Biology subjects

Livezey, J. A.

Publications and source records attributed to Livezey, J. A..

7 recordsLinked to original sources

bioRxiv2026

Closed-loop optimization of a high-dimensional generative latent space for rhythmic visual response

Neural oscillations accompany a wide range of cognitive states and behaviors including perception, memory, and movement, and modulating them is of growing interest for both basic neuroscience and clinical research. Previous demonstrations of closed-loop modulation of visual neural responses mainly relied on invasive recordings and focused on firing-rate maximization rather than rhythmic modulation. Here, we show that the relative power of steady-state visual evoked response (SSVEP), measured with readily-available, non-invasive electroencephalography, can be modulated in closedloop as a function of image stimulus parameters for single participants within a single session. Stimulus optimization with flicker frequencies in the alpha and theta bands was successful in 10, 20, and 40 dimensional latent spaces. We also show that optimized stimuli generalize to new participants when shown in open-loop. Finally, we characterize the visual features that modulate relative SSVEP power and find that low-frequency spatial power in the image drives theta and alpha in opposite directions. Together, our results show that closed-loop stimulus optimization is a viable method for rhythmic neural modulation using noninvasive neuroimaging methods.

neuroscience↗

bioRxiv2023

Resolving Non-identifiability Mitigates Bias in Models of Neural Tuning and Functional Coupling

In the brain, all neurons are driven by the activity of other neurons, some of which maybe simultaneously recorded, but most are not. As such, models of neuronal activity need to account for simultaneously recorded neurons and the influences of unmeasured neurons. This can be done through inclusion of model terms for observed external variables (e.g., tuning to stimuli) as well as terms for latent sources of variability. Determining the influence of groups of neurons on each other relative to other influences is important to understand brain functioning. The parameters of statistical models fit to data are commonly used to gain insight into the relative importance of those influences. Scientific interpretation of models hinge upon unbiased parameter estimates. However, evaluation of biased inference is rarely performed and sources of bias are poorly understood. Through extensive numerical study and analytic calculation, we show that common inference procedures and models are typically biased. We demonstrate that accurate parameter selection before estimation resolves model non-identifiability and mitigates bias. In diverse neurophysiology data sets, we found that contributions of coupling to other neurons are often overestimated while tuning to exogenous variables are underestimated in common methods. We explain heterogeneity in observed biases across data sets in terms of data statistics. Finally, counter to common intuition, we found that model non-identifiability contributes to bias, not variance, making it a particularly insidious form of statistical error. Together, our results identify the causes of statistical biases in common models of neural data, provide inference procedures to mitigate that bias, and reveal and explain the impact of those biases in diverse neural data sets. 1 Author SummaryExperimental data of interacting cells under the influence of external as well as unobserved factors are ubiquitous. Parametric models are often used to gain understanding of the processes that generated such data. As such, biological understanding hinges upon accurate inference of model parameters. Whether and how systemic parameter bias manifests in such models is poorly understood. We study this issue in the specific context of estimating the static and dynamic interactions of simultaneously recorded neurons influenced by stimuli and unobserved neurons. Through extensive numerical study and analytic calculations, we identify and mitigate bias in such models. When applied to diverse neural data sets, we found that common models and inference procedures often overestimate the importance of coupling and underestimate tuning. In contrast to common intuition, we find that model non-identifiability contributes to estimation bias, not variance, making it a particularly insidious form of statistical error. As the experimental and statistical issues examined here are common, the insights and solutions we developed will likely impact many fields of biology.

neuroscience↗

bioRxiv2022

Cortical surface electrical potentials are composed of multiple bandlimited frequency components, including high-gamma

A common challenge in neuroscience is how to decompose noisy, multi-source signals measured in experiments into biophysically interpretable components. Analysis of cortical surface electrical potentials (CSEPs) measured using electrocorticography arrays (ECoG) typifies this problem. We hypothesized that high frequency (70-1,000 Hz) CSEPs are composed of broadband (i.e., power-law) and bandlimited components with potentially differing biophysical origins. In particular, the high-gamma band (70-150 Hz) has been shown to be highly predictive for encoding and decoding behaviors and stimuli. Despite its demonstrated importance, whether high-gamma is composed of a bandlimited signal is poorly understood. To address this gap, we recorded CSEPs from rat auditory cortex and demonstrate that the evoked CSEPs are composed of multiple distinct frequency components, including high-gamma. We then show, using a novel robust regression method, that at fast timescales and on single trials during speech production, human high-gamma amplitude cannot be explained by a modulating power-law component; thus, high-gamma is band-limited. Furthermore, we show that the power-law component is less predictive of produced speech compared to the raw high-gamma amplitude. Finally, we show that the largest variance component of human ECoG signals is low-frequency and band-limited, not broadband. Together these results demonstrate that there are multiple, band-limited components of high frequency power in cortical surface electrical potentials, including the high-gamma band, which may have different biophysical origins. Significance StatementElectrocortigraphy (ECoG) records cortical surface electrical potentials (CSEPs). ECoG is utilized in both humans and animal models to understand distributed cortical processing and for brain machine interfaces. The spectral structure of evoked CSEPs is greatly debated. To address this issue, we recorded from rat auditory cortex using {micro}ECoG and human ventral sensory-motor cortex during speech production with high-density ECoG. Using novel analytic approaches, we found that evoked CSEPs are composed of multiple band-limited components, including high-gamma. These results contrast with the dominant thinking in the field that the high-frequency power in ECoG is broadband. Our results raise the possibility that distinct frequency components of ECoG are biomarkers of processing in different cortical layers.

neuroscience↗

bioRxiv2022

Not optimal, just noisy: the geometry of correlated variability leads to highly suboptimal sensory coding

The brain represents the world through the activity of neural populations. Correlated variability across simultaneously recorded neurons (noise correlations) has been observed across cortical areas and experimental paradigms. Many studies have shown that correlated variability improves stimulus coding compared to a null model with no correlations. However, such results do not shed light on whether neural populations correlated variability achieves optimal coding. Here, we assess optimality of noise correlations in diverse datasets by developing two novel null models each with a unique biological interpretation: a uniform correlations null model and a factor analysis null model. We show that across datasets, the correlated variability in neural populations leads to highly suboptimal coding performance according to these null models. We demonstrate that biological constraints prevent many subsets of the neural populations from achieving optimality according to these null models, and that subselecting based on biological criteria leaves coding performance suboptimal. Finally, we show that the optimal subpopulation is exponentially small as a function of neural dimensionality. Together, these results show that the geometry of correlated variability leads to highly suboptimal sensory coding.

neuroscience↗

bioRxiv2020

Accurate inference in parametric models reshapes neuroscientific interpretation and improves data-driven discovery

A central goal of systems neuroscience is to understand the relationships amongst constituent units in neural populations and their modulation by external factors using high-dimensional and stochastic neural recordings. Statistical models, particularly parametric models, play an instrumental role in accomplishing this goal, because their fitted parameters can provide insight into the underlying biological processes that generated the data. However, extracting conclusions from a parametric model requires that it is fit using an inference procedure capable of selecting the correct parameters and properly estimating their values. Traditional approaches to parameter inference have been shown to suffer from failures in both selection and estimation. Recent development of algorithms that ameliorate these deficiencies raises the question of whether past work relying on such inference procedures have produced inaccurate systems neuroscience models, thereby impairing their interpretation. Here, we used the Union of Intersections, a statistical inference framework capable of state-of-the-art selection and estimation performance, to fit functional coupling, encoding, and decoding models across a battery of neural datasets. We found that, compared to baseline procedures, UoI inferred models with increased sparsity, improved stability, and qualitatively different parameter distributions, while maintaining predictive performance across recording modality, brain region, and task. Specifically, we obtained highly sparse functional coupling networks with substantially different community structure, more parsimonious encoding models, and decoding models that rely on fewer single-units. Together, these results demonstrate that accurate parameter inference reshapes interpretation in diverse neuroscience contexts. The ubiquity of model-based data-driven discovery in biology suggests that analogous results would be seen in other fields.

neuroscience↗

bioRxiv2019

Heterogeneous synaptic weighting improves neural coding in the presence of common noise

Simultaneous recordings from the cortex have revealed that neural activity is highly variable, and that some variability is shared across neurons in a population. Further experimental work has demonstrated that the shared component of a neuronal populations variability is typically comparable to or larger than its private component. Meanwhile, an abundance of theoretical work has assessed the impact shared variability has upon a population code. For example, shared input noise is understood to have a detrimental impact on a neural populations coding fidelity. However, other contributions to variability, such as common noise, can also play a role in shaping correlated variability. We present a network of linear-nonlinear neurons in which we introduce a common noise input to model, for instance, variability resulting from upstream action potentials that are irrelevant for the task at hand. We show that by applying a heterogeneous set of synaptic weights to the neural inputs carrying the common noise, the network can improve its coding ability as measured by both Fisher information and Shannon mutual information, even in cases where this results in amplification of the common noise. With a broad and heterogeneous distribution of synaptic weights, a population of neurons can remove the harmful effects imposed by afferents that are uninformative about a stimulus. We demonstrate that some nonlinear networks benefit from weight diversification up to a certain population size, above which the drawbacks from amplified noise dominate over the benefits of diversification. We further characterize these benefits in terms of the relative strength of shared and private variability sources. Finally, we studied the asymptotic behavior of the mutual information and Fisher information analytically in our various networks as a function of population size. We find some surprising qualitative changes in the asymptotic behavior as we make seemingly minor changes in the synaptic weight distributions.

neuroscience↗

bioRxiv2019

Spatial whitening in the retina may be necessary for V1 to learn a sparse representation of natural scenes

Retinal ganglion cell outputs are less correlated across space than are natural scenes, and it has been suggested that this decorrelation is performed in the retina in order to improve efficiency and to benefit processing later in the visual system. However, sparse coding, a successful computational model of primary visual cortex, is achievable under some conditions with highly correlated inputs: most sparse coding algorithms learn the well-known sparse features of natural images and can output sparse, high-fidelity codes with or without a preceding decorrelation stage of processing. We propose that sparse coding with biologically plausible local learning rules does require decorrelated inputs, providing a possible explanation for why whitening may be necessary early in the visual system.

neuroscience↗