Biology subjects

Bertram, R.

Publications and source records attributed to Bertram, R..

6 recordsLinked to original sources

bioRxiv2025

Heterogeneous monotonic and non-monotonic responses to odor in mitral/tufted glomeruli of the mouse olfactory bulb

Animals can recognize and discriminate between different odors and the same odor over a range of concentrations. Processing within the mouse olfactory bulb (OB) may be involved, yet the underlying mechanisms remain unclear. Each olfactory receptor neuron (ORN) type maps to the OB in olfactory receptor specific channels called glomeruli where they connect with the dendrites of mitral/tufted cells (MTCs), which project their axons to the rest of the brain. Differences between input and output define the functions carried out by a brain region. Using in vivo dual-color 2-photon Ca2+ imaging from the ORNs and MTCs innervating the same glomeruli in the mouse OB, we identified a novel MTC response type with a non-monotonic concentration-response relationship. We used mathematical modeling to demonstrate that non-monotonic response types are consistent with a form of interglomerular processing, which we propose is a mechanism to facilitate odor discrimination and the ability to achieve concentration-invariant odor perception. Graphical AbstractThe olfactory bulb input-output transformation was imaged using dual-color 2-photon imaging. (Top) Cartoon and histological examples of labeling the olfactory bulb input and output using spectrally distinct optical sensors. (Middle) Modeling and experimental work reveal that input neurons that respond with primarily monotonic concentration-response relationships are transformed into a mix of monotonic and non-monotonic responses. (Bottom) Olfactory bulb output state space for a population of 3 glomeruli with exclusively monotonic responses (black), and with both monotonic and non-monotonic responses (red). Each trajectory represents a cloud of points that reflects variations in the fluctuations of molecular components present in natural odor stimuli. Monotonic responses alone cluster together, while multiple response types broaden coverage of MTC state space. Greater coverage of state space makes it easier to discriminate one odor from another, even though the dimension of olfactory bulb state space is larger than 3. Key PointsO_LIAlthough the olfactory bulb is the first stage of olfactory sensory processing, its role in transforming sensory information remains poorly understood. C_LIO_LIDifferent olfactory receptor neuron types map to the olfactory bulb in olfactory receptor specific channels called glomeruli, where they interact with the dendrites of mitral/tufted cells, which project to the rest of the brain. C_LIO_LIWe used dual-color 2-photon Ca2+ imaging to image the input-output transformation olfactory receptor neuron (ORN) and mitral/tufted (MTC) glomeruli across a wide concentration range. C_LIO_LIThe ORN input to all tested glomeruli responded to increasing concentration changes with monotonic increases, while the corresponding MTCs outputs of each glomerulus responded with monotonic and non-monotonic changes. C_LIO_LIThe results were consistent with a mathematical model of the olfactory bulb incorporating feed-forward and lateral inhibition. C_LI

neuroscience↗

bioRxiv2024

Information on Oral Temperature is More Robustly Encoded Than Taste in Neurons of the Mouse Gustatory Cortex

Eating behaviors are influenced by the integration of gustatory, olfactory, and somatosensory signals, which all contribute to the perception of flavor. Although extensive research has explored the neural correlates of taste in the gustatory cortex (GC), less is known about its role in encoding thermal information. This study investigates the encoding of oral thermal and chemosensory signals by GC neurons compared to the oral somatosensory cortex. In this study, we recorded the spiking activity of more than 900 GC neurons and 500 neurons from the oral somatosensory cortex in mice allowed to freely lick small drops of gustatory stimuli or deionized water at varying non-nociceptive temperatures. We then developed and used a Bayesian-based analysis technique to assess neural classification scores based on spike rate and phase timing within the lick cycle. Our results indicate that GC neurons rely predominantly on rate information, although phase information is needed to achieve maximum accuracy, to effectively encode both chemosensory and thermosensory signals. GC neurons can effectively differentiate between thermal stimuli, excelling in distinguishing both large contrasts (14{degrees}C vs. 36{degrees}C) and, although less effectively, more subtle temperature differences. Finally, a direct comparison of the decoding accuracy of thermosensory signals between the two cortices reveals that while the somatosensory cortex showed higher overall accuracy, the GC still encodes significant thermosensory information. These findings highlight the GCs dual role in processing taste and temperature, emphasizing the importance of considering temperature in future studies of taste processing. Key PointsO_LIFlavor perception relies on gustatory, olfactory, and somatosensory integration, with the gustatory cortex (GC) central to taste processing. C_LIO_LIGC neurons also respond to temperature, but the specifics of how the GC processes taste and oral thermal stimuli remain unclear. C_LIO_LIThe focus of this study is on the role of GC neurons in the encoding of oral thermal information, particularly compared to the coding functions of the oral somatosensory cortex. C_LIO_LIWe found that while the somatosensory cortex shows a higher classification accuracy for distinguishing water temperature, the GC still encodes a substantial amount of thermosensory information. C_LIO_LIThese results emphasize the importance of including temperature as a key factor in future studies of cortical taste coding. C_LI

neuroscience↗

bioRxiv2024

Population Bursts in a Modular Neural Network as a Mechanism for Synchronized Activity in KNDy Neurons

The pulsatile activity of gonadotropin-releasing hormone neurons (GnRH neurons) is a key factor in the regulation of reproductive hormones. This pulsatility is orchestrated by a network of neurons that release the neurotransmitters kisspeptin, neurokinin B, and dynorphin (KNDy neurons), and produce episodic bursts of activity driving the GnRH neurons. We show in this computational study that the features of coordinated KNDy neuron activity can be explained by a neural network in which connectivity among neurons is modular. That is, a network structure consisting of clusters of highly-connected neurons with sparse coupling among the clusters. This modular structure, with distinct parameters for intracluster and intercluster coupling, also yields predictions for the differential effects of the co-released transmitters neurokinin B and dynorphin. In particular, it provides one possible explanation for how the excitatory neurotransmitter neurokinin B and the inhibitory neurotransmitter dynorphin can both increase the degree of synchronization among KNDy neurons. Author summarySince the discovery of a small population of hypothalamic neurons that secrete kisspeptin, neurokinin B, and dynorphin (KNDy neurons), there has been interest in their role as a pacemaker for the pulsatile release of key reproductive hormones. A fundamental question is what mechanism coordinates KNDy neuron activity to generate population bursts. Optical imaging of the KNDy network at single-neuron resolution has revealed that individual KNDy neurons participate in many, but not all, population bursts. It has also shown that the order in which the neurons are recruited in each burst could be highly determined in some animals but not in others. We demonstrate here that these observations can be explained by a neural network with a modular structure. We also show how such a structure can explain the paradoxical finding that both the excitatory neurotransmitter neurokinin B and the inhibitory neurotransmitter dynorphin can act to increase the degree of synchronization among the KNDy neurons.

neuroscience↗

bioRxiv2022

Active licking shapes cortical taste coding.

Neurons in the gustatory cortex (GC) represent taste through time-varying changes in their spiking activity. The predominant view is that the neural firing rate represents the sole unit of taste information. It is currently not known whether the phase of spikes relative to lick timing is used by GC neurons for taste encoding. To address this question, we recorded spiking activity from >500 single GC neurons in male and female mice permitted to freely lick to receive four liquid gustatory stimuli and water. We developed a set of data analysis tools to determine the ability of GC neurons to discriminate gustatory information and then to quantify the degree to which this information exists in the spike rate versus the spike timing or phase relative to licks. These tools include machine learning algorithms for classification of spike trains and methods from geometric shape and functional data analysis. Our results show that while GC neurons primarily encode taste information using a rate code, the timing of spikes is also an important factor in taste discrimination. A further finding is that taste discrimination using spike timing is improved when the timing of licks is considered in the analysis. That is, the interlick phase of spiking provides more information than the absolute spike timing itself. Overall, our analysis demonstrates that the ability of GC neurons to distinguish among tastes is best when spike rate and timing is interpreted relative to the timing of licks. Significance StatementNeurons represent information from the outside world via changes in their number of action potentials (spikes) over time. This study examines how neurons in the mouse gustatory cortex (GC) encode taste information when gustatory stimuli are experienced through the active process of licking. We use electrophysiological recordings and data analysis tools to evaluate the ability of GC neurons to distinguish tastants and then to quantify the degree to which this information exists in the spike rate versus the spike timing relative to licks. We show that the neurons ability to distinguish between tastes is higher when spike rate and timing are interpreted relative to the timing of licks, indicating that the lick cycle is a key factor for taste processing.

neuroscience↗

bioRxiv2019

Herbivore intraspecific variation and evolutionary divergence drive trophic cascade strength

AO_SCPLOWBSTRACTC_SCPLOWTrophic cascades--the indirect effect of predators on non-adjacent lower trophic levels--are important drivers of the structure and dynamics of ecological communities. However, the influence of intraspecific trait variation on the strength of trophic cascade remains largely unexplored, which limits our understanding of the mechanisms underlying ecological networks. Here we experimentally investigated how intraspecific difference among herbivore lineages specialized on different host plants influences the strength of trophic cascade in a terrestrial tritrophic system. We found that the occurrence and strength of the trophic cascade are strongly influenced by herbivores lineage and host-plant specialization but are not associated with density-dependent effects mediated by the growth rate of herbivore populations. Our findings stress the importance of intraspecific heterogeneities and evolutionary specialization as drivers of the strength of trophic cascades and underline that intraspecific variation should not be overlooked to decipher the joint influence of evolutionary and ecological factors on the functioning of multi-trophic interactions.

ecology↗

bioRxiv2019

Why Pacing Frequency Affects the Production of Early Afterdepolarizations in Cardiomyocytes: An Explanation Revealed by Slow/Fast Analysis of a Minimal Model

Early afterdepolarizations (EADs) are pathological voltage oscillations in cardiomyocytes that have been observed in response to a number of pharmacological agents and disease conditions. Phase-2 EADs consist of small voltage fluctuations that occur during the plateau of an action potential, typically under conditions in which the action potential is elongated. Although a single-cell behavior, EADs can lead to tissue-level arrhythmias, including ventricular tachycardia. Much is currently known about the biophysical mechanisms (i.e., the roles of ion channels and intracellular Ca2+ stores) for the various forms of EADs, due partially to the development and analysis of mathematical models. This includes the application of slow/fast analysis, which takes advantage of timescale separation inherent in the system to simplify its analysis. We take this further, using a minimal 3D model to demonstrate that the phase-2 EADs are canards that are formed in the neighborhood of a folded node singularity. This knowledge allows us to determine the number of EADs that can be produced for a given parameter set without performing computer simulations, and provides guidance on parameter changes that can facilitate or inhibit EAD production. With this approach, we demonstrate why periodic stimulation, as would occur in an intact heart, preferentially facilitates EAD production when applied at low frequencies. We also explain the origin of complex alternan dynamics that can occur with intermediate-frequency stimulation, in which varying numbers of EADs are produced with each stimulation. These revelations fall out naturally from an understanding of folded node singularities, but are hard or impossible to glean from a knowledge of the biophysical mechanism for EADs alone. Therefore, an understanding of the canard mechanism is a useful complement to an understanding of the biophysical mechanism that has been developed over years of experimental and computational investigations.

systems biology↗