Biology subjects

Barreiro, A. K.

Publications and source records attributed to Barreiro, A. K..

6 recordsLinked to original sources

bioRxiv2025

Is critical brain dynamics more prevalent than previously thought?

The hypothesis that the brain operates near criticality has far-reaching implications for brain function and is supported by growing experimental evidence. Observations of scale-invariant brain activity agree with this hypothesis, but what about when brain activity is not scale-invariant? Should we reject the criticality hypothesis When power-laws poorly fit the data or when strong oscillations occur (dominated by a specific time scale)? Here we show several ways that criticality can be hidden from traditional data analytic approaches, leading to false negative conclusions. We use a parsimonious high-dimensional model to demonstrate how neural systems may separate different dynamical modes into different subspaces, simultaneously generating non-critical dynamics, critical oscillations, and scale-invariant avalanches. Our results point to a need for new methods capable of revealing hidden criticality and suggest that criticality could be more prevalent than previously thought, hidden in subspaces not readily revealed by standard data analyses. Does the brain operate in a dynamical regime close to a critical phase transition? This question is fundamental; the nature of neural computation is strongly impacted by whether a system is close to or far from criticality [1-5]. Evidence suggesting that the brain operates close to criticality has accumulated at an accelerating pace over the past two decades (a recent meta analysis found 31 reports in 2024 alone [2]). The two most common types of experimental evidence have been based on neuronal avalanche analysis [1, 6, 7] and long-range temporal correlation analysis [8-12]. Both these approaches examine a one-dimensional time series of collective neural activity, averaged over large populations of neurons, seeking temporally-correlated, scale-invariant fluctuations that obey specific scaling laws predicted by theory. In many cases, measurements agree with these predictions; the success and persistence of the criticality hypothesis rests on these cases. However, it is not difficult to find measurements that do not agree, with fluctuations poorly described by power-laws and scaling laws. Another common observation that, naively, seems to contradict criticality is oscillatory brain activity with a particular dominant frequency. At first glance, either the lack of power-laws or the prominence of a dominant oscillatory time scale appear to contradict the criticality hypothesis. What should we conclude from these apparently contradictory observations? Assuming that all the experimental observations are sound and valid, there are two possible explanations, not necessarily mutually exclusive. First, it could be that there are biophysical control parameters constantly in flux in the brain, causing shifts in proximity to criticality. In this scenario, non-scale-invariant data reflect true deviations from criticality and we should conclude that the brain is, at times, closer to and, at other times, further from criticality. This view has been proposed in multiple previous studies and is consistent with substantial experimental evidence [6, 13-19]. However, here we argue that there is an important, but rarely considered, second plausible explanation. It could be that traditional methods for assessing criticality can be fooled. In this case, the system dynamics could be truly critical and scale-invariant, but not visible to traditional methods. We first show why such hidden criticality is plausible considering general mathematical arguments and relevant experimental evidence. Then, we use a parsimonious computational model to demonstrate concrete examples of ground truth scale-invariant critical dynamics that are missed by traditional analyses of population average activity. Moreover, our model shows how critical oscillations, avalanches, and other non-critical modes of dynamics can coexist, by separating each different mode into a distinct low-dimensional subspace. All together, our results suggest that the combination of these various subspaces make up the high-dimensional dynamical system commonly observed in the brain. Our results reconcile multiple, seemingly discrepant, experimental observations and describe prospects for new methods that can reveal hidden criticality.

neuroscience↗

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↗

bioRxiv2024

Cortex deviates from criticality during action and deep sleep: a temporal renormalization group approach

The hypothesis that the brain operates near criticality explains observations of complex, often scale-invariant, neural activity. However, the brain is not static, its dynamical state varies depending on what an organism is doing. Neurons often become more synchronized (ordered) during unconsciousness and more desynchronized (disordered) in highly active awake conditions. Are all these states equidistant from criticality; if not, which is closest? The fundamental physics of how systems behave near criticality came from renormalization group (RG) theory, but RG for neural systems remains largely undeveloped. Here we developed a temporal RG (tRG) theory for analysis of typical neuroscience data. We mathematically identified multiple types of criticality (tRG fixed points) and developed tRG-driven data analytic methods to assess proximity to each fixed point based on relatively short time series. Unlike traditional methods for studying criticality in neural systems, our tRG approach allows time-resolved measurements of distance from criticality in experiments at behaviorally relevant timescales. We apply our approach to recordings of spike activity in mouse visual cortex, showing that the relaxed, awake state is closest to criticality. When arousal shifts away from this state - either increasing in more active awake states or decreasing in deep sleep - cortical dynamics deviate from criticality.

neuroscience↗

bioRxiv2023

Modality of odor perception is transmitted to cortical brain regions from the olfactory bulb

Odor perception is the impetus for important animal behaviors with two predominate modes of processing: odors pass through the front of the nose (orthonasal) while inhaling and sniffing, or through the rear (retronasal) during exhalation and while eating. Despite the importance of olfaction for an animals well-being and that ortho and retro naturally occur, it is unknown how the modality (ortho versus retro) is even transmitted to cortical brain regions, which could significantly affect how odors are processed and perceived. Using multi-electrode array recordings in tracheotomized anesthetized rats, which decouples ortho-retro modality from breathing, we show that mitral cells in rat olfactory bulb can reliably and directly transmit ortho versus retronasal modality with ethyl butyrate, a common food odor. Drug manipulations affecting synaptic inhibition via GABAA lead to worse decoding of ortho versus retro, independent of whether overall inhibition increases or decreases, suggesting that the olfactory bulb circuit may naturally favor encoding this important aspect of odors. Detailed data analysis paired with a firing rate model that captures population trends in spiking statistics shows how this circuit can encode odor modality. We have not only demonstrated that ortho/retro information is encoded to downstream brain regions, but also use modeling to demonstrate a plausible mechanism for this encoding: due to synaptic adaptation, it is the slower time course of the retronasal stimulation that causes retronasal responses to be stronger and less sensitive to inhibitory drug manipulations than orthonasal responses. New and NoteworthyWhether ortho (sniffing odors) versus retro (exhalation and eating) is encoded from the olfactory bulb to other brain areas is not completely known. Using multi-electrode array recordings in anesthetized rats, we show that the olfactory bulb transmits this information downstream via spikes. Altering inhibition degrades ortho/retro information on average. We use theory and computation to explain our results, which should have implications on cortical processing considering that only food odors occur retronasally.

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↗