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Stimulus intensity determines experience-dependent modifications in neocortical neuron firing rates.

ABSTRACT: Although subthreshold inputs of neocortical sensory neurons are broadly tuned, the spiking output is more restricted. These subthreshold inputs provide a substrate for stimulus intensity-dependent changes their spiking output, as well as for experience-dependent plasticity to alter firing properties. Here we investigated how different stimulus intensities modified the firing output of individual neurons in layer 2/3 of the mouse barrel cortex. Decreasing stimulus intensity over a 30-fold range lowered the firing rates evoked by principal whisker stimulation and reduced the overall size of the responding ensemble in whisker-undeprived animals. We then examined how these responses were changed after single-whisker experience (SWE). After 7 days of SWE, the mean magnitude of response to spared whisker stimulation at the highest stimulus intensity was not altered. However, lower-intensity whisker stimulation revealed a more than 10-fold increase in mean firing output compared with control animals. Also, under control conditions, only ~15% of neurons showed any firing at low stimulus intensity, compared with more than 70% of neurons after SWE. However, response changes measured in the immediately surrounding representations were detected only for the highest stimulus intensity. Overall, these data showed that the measurement of experience-dependent changes in the spike output of neocortical neurons was highly dependent upon stimulus intensity.

SUBMITTER: Glazewski S

PROVIDER: S-EPMC4331261 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Modern Graph Neural Networks (GNNs) provide opportunities to study the determinants underlying the complex activity patterns of biological neuronal networks. In this study, we applied GNNs to a large-scale electrophysiological dataset of rodent primary neuronal networks obtained by means of high-density microelectrode arrays (HD-MEAs). HD-MEAs allow for long-term recording of extracellular spiking activity of individual neurons and networks and enable the extraction of physiologically relevant features at the single-neuron and population level. We employed established GNNs to generate a combined representation of single-neuron and connectivity features obtained from HD-MEA data, with the ultimate goal of predicting changes in single-neuron firing rate induced by a pharmacological perturbation. The aim of the main prediction task was to assess whether single-neuron and functional connectivity features, inferred under baseline conditions, were informative for predicting changes in neuronal activity in response to a perturbation with Bicuculline, a GABA A receptor antagonist. Our results suggest that the joint representation of node features and functional connectivity, extracted from a baseline recording, was informative for predicting firing rate changes of individual neurons after the perturbation. Specifically, our implementation of a GNN model with inductive learning capability (GraphSAGE) outperformed other prediction models that relied only on single-neuron features. We tested the generalizability of the results on two additional datasets of HD-MEA recordings-a second dataset with cultures perturbed with Bicuculline and a dataset perturbed with the GABA A receptor antagonist Gabazine. GraphSAGE models showed improved prediction accuracy over other prediction models. Our results demonstrate the added value of taking into account the functional connectivity between neurons and the potential of GNNs to study complex interactions between neurons.

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Stimulus intensity determines experience-dependent modifications in neocortical neuron firing rates.

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