Biology subjects

Alonso, L. M.

Publications and source records attributed to Alonso, L. M..

5 recordsLinked to original sources

bioRxiv2024

Circuit function is more robust to changes in synaptic than intrinsic conductances

Circuit function results from both intrinsic conductances of network neurons and the synaptic conductances that connect them. In models of neural circuits, different combinations of maximal conductances can give rise to similar activity. We compared the robustness of a neural circuit to changes in their intrinsic versus synaptic conductances. To address this, we performed a sensitivity analysis on a population of conductance-based models of the pyloric network from the crustacean stomatogastric ganglion (STG). The model network consists of three neurons with nine currents: a sodium current (Na), three potassium currents (Kd, KCa, A-type), two calcium currents (CaS and CaT), a hyperpolarization-activated current (H), a non-voltage-gated leak current (leak), and a neuromodulatory current (MI). The model cells are connected by seven synapses of two types, glutamatergic and cholinergic. We produced one hundred models of the pyloric network that displayed similar activities with values of maximal conductances distributed over wide ranges. We evaluated the robustness of each model to changes in their maximal conductances. We found that individual models have different sensitivities to changes in their maximal conductances, both in their intrinsic and synaptic conductances. As expected the models become less robust as the extent of the changes increase. Despite quantitative differences in their robustness, we found that in all cases, the model networks are more sensitive to the perturbation of their intrinsic conductances than their synaptic conductances.

neuroscience↗

bioRxiv2021

A model of rapid homeostatic plasticity accounts for hidden, long-lasting changes in a neuronal circuit after exposure to high potassium.

Neural circuits must both function reliably and flexibly adapt to changes in their environment. We studied how both biological neurons and computational models respond to high potassium concentrations. Pyloric neurons of the crab stomatogastric ganglion (STG) initially become quiescent, then recover spiking activity in high potassium saline. The neurons retain this adaptation and recover more rapidly in subsequent high potassium applications, even after hours in control saline. We constructed a novel activity-dependent computational model that qualitatively captures these results. In this model, regulation of conductances is gated on and off depending on how far the neuron is from its target activity. This allows the model neuron to retain a trace of past perturbations even after it returns to its target activity in control conditions. Thus, perturbation, followed by recovery of normal activity, can hide cryptic changes in neuronal properties that are only revealed by subsequent perturbations.

neuroscience↗

bioRxiv2019

Temperature compensation in a small rhythmic circuit

Temperature affects the conductances and kinetics of the ionic channels that underlie neuronal activity. Each membrane conductance has a different characteristic temperature sensitivity, which raises the question of how neurons and neuronal circuits can operate robustly over wide temperature ranges. To address this, we employed computational models of the pyloric network of crabs and lobsters. We employed a landscape optimization scheme introduced previously (Alonso and Marder, 2019) to produce multiple different models that exhibit triphasic pyloric rhythms over a range of temperatures. We use the currentscapes introduced in (Alonso and Marder, 2019) to explore the dynamics of model currents and how they change with temperature. We found that temperature changes the relative contributions of the currents to neuronal activity so that rhythmic activity smoothly slides through changes in mechanisms. Moreover, the responses of the models to extreme perturbations--such as gradually decreasing a current type--are often qualitatively different at different temperatures.

neuroscience↗

bioRxiv2018

Visualization of the relative contributions of conductances in neuronal models with similar behavior and different conductance densities

Conductance-based models of neural activity produce large amounts of data that can be hard to visualize and interpret. Here we introduce two novel visualization methods to display the dynamics of the ionic currents, and to investigate how the contribution of each current changes in response to perturbation. We explored the solutions of a single compartment, conductance-based model of neural activity with seven voltage-gated ionic currents and a leak channel. We employed landscape optimization to find sets of maximal conductances that produce similar target activity and displayed the dynamics of the currents. We examined in detail six examples of a bursting model neuron that differ as much as 3-fold in the conductance densities of each of the 8 currents in the model. The maximal conductance of each current does not simply predict the importance of the current for neuronal dynamics. We then compared the effects of systematically reducing the conductances of each current for neuronal dynamics, and demonstrate that models that appear similar under starting conditions behave dramatically differently to the decreases in conductance densities. These examples provide heuristic insight into why individuals with similar behavior can nonetheless respond widely differently to perturbations.

neuroscience↗

bioRxiv2017

Learning to read the imprints of consciousness on global brain dynamics: an application to intra-operative monitoring of anesthesia

In daily life, in the operating room and in the laboratory, the operational way to assess wakefulness and consciousness is through responsiveness. A number of studies suggest that the awake, conscious state is not the default behavior of an assembly of neurons, but rather a very special state of activity that has to be actively maintained and curated to support its functional properties. Thus responsiveness is a feature that requires active maintenance, such as a homeostatic mechanism to balance excitation and inhibition. In this work we developed a method for monitoring such maintenance processes, focusing on a specific signature of their behavior derived from the theory of dynamical systems: stability analysis of dynamical modes. When such mechanisms are at work, their modes of activity are at marginal stability, neither damped (stable) nor exponentially growing (unstable) but rather hovering in between. We have previously shown that, conversely, under induction of anesthesia those modes become more stable and thus less responsive, then reversed upon emergence to wakefulness. We take advantage of this effect to build a single-trial classifier which detects whether a subject is awake or unconscious achieving high performance. We show that our approach can be developed into a mean for intra-operative monitoring of the depth of anesthesia, an application of fundamental importance to modern clinical practice.

neuroscience↗