Noise correlations

Recent years have witnessed a growing body of experimental evidence highlighting the active role of glial cells in modulating neuronal dynamics. Glial cells, including astrocytes and microglia, have been found to influence various aspects of neuronal function, such as regulating neurotransmitter concentrations and ion buffering. Yet, the mechanisms through which glial cells communicate with and affect neighboring neurons remain unclear. In this study, we seek to develop a computational model to explore recent experimental findings which demonstrated that glial cells physically wrap around, o

How the activity of sensory neurons supports perceptual decisions remains one of the central questions of systems neuroscience. Two primary frameworks guide the field’s thinking on this question. One framework, guided by ideas of information maximization, redundancy reduction, and efficient coding, focuses on the decision-related information represented by neural responses and how it is extracted from sensory populations. In another framework, based on the Bayesian brain hypothesis, sensory responses represent posterior beliefs about the outside world, which then guide decision-making. Interes

…this representational drift signal correlations predict future noise correlations, suggesting that stimulus-driven co-activation strengthens effective connectivity via Hebbian-like plasticity. Linear network models reveal that these temporal dependencies between signal and noise correlations emerge only when Hebbian learning balances stochastic synaptic changes, preventing functional degradation

…activity [1], dimensionality [2], noise correlations [3], and autocorrelation timescales [4]. State-dependent changes in the primary visual cortex (V1) are potentially induced by cortico-cortical signals, such as those from V4 to V1, known to mediate visual attention in macaques [5]. However, relationships between such top-down signals and the aforementioned state-dependent changes have not been demonstrated directly. To investig

…variability, reducing the signal-to-noise ratio of information processing in the central nervous system. To explicitly test this hypothesis, we investigate behavior and brain activity in young and old mice performing a visual decision-making task. Old animals show relatively worse performance (a shallower psychometric curve and slower reaction times). We then used extracellular neural recordings to quantify single-neuron variabi

Inhibitory neurons are pivotal in shaping neural dynamics, acting as a regulatory mechanism to maintain network stability and enhance sensory processing. Their interplay within neural circuits affects stimulus selectivity, temporal precision, and memory functions. On the other hand, mounting evidence suggests that neural responses to sensory stimuli change over time – a phenomenon termed representational drift. Yet, how inhibitory neurons contribute to representational drift remains largely unknown.Here, we examined the collective statistics of inhibitory and excitatory neurons in layers 2/3 o

Information from rich visual signals and ongoing behavioral variables simultaneously drive the same neurons in the cortex without corrupting each other (Niell et al. 2010, Stringer et al. 2019, Shimaoka et al. 2019). How does the anatomical organization of the cortex support the joint coding of these streams of information? We hypothesized that stimulus-driven and non-stimulus activity follow different anatomical organizations in the visual cortex. To investigate the physical distribution of these signals, we used the Light Beads Microscope (LBM, Demas et al. 2021) to volumetrically image the

…cross-network connection probability, noise correlations

…dissimilarity including canonical correlations analysis (CCA), centered kernel alignment (CKA), representational similarity analysis (RSA), shape metrics, and so on. Intuitively, these measures quantify similarity in the geometry of neural responses while removing expected forms of invariance across networks (e.g. permutations over arbitrary neuron indices). However, these met

The ability to record large neural populations—hundreds to thousands of cells simultaneously—is a defining feature of modern systems neuroscience. Aside from improved experimental efficiency, what do these technologies fundamentally buy us? I'll argue that they provide an exciting opportunity to move beyond studying the "average" neural response. That is, by providing dense neural circuit measurements in individual subjects and moments in time, these recordings enable us to track changes across repeated behavioral trials and across experimental subjects. These two forms of variability are stil

What we expect to see can greatly affect what we perceive. How does expectation influence local circuitry among visual cortical neurons to allow for perceptual discrimination in the context of rich natural inputs? Little work has sought to understand how populations of sensory cortical neurons change their responses with expectation, and the majority of studies manipulate expectation in a task-irrelevant way, disallowing for comparisons of changes in neural activity with behavioral performance. Theoretical and experimental work indicates that changes at the population level, in particular thos

Correlated fluctuations of neural activity, termed noise correlations (NCs), are widespread in cortex and affected by sensory and non-sensory variables. Past work suggests that divisive normalization (DN), a cortical computation responsible for coordinating neural activity, could explain those modulations of neuronal covariability. However, quantifying the relation between DN and NCs remains challenging, as most successful descriptive models of neural variability do not account for DN. We propose a pairwise stochastic DN model that accounts for the effect of DN on covariability. In the model,

…First, noise in the neural responses (aka ``noise correlations'') can interfere with the estimation of the eigenvalues of the signal distribution. Second,

How the brain separates information about multiple objects despite overlap in the neurons responsive to each item is not well understood. It has recently been proposed that when more than one stimulus is present, single neurons can fluctuate between coding one vs. the other(s) across some time period, i.e. a form of neural multiplexing in the time domain (Caruso et al., 2018). However, it is not known (a) whether such fluctuations occur specifically when two distinct objects are present and not in other cases, and (b) how fluctuations in individual neurons might be coordinated with one anot