David M. Zoltowski
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View article: Improved inference of latent neural states from calcium imaging data
Improved inference of latent neural states from calcium imaging data Open
Calcium imaging (CI) is a standard method for recording neural population activity, as it enables simultaneous recording of hundreds-to-thousands of individual somatic signals. Accordingly, CI recordings are prime candidates for population…
View article: Computation-through-Dynamics Benchmark: Simulated datasets and quality metrics for dynamical models of neural activity
Computation-through-Dynamics Benchmark: Simulated datasets and quality metrics for dynamical models of neural activity Open
A primary goal of systems neuroscience is to discover how ensembles of neurons transform inputs into goal-directed behavior, a process known as neural computation. A powerful framework for understanding neural computation uses neural dynam…
View article: Brain-to-Text Benchmark '24: Lessons Learned
Brain-to-Text Benchmark '24: Lessons Learned Open
Speech brain-computer interfaces aim to decipher what a person is trying to say from neural activity alone, restoring communication to people with paralysis who have lost the ability to speak intelligibly. The Brain-to-Text Benchmark '24 a…
View article: Structured flexibility in recurrent neural networks via neuromodulation
Structured flexibility in recurrent neural networks via neuromodulation Open
The goal of theoretical neuroscience is to develop models that help us better understand biological intelligence. Such models range broadly in complexity and biological detail. For example, task-optimized recurrent neural networks (RNNs) h…
View article: Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems
Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems Open
Understanding how the collective activity of neural populations relates to computation and ultimately behavior is a key goal in neuroscience. To this end, statistical methods which describe high-dimensional neural time series in terms of l…
View article: Competitive integration of time and reward explains value-sensitive foraging decisions and frontal cortex ramping dynamics
Competitive integration of time and reward explains value-sensitive foraging decisions and frontal cortex ramping dynamics Open
Patch foraging presents a ubiquitous decision-making process in which animals decide when to abandon a resource patch of diminishing value to pursue an alternative. We developed a virtual foraging task in which mouse behavior varied system…
View article: Modeling communication and switching nonlinear dynamics in multi-region neural activity
Modeling communication and switching nonlinear dynamics in multi-region neural activity Open
Understanding how multiple brain regions interact to produce behavior is a major challenge in systems neuroscience, with many regions causally implicated in common tasks such as sensory processing and decision making. However, a precise de…
View article: Modeling statistical dependencies in multi-region spike train data
Modeling statistical dependencies in multi-region spike train data Open
View article: Unifying and generalizing models of neural dynamics during decision-making
Unifying and generalizing models of neural dynamics during decision-making Open
An open question in systems and computational neuroscience is how neural circuits accumulate evidence towards a decision. Fitting models of decision-making theory to neural activity helps answer this question, but current approaches limit …
View article: Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations
Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations Open
Gaussian Process Factor Analysis (GPFA) has been broadly applied to the problem of identifying smooth, low-dimensional temporal structure underlying large-scale neural recordings. However, spike trains are non-Gaussian, which motivates com…
View article: Efficient non-conjugate Gaussian process factor models for spike count\n data using polynomial approximations
Efficient non-conjugate Gaussian process factor models for spike count\n data using polynomial approximations Open
Gaussian Process Factor Analysis (GPFA) has been broadly applied to the\nproblem of identifying smooth, low-dimensional temporal structure underlying\nlarge-scale neural recordings. However, spike trains are non-Gaussian, which\nmotivates …
View article: Discrete Stepping and Nonlinear Ramping Dynamics Underlie Spiking Responses of LIP Neurons during Decision-Making
Discrete Stepping and Nonlinear Ramping Dynamics Underlie Spiking Responses of LIP Neurons during Decision-Making Open
View article: Scaling the Poisson GLM to massive neural datasets through polynomial approximations.
Scaling the Poisson GLM to massive neural datasets through polynomial approximations. Open
Recent advances in recording technologies have allowed neuroscientists to record simultaneous spiking activity from hundreds to thousands of neurons in multiple brain regions. Such large-scale recordings pose a major challenge to existing …
View article: Discrete stepping and nonlinear ramping dynamics underlie spiking responses of LIP neurons during decision-making
Discrete stepping and nonlinear ramping dynamics underlie spiking responses of LIP neurons during decision-making Open
Neurons in macaque area LIP exhibit gradual ramping in their trial-averaged spike responses during sensory decision-making. However, recent work has sparked debate over whether single-trial LIP spike trains are better described by discrete…