Biology subjects

Butts, D. A.

Publications and source records attributed to Butts, D. A..

3 recordsLinked to original sources

bioRxiv2018

Functional characterization of retinal ganglion cells using tailored nonlinear modeling

There are 20-50 functionally- and anatomically-distinct ganglion cell types in the mammalian retina; each type encodes a unique feature of the visual world and conveys it via action potentials to the brain. Individual ganglion cells receive input from unique presynaptic retinal circuits, and the characteristic patterns of light-evoked action potentials in each ganglion cell type therefore reflect computations encoded in synaptic input and in postsynaptic signal integration and spike generation. Unfortunately, there is a dearth of tools for characterizing retinal ganglion cell computation. Therefore, we developed a statistical model, the separable Nonlinear Input Model, capable of characterizing the large array of distinct computations reflected in retinal ganglion cell spiking. We recorded ganglion cell responses to a correlated noise (\"cloud\") stimulus designed to accentuate the important features of retinal processing in an in vitro preparation of mouse retina and found that this model accurately predicted ganglion cell responses at high spatiotemporal resolution. It identified multiple receptive fields (RFs) reflecting the main excitatory and suppressive components of the response of each neuron. Most significantly, our model succeeds where others fail, accurately identifying ON-OFF cells and segregating their distinct ON and OFF selectivity and demonstrating the presence of different types of suppressive receptive fields. In total, our computational approach offers rich description of ganglion cell computation and sets a foundation for relating retinal computation to retinal circuitry.

neuroscience↗

bioRxiv2018

Characterizing the nonlinear structure of shared variability in cortical neuron populations using neural networks

Sensory neurons often have variable responses to repeated presentations of the same stimulus, which can significantly degrade the information contained in those responses. Such variability is often shared across many neurons, which in principle can allow a decoder to mitigate the effects of such noise, depending on the structure of the shared variability and its relationship to sensory encoding at the population level. Latent variable models offer an approach for characterizing the structure of this shared variability in neural population recordings, although they have thus far typically been used under restrictive mathematical assumptions, such as assuming linear transformations between the latent variables and neural activity. Here we leverage recent advances in machine learning to introduce two nonlinear latent variable models for analyzing large-scale neural recordings. We first present a general nonlinear latent variable model that is agnostic to the stimulus tuning properties of the individual neurons, and is hence well suited for exploring neural populations whose tuning properties are not well characterized. This motivates a second class of model, the Generalized Affine Model, which simultaneously determines each neurons stimulus selectivity and a set of latent variables that modulate these stimulus responses both additively and multiplicatively. While these approaches can detect general nonlinear relationships in shared neural variability, we find that neural activity recorded in anesthetized primary visual cortex (V1) is best described by a single additive and single multiplicative latent variable, i.e., an \"affine model\". In contrast, application of the same models to recordings in awake macaque prefrontal cortex discover more general nonlinearities to compactly describe the population response variability. These results thus demonstrate how nonlinear latent variable models can be used to describe population variability, and suggest that a range of methods is necessary to study different brain regions under different experimental conditions.

neuroscience↗

bioRxiv2017

Spatial organization of the mouse auditory cortex to sound dynamics revealed using automated image segmentation

Sound stimuli are characterized by their rich spectral and temporal dynamic properties. Individual neurons in auditory cortex (ACX) encode both spectral and temporal aspects of sounds e.g. sound onset and/or offset. While the different fields of the ACX show gradients of frequency selectivity the large-scale organization of sound dynamics is unknown. We used widefield imaging of GCaMP6s in awake mouse ACX combined with a novel unsupervised image segmentation technique to investigate the spatiotemporal representation of sound onset and offset. Using this technique, we identified known auditory fields but also detected novel ACX areas. Furthermore, we found that ACX areas differed in their responses to tone onset and offset. Multiple areas were preferentially activated by tone offset, and on-response areas were more spatially localized than off-response areas. We also found tonotopy in off-responses. Together our results demonstrate a different spatial distribution of neurons across ACX for processing sound onsets versus offsets.

neuroscience↗