts responses to video stimuli and cell types, dendritic features and connectivity within the MICrONS functional connectomics dataset.

Dense calcium imaging combined with co-registered high-resolution electron microscopy reconstruction of the brain of the same mouse provide a functional connectomics map of tens of thousands of neurons of a region of the primary cortex and higher visual areas.

Although Bayesian models provide good accounts of perceptual decisions, it is unclear how their components are represented in the brain. This paper addresses this question by showing that uncertainty decoded from visual cortex helps predict behavior.

Neural Decomposition (NEURD) is a software package that decomposes neuronal data from high-resolution electron microscopy volumes into feature-rich graph representations to facilitate analysis for neuroscience research.

Computational modelling and functional imaging of awake, active mice show that behaviour directly changes neuronal tuning in the visual cortex through pupil dilation.

Excitatory neurons in the neocortex exhibit considerable morphological diversity, yet their organizational principles remain a subject of ongoing research. Here, the authors use unsupervised learning to show that most excitatory neuron morphologies in the mouse visual cortex form a continuum, with notable exceptions in deeper layers.

Using volumetric electron microscopy, the authors map and analyze the structure of cortical inhibition with synaptic resolution across a column of visual cortex.

A study now shows that variability in neuronal responses in the visual system mainly arises from slow fluctuations in excitability, presumably caused by factors of nonsensory origin, such as arousal, attention or anesthesia.

Cortical activity is modulated by an intricate network of feedforward and feedback connectivity. Here the authors demonstrate distinct organizational rules govern feedback projections from lateral medial area to V1 versus projections from vibrissal M1 to vibrissal S1.

Patch-seq reveals new neuronal subtypes by combining electrophysiological and RNA-seq data on single neurons in situ.

Single-cell transcriptomic, morphological and electrophysiological characteristics are combined to classify more than 1,300 neurons from mouse motor cortex.

The authors develop a deep learning approach that enables an efficient search of the input space to find the best stimuli for modeled neurons. When tested, these stimuli are most effective at driving their matching cells in the brain.

Ouzounov et al. report calcium imaging with three-photon microscopy in the mouse brain. The approach enabled noninvasive recording of activity with high spatial and temporal resolution from GCaMP6-labeled neurons located as deep as the hippocampus.

The authors found that subdural electrical stimulation of visual cortex only produced a visual percept if high-frequency gamma oscillations were evoked in the temporoparietal junction (TPJ). Furthermore, electrical stimulation of the TPJ modified the detectability of visual stimuli. These results link the TPJ to visual perception.

Layer 4 of the mammalian neocortex is important for cortical information processing but its cellular composition and local circuits remains elusive. The authors compared two primary sensory cortical areas of mice and found differences in cell composition and local connectivity.

Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. Here, the authors present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information which indicates near-optimal nonlinear decoding.

The precise cell-type specific role of inhibitory interneurons in regulating sensory responses in the olfactory bulb is not known. Here, the authors report that removing GABAergic inhibition from one layer differentially affects response dynamics of the two main output cell types and changes odor mixture processing.

In addition to light intensity, changes in pupil diameter are correlated with mental effort, attention and levels of arousal. Reimer et al. report that across behavioural states, fluctuations in pupil diameter are highly correlated with activity of noradrenergic and cholinergic projection neurons.

A challenge for any machine learning system is to continually adapt to new data. While methods to address this issue are developed, their performance is hard to compare. A new framework to facilitate benchmarking divides approaches into three categories, defined by whether models need to adapt to new tasks, domains or classes.

The authors recorded populations of up to 500 neurons in the mouse primary visual cortex during natural movies. They found that higher-order correlations in natural scenes induce a sparser code, with reliable activation of a smaller set of neurons that can be read out more easily, but only in anesthetized and active awake animals, not during quiet wakefulness.

One challenge that faces artificial intelligence is the inability of deep neural networks to continuously learn new information without catastrophically forgetting what has been learnt before. To solve this problem, here the authors propose a replay-based algorithm for deep learning without the need to store data.

Attention reduces correlated variability in population activity, however the effect of fluctuations in attentional state has not been studied. Here, the authors report in a novel visual task that fluctuations in attentional allocation have a pronounced effect on correlated variability at longer timescales.

The authors show that, unlike the consolidation and refinement of excitatory connections observed during sensory map formation, a dramatic broadening of patterned activation domains, connectivity, and tuning occurs in interneurons in the olfactory bulb. This developmental expansion is sensitive to activity manipulations and may reveal general principles of interneuron network development.

The BRAIN Initiative Cell Census Network has constructed a multimodal cell census and atlas of the mammalian primary motor cortex in a landmark effort towards understanding brain cell-type diversity, neural circuit organization and brain function.

Traumatic brain injury is associated with changes to the metabolome. Here the authors show that acute traumatic brain injury has distinctive serum metabolic patterns which may suggest protective changes of systemic lipid metabolism aiming to maintain lipid homeostasis in the brain.

Silicon microelectrodes are a powerful technique for recording neuronal population activity. Increases in probe size and density make for larger recordable populations, but also require new techniques for processing the resulting data. The authors describe a suite of practical, open source software for spike sorting of large, dense electrode arrays.

One of the ambitions of computational neuroscience is that we will continue to make improvements in the field of artificial intelligence that will be informed by advances in our understanding of how the brains of various species evolved to process information. To that end, here the authors propose an expanded version of the Turing test that involves embodied sensorimotor interactions with the world as a new framework for accelerating progress in artificial intelligence.

It is an outstanding challenge to develop intelligent machines that can learn continually from interactions with their environment, throughout their lifetime. Kudithipudi et al. review neuronal and non-neuronal processes in organisms that address this challenge and discuss pathways to developing biologically inspired approaches for lifelong learning machines.

To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.

This protocol describes how to integrate whole-cell patch-clamp in single neurons from mouse brain tissue slices with single-cell RNA sequencing and morphological recovery.