Biology subjects

Zavatone-Veth, J. A.

Publications and source records attributed to Zavatone-Veth, J. A..

4 recordsLinked to original sources

bioRxiv2023

Neural circuits for fast Poisson compressed sensing in the olfactory bulb

Within a single sniff, the mammalian olfactory system can decode the identity and concentration of odorants wafted on turbulent plumes of air. Yet, it must do so given access only to the noisy, dimensionally-reduced representation of the odor world provided by olfactory receptor neurons. As a result, the olfactory system must solve a compressed sensing problem, relying on the fact that only a handful of the millions of possible odorants are present in a given scene. Inspired by this principle, past works have proposed normative compressed sensing models for olfactory decoding. However, these models have not captured the unique anatomy and physiology of the olfactory bulb, nor have they shown that sensing can be achieved within the 100-millisecond timescale of a single sniff. Here, we propose a rate-based Poisson compressed sensing circuit model for the olfactory bulb. This model maps onto the neuron classes of the olfactory bulb, and recapitulates salient features of their connectivity and physiology. For circuit sizes comparable to the human olfactory bulb, we show that this model can accurately detect tens of odors within the timescale of a single sniff. We also show that this model can perform Bayesian posterior sampling for accurate uncertainty estimation. Fast inference is possible only if the geometry of the neural code is chosen to match receptor properties, yielding a distributed neural code that is not axis-aligned to individual odor identities. Our results illustrate how normative modeling can help us map function onto specific neural circuits to generate new hypotheses.

neuroscience↗

bioRxiv2022

Population geometry enables fast sampling in spiking neural networks

For animals to navigate an uncertain world, their brains need to estimate uncertainty at the timescales of sensations and actions. Sampling-based algorithms afford a theoretically-grounded framework for probabilistic inference in neural circuits, but it remains unknown how one can implement fast sampling algorithms in biologically-plausible spiking networks. Here, we propose to leverage the population geometry, controlled by the neural code and the neural dynamics, to implement fast samplers in spiking neural networks. We first show that two classes of spiking samplers--efficient balanced spiking networks that simulate Langevin sampling, and networks with probabilistic spike rules that implement Metropolis-Hastings sampling--can be unified within a common framework. We then show that careful choice of population geometry, corresponding to the natural space of parameters, enables rapid inference of parameters drawn from strongly-correlated high-dimensional distributions in both networks. Our results suggest design prin-ciples for algorithms for sampling-based probabilistic inference in spiking neural networks, yielding potential inspiration for neuromorphic computing and testable predictions for neurobiology.

neuroscience↗

bioRxiv2021

Manipulating neural dynamics to tune motion detection

Neurons integrate excitatory and inhibitory signals to produce their outputs, but the role of input timing in this integration remains poorly understood. Motion detection is a paradigmatic example of this integration, since theories of motion detection rely on different delays in visual signals. These delays allow circuits to compare scenes at different times to calculate the direction and speed of motion. It remains untested how response dynamics of individual cell types drive motion detection and velocity sensitivity. Here, we sped up or slowed down specific neuron types in Drosophilas motion detection circuit by manipulating ion channel expression. Altering the dynamics of individual neurons upstream of motion detectors changed their integrating properties and increased their sensitivity to fast or slow visual motion, exposing distinct roles for dynamics in tuning directional signals. A circuit model constrained by data and anatomy reproduced the observed tuning changes. Together, these results reveal how excitatory and inhibitory dynamics jointly tune a canonical circuit computation.

neuroscience↗

bioRxiv2020

Statistical structure of the trial-to-trial timing variability in synfire chains

Timing and its variability are crucial for behavior. Consequently, neural circuits that take part in the control of timing and in the measurement of temporal intervals have been the subject of much research. Here, we provide an analytical and computational account of the temporal variability in what is perhaps the most basic model of a timing circuit, the synfire chain. First, we study the statistical structure of trial-to-trial timing variability in a reduced but analytically tractable model: a chain of single integrate-and-fire neurons. We show that this circuits variability is well-described by a generative model consisting of local, global, and jitter components. We relate each of these components to distinct neural mechanisms in the model. Next, we establish in simulations that these results carry over to a noisy homogeneous synfire chain. Finally, motivated by the fact that a synfire chain is thought to underlie the circuit that takes part in the control and timing of zebra finch song, we present simulations of a biologically realistic synfire chain model of the zebra finch timekeeping circuit. We find the structure of trial-to-trial timing variability to be consistent with our previous findings, and to agree with experimental observations of the songs temporal variability. Our study therefore provides a possible neuronal account of behavioral variability in zebra finches.

neuroscience↗