Biology subjects

Cayco-Gajic, N. A.

Publications and source records attributed to Cayco-Gajic, N. A..

4 recordsLinked to original sources

bioRxiv2023

Disentangling Mixed Classes of Covariability in Large-Scale Neural Data

Recent work has argued that large-scale neural recordings are often well described by low-dimensional latent dynamics identified using dimensionality reduction. However, the view that task-relevant variability is shared across neurons misses other types of structure underlying behavior, including stereotyped neural sequences or slowly evolving latent spaces. To address this, we introduce a new framework that simultaneously accounts for variability that is shared across neurons, trials, or time. To identify and demix these covariability classes, we develop a new unsupervised dimensionality reduction method for neural data tensors called sliceTCA. In three example datasets, including motor cortical dynamics during a classic reaching task and recent multi-region recordings from the International Brain Laboratory, we show that sliceTCA can capture more task-relevant structure in neural data using fewer components than traditional methods. Overall, our theoretical framework extends the classic view of low-dimensional population activity by incorporating additional classes of latent variables capturing higher-dimensional structure.

neuroscience↗

bioRxiv2019

Transformation of population code from dLGN to V1 facilitates linear decoding

How neural populations represent sensory information, and how that representation is transformed from one brain area to another, are fundamental questions of neuroscience. The dorsolateral geniculate nucleus (dLGN) and primary visual cortex (V1) represent two distinct stages of early visual processing. Classic sparse coding theories propose that V1 neurons represent local features of images. More recent theories have argued that the visual pathway transforms visual representations to become increasingly linearly separable. To test these ideas, we simultaneously recorded the spiking activity of mouse dLGN and V1 in vivo. We find strong evidence for both sparse coding and linear separability theories. Surprisingly, the correlations between neurons in V1 (but not dLGN) were shaped as to be irrelevant for stimulus decoding, a feature which we show enables linear separability. Therefore, our results suggest that the dLGN-V1 transformation reshapes correlated variability in a manner that facilitates linear decoding while producing a sparse code.

neuroscience↗

bioRxiv2018

A Moment-Based Maximum Entropy Model for Fitting Higher-Order Interactions in Neural Data

Correlations in neural activity have been demonstrated to have profound consequences for sensory encoding. To understand how neural populations represent stimulus information, it is therefore necessary to model how pairwise and higher-order spiking correlations between neurons contribute to the collective structure of population-wide spiking patterns. Maximum entropy models are an increasingly popular method for capturing collective neural activity by including successively higher-order interaction terms. However, incorporating higher-order interactions in these models is difficult in practice due to two factors. First, the number of parameters exponentially increases as higher orders are added. Second, because triplet (and higher) spiking events occur infrequently, estimates of higher-order statistics may be contaminated by sampling noise. To address this, we extend previous work on the Reliable Interaction class of models [1] to develop a normalized variant that adaptively identifies the specific pairwise and higher-order moments that can be estimated from a given dataset for a specified confidence level. The resulting \"Reliable Moment\" model is able to capture cortical-like distributions of population spiking patterns. Finally, we show that, compared with the Reliable Interaction model, the Reliable Moment model infers fewer strong spurious higher-order interactions and is better able to predict the frequencies of previously unobserved spiking patterns.

neuroscience↗

bioRxiv2018

Open Source Brain: a collaborative resource for visualizing, analyzing, simulating and developing standardized models of neurons and circuits

Computational models are powerful tools for investigating brain function in health and disease. However, biologically detailed neuronal and circuit models are complex and implemented in a range of specialized languages, making them inaccessible and opaque to many neuroscientists. This has limited critical evaluation of models by the scientific community and impeded their refinement and widespread adoption. To address this, we have combined advances in standardizing models, open source software development and web technologies to develop Open Source Brain, a platform for visualizing, simulating, disseminating and collaboratively developing standardized models of neurons and circuits from a range of brain regions. Model structure and parameters can be visualized and their dynamical properties explored through browser-controlled simulations, without writing code. Open Source Brain makes neural models transparent and accessible and facilitates testing, critical evaluation and refinement, thereby helping to improve the accuracy and reproducibility of models, and their dissemination to the wider community.

neuroscience↗