Bounds, H. A. | bioRxiv Science

bioRxiv2022

Modeling the Diverse Effects of Divisive Normalization on Noise Correlations

Divisive normalization, a prominent descriptive model of neural activity, is employed by theories of neural coding across many different brain areas. Yet, the relationship between normalization and the statistics of neural responses beyond single neurons remains largely unexplored. Here we focus on noise correlations, a widely studied pairwise statistic, because its stimulus and state dependence plays a central role in neural coding. Existing models of covariability typically ignore normalization despite empirical evidence suggesting it affects correlation structure in neural populations. We therefore propose a pairwise stochastic divisive normalization model that accounts for the effects of normalization and other factors on covariability. We first show that normalization modulates noise correlations in qualitatively different ways depending on whether normalization is shared between neurons, and we discuss how to infer when normalization signals are shared. We then apply our model to calcium imaging data from mouse primary visual cortex (V1), and find that it accurately fits the data, often outperforming a popular alternative model of correlations. Our analysis indicates that normalization signals are often shared between V1 neurons in this dataset. Our model will enable quantifying the relation between normalization and covariability in a broad range of neural systems, which could provide new constraints on circuit mechanisms of normalization and their role in information transmission and representation. Author SummaryCortical responses are often variable across identical experimental conditions, and this variability is shared between neurons (noise correlations). These noise correlations have been studied extensively to understand how they impact neural coding and what mechanisms determine their properties. Here we show how correlations relate to divisive normalization, a mathematical operation widely adopted to describe how the activity of a neuron is modulated by other neurons via divisive gain control. We introduce the first statistical model of this relation. We extensively validate the model and investigate parameter inference in synthetic data. We find that our model, when applied to data from mouse visual cortex, outperforms a popular model of noise correlations that does not include normalization, and it reveals diverse influences of normalization on correlations. Our work demonstrates a framework to measure the relation between noise correlations and the parameters of the normalization model, which could become an indispensable tool for quantitative investigations of noise correlations in the wide range of neural systems that exhibit normalization.

neuroscience↗

bioRxiv2021

Multifunctional Cre-dependent transgenic mice for high-precision all-optical interrogation of neural circuits

Determining which features of the neural code drive perception and behavior requires the ability to simultaneous read out and write in neural activity patterns with high precision across many neurons. All-optical systems that combine two photon (2p) calcium imaging and targeted 2p photostimulation enable the activation of specific, functionally defined groups of neurons in behaving animals. However, these techniques do not yet have the ability to reveal how the specific distribution of firing rates across a relevant neural population mediates neural computation and behavior. The key technical obstacle is the inability to transform single-cell calcium signals into accurate estimates of firing rate changes and then write in these cell-specific firing rate changes to each individual neuron in a targeted population. To overcome this challenge, we made two advances: first we introduce a new genetic line of mice for robust Cre-dependent co-expression of a high-performance calcium indicator and a potent soma-targeted microbial opsin. Second, using this line, we developed a pipeline that enables the read-out and write-in of precise population vectors of neural activity across a targeted group of neurons. The combination of the new multifunctional transgenic line and the photostimulation paradigm offer a powerful and convenient platform for investigating the neural codes of computation and behavior. It may prove particularly useful for probing causal features of the geometry of neural representations where the ability to directly control the topology of population activity is essential.

neuroscience↗