Biology subjects

Gautam, S. H.

Publications and source records attributed to Gautam, S. H..

7 recordsLinked to original sources

bioRxiv2024

Coding odor modality in piriform cortex efficiently with low-dimensional subspaces: a Shared Covariance Decoding approach

A fundamental question in neuroscience is how sensory signals are decoded from noisy cortical activity. We address this question in the olfactory system, decoding the route by which odorants arrive into the nasal cavity: through the nostrils (orthonasal), or through the back of the throat (retronasal). We recently showed with modeling and novel experiments on anesthetized rats that orthonasal versus retronasal modality information is encoded in the olfactory bulb (OB, a pre-cortical region). However, key questions remain: is modality information transmitted from OB to anterior piriform cortex (aPC)? How can this information be extracted from a much noisier cortical population with overall less firing? With simultaneous spike recordings of populations of neurons in OB and aPC, we show that an unsupervised and biologically plausible algorithm, Shared Covariance Decoding (SCD), can indeed linearly encode modality in low dimensional subspaces. Specifically, SCD improves encoding of ortho/retro in aPC compared to Fishers linear discriminant analysis (LDA). Consistent with our theoretical analysis, when noise correlations between OB and aPC are low and OB well-encodes modality, modality in aPC tends to be encoded optimally with SCD. We observe that with several algorithms (LDA, SCD, optimal) that the decoding accuracy distributions are invariant when GABAA (ant-)agonists (bicuculline and muscimol) are applied to OB, consistent invariance in population firing in aPC. Overall, we show modality information can be encoded efficiently in piriform cortex.

neuroscience↗

bioRxiv2023

When random variation results in functional significance

Many functional properties vary dramatically across neurons in cerebral cortex. Two fundamental goals of systems neuroscience are to determine which neurons execute which functions and how the different functional properties of a neuron are related. Often, it is assumed that if two properties are uncorrelated, then there is no important relationship to report. Here we show that this assumption can lead to wrong conclusions; functional segregation can emerge, by chance, due to random variation when that variation is distributed according to skewed, heavy-tailed distributions. We reinterpret the results we previously reported in Kells et al 2019 [1], showing that they are a prime example of functional segregation due to random variation.

neuroscience↗

bioRxiv2023

Low dimensional criticality embedded in high dimensional awake brain dynamics

Whether cortical neurons operate in a strongly or weakly correlated dynamical regime determines fundamental information processing capabilities and has fueled decades of debate. Here we offer a resolution of this debate; we show that two important dynamical regimes, typically considered incompatible, can coexist in the same local cortical circuit by separating them into two different subspaces. In awake mouse motor cortex, we find a low-dimensional subspace with large fluctuations consistent with criticality - a dynamical regime with moderate correlations and multi-scale information capacity and transmission. Orthogonal to this critical subspace, we find a high-dimensional subspace containing a desynchronized dynamical regime, which may optimize input discrimination. The critical subspace is apparent only at long timescales, which explains discrepancies among some previous studies. Using a computational model, we show that the emergence of a low-dimensional critical subspace at large timescale agrees with established theory of critical dynamics. Our results suggest that cortex leverages its high dimensionality to multiplex dynamical regimes across different subspaces. TeaserTemporal coarse-graining reveals a low-dimensional critical subspace coexistent with a desynchronized subspace in awake cortex.

neuroscience↗

bioRxiv2022

Sensory input to cortex encoded on low-dimensional periphery-correlated subspaces

As information about the world is conveyed from the sensory periphery to central neural circuits, it mixes with complex ongoing cortical activity. How do neural populations keep track of sensory signals, separating them from noisy ongoing activity? Here we show that sensory signals are encoded more reliably in certain low-dimensional subspaces. These coding subspaces are defined by correlations between neural activity in primary sensory cortex and upstream sensory brain regions; the most correlated dimensions were best for decoding. We analytically show that these correlation-based coding subspaces improve, reaching optimal limits (without an ideal observer) as noise correlations between cortex and upstream regions are reduced. We show that this principle generalizes across diverse sensory stimuli in the olfactory system and the visual system of awake mice. Our results demonstrate an algorithm the cortex may use to multiplex different functions, processing sensory input in low dimensional subspaces separate from other ongoing functions. Significance statementTraditionally, primary sensory cortex was thought to have one job - processing sensory signals. As technical advances allow more holistic measurements of the brain and body in action, it has become clear that primary sensory cortex is involved with many other aspects of brain function, not just dealing with sensory input. How can a single neural circuit juggle multiple jobs simultaneously? Here we use numerical, analytical, and experimental methods to demonstrate an algorithm the brain may use to solve this problem by separating different jobs into different subspaces defined by correlations between primary sensory cortex and the brain regions that source the sensory input signals.

neuroscience↗

bioRxiv2021

Analyzing the differences in olfactory bulb mitral cell spiking with ortho- and retronasal stimulation

The majority of olfaction studies focus on orthonasal stimulation where odors enter via the front nasal cavity, while retronasal olfaction, where odors enter the rear of the nasal cavity during feeding, is understudied. The processing of retronasal odors via coordinated spiking of neurons in the olfactory bulb (OB) is largely unknown. To this end, we use multi-electrode array in vivo recordings of rat OB mitral cells (MC) in response to a food odor with both modes of stimulation, and find significant differences in evoked firing rates and spike count covariances (i.e., noise correlations). To better understand these differences, we develop a single-compartment biophysical OB model that is able to reproduce key properties of important OB cell types. Prior experiments in olfactory receptor neurons (ORN) showed retro stimulation yields slower and spatially smaller ORN inputs than with ortho, yet whether this is consequential for OB activity remains unknown. Indeed with these specifications for ORN inputs, our OB model captures the trends in our OB data. We also analyze how first and second order ORN input statistics dynamically transfer to MC spiking statistics with a phenomenological linear-nonlinear filter model, and find that retro inputs result in larger temporal filters than ortho inputs. Finally, our models show that the temporal profile of ORN is crucial for capturing our data and is thus a distinguishing feature between ortho and retro stimulation, even at the OB. Using data-driven modeling, we detail how ORN inputs result in differences in OB dynamics and MC spiking statistics. These differences may ultimately shape how ortho and retro odors are coded. Author summaryOlfaction is a key sense for many cognitive and behavioral tasks, and is particularly unique because odors can naturally enter the nasal cavity from the front or rear, i.e., ortho- and retro-nasal, respectively. Yet little is known about the differences in coordinated spiking in the olfactory bulb with ortho versus retro stimulation, let alone how these different modes of olfaction may alter coding of odors. We simultaneously record many cells in rat olfactory bulb to assess the differences in spiking statistics, and develop a biophysical olfactory bulb network model to study the reasons for these differences. Using theoretical and computational methods, we find that the olfactory bulb transfers input statistics differently for retro stimulation relative to ortho stimulation. Furthermore, our models show that the temporal profile of inputs is crucial for capturing our data and is thus a distinguishing feature between ortho and retro stimulation, even at the olfactory bulb. Understanding the spiking dynamics of the olfactory bulb with both ortho and retro stimulation is a key step for ultimately understanding how the brain codes odors with different modes of olfaction.

neuroscience↗

bioRxiv2021

Odor-evoked Increases in Spiking Variability in the Olfactory Bulb

At the onset of sensory stimulation, the variability and co-variability of spiking activity is widely reported to decrease, especially in cortex. Considering the potential benefits of such decreased variability for coding, it has been suggested that this could be a general principle governing all sensory systems. Here we show that this is not so. We recorded mitral cells in olfactory bulb (OB) of anesthetized rats and found increased variability and co-variability of spiking at the onset of olfactory stimulation. Using models and analysis, we predicted that these increases arise due to network interactions within OB, without increasing variability of input from the nose. We tested and confirmed this prediction in awake animals with direct optogenetic stimulation of OB to circumvent the pathway through the nose. Our results establish increases in spiking variability at stimulus onset as a viable alternative coding strategy to the more commonly observed decreases in variability in many cortical systems. SummaryThe spiking variability of neural networks has important implications for how information is encoded to higher brain regions. It has been well documented by numerous labs in many cortical and motor regions that spiking variability decreases with stimulus onset, yet whether this principle holds in the olfactory bulb has not been tested. In stark contrast to this common view, we demonstrate that the onset of sensory input can cause an increase in the variability of neural activity in the mammalian olfactory bulb. We show this in both anesthetized and awake rodents. Furthermore, we use computational models to describe the mechanisms of this phenomenon. Our finding establish sensory evoked increases in spiking variability as a viable alternative.

neuroscience↗

bioRxiv2020

Reduced complexity of brain and behavior due to MeCP2 disruption and excessive inhibition

Rett syndrome (RTT) is a devastating neurodevelopmental disorder, caused by disruptions to the MECP2 gene, and resulting in severe cognitive and motor impairment. Previous work strongly suggests that healthy MECP2 function is required to have a normal balance between excitatory and inhibitory neurons. However, the details of how neural circuit dynamics and motor function are disrupted remain unclear. How might imbalanced E/I cause problems for motor function in RTT? We addressed this question in the motor cortex of awake, freely behaving rats, comparing normal rats with a transgenic rat model of RTT. We recorded single-unit spiking activity while simultaneously recording body movement of the rats. We found that RTT rats tend to have excessive synchrony among neurons in the motor cortex and less complex body movements. Importantly, greater synchrony was correlated with greater stereotypy of relationships between neurons and body movements. To further test how our observations were related to an E/I imbalance, we pharmacologically altered inhibitory synaptic interactions. We were able to recapitulate many of the phenomena we found in MECP2-deficient rats by enhancing inhibition in normal rats. Our results suggest that RTT-related E/I imbalance in the motor cortex gives rise to excessive synchrony and, consequently, a stereotyped motor code, which may underlie abnormal motor function in RTT.

neuroscience↗