How neuromodulators control the excitability of neurons.
<p><b>A:</b> Cortical pyramidal neuron in control condition. Electrophysiology data (black), multiple fitted AdEx models (grey), and one selected model (orange, same colour throughout the paper) are presented together (Voltage traces, solid, on bottom, adaptation current, dashed, o...
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2025
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| _version_ | 1852014359820107776 |
|---|---|
| author | Domenico Guarino (20428000) |
| author2 | Ilaria Carannante (12526456) Alain Destexhe (259217) |
| author2_role | author author |
| author_facet | Domenico Guarino (20428000) Ilaria Carannante (12526456) Alain Destexhe (259217) |
| author_role | author |
| dc.creator.none.fl_str_mv | Domenico Guarino (20428000) Ilaria Carannante (12526456) Alain Destexhe (259217) |
| dc.date.none.fl_str_mv | 2025-12-01T18:52:21Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pcbi.1013765.g001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/How_neuromodulators_control_the_excitability_of_neurons_/30756177 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Genetics Neuroscience Evolutionary Biology Science Policy Biological Sciences not elsewhere classified yet biologically accurate electrophysiological properties induced dimensionality reduction technique adaptive exponential integrate switching &# 8221 scaling &# 8221 analyse electrophysiological data xlink "> first different neuromodulators remap brain </ p altering neuronal excitability xlink "> excitability landscape data using &# 8220 &# 8211 still lack source python seven types readily applicable published papers parameters space parameter space overlapping clusters offering experimenters neuronal activity neuromodulated conditions much work integrative perspective five neuromodulators five areas fire model extract features existing behaviour dynamical regimes compact description cohesive picture based workflow another species analyses revealed adex ). |
| dc.title.none.fl_str_mv | How neuromodulators control the excitability of neurons. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p><b>A:</b> Cortical pyramidal neuron in control condition. Electrophysiology data (black), multiple fitted AdEx models (grey), and one selected model (orange, same colour throughout the paper) are presented together (Voltage traces, solid, on bottom, adaptation current, dashed, on top). The features measured over the traces were: time to first, second, third, and last spike (red bars 1, 2, 3, 4), inverse of the first and last interspike interval (bars 5 and 6), and firing frequency. <b>B:</b> The same pyramidal neuron in Methacholine (MCh) bath application. The voltage and adaptation traces are appreciably different. The same features as in A were measured. <b>C:</b> To study the relationship between the two conditions, we applied a dimensional reduction technique (PCA) to the parameters of fitted (grey points) and selected (centroid, same colour as in the respective panels) models. In this space, where the top three features contributing to the first principal component were [V_peak, tau_w, g_L] and the top contributors of the second principal component were [b, Delta_T, t_ref], the control and modulated clusters were distinct (Silhouette score ). <b>D:</b> xcitability landscape, where each fitted model is projected onto two biophysical ratios. The control condition (orange) is near the bottom of the excitability landscape, below the diagonal, in Class II, where the resting state is lost by a subcritical Hopf. Methacholine (violet) scales the mean up and to the right, producing stronger, faster subthreshold adaptation relative to leak. The mean moves closer to the diagonal and to switching (small circles: individual fits; filled symbols with black arrows: condition means and mean Control→Modulated displacement; dashed line: Bogdanov–Takens boundary). <b>E, F:</b> To understand the effects of neuromodulation in terms of excitability dynamics, we plotted the V (solid) and w (dashed) nullclines, with their intersection corresponding to fixed points. With respect to control (orange), MCh (violet) increases the excitability of the neuron by lowering the slope of the <i>V</i>-nullcline left branch, while also reducing the adaptation rate (<i>w</i>-nullcline slope).</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_0f59d90fe9727643fcfcfcb516b0754f |
| identifier_str_mv | 10.1371/journal.pcbi.1013765.g001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30756177 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | How neuromodulators control the excitability of neurons.Domenico Guarino (20428000)Ilaria Carannante (12526456)Alain Destexhe (259217)GeneticsNeuroscienceEvolutionary BiologyScience PolicyBiological Sciences not elsewhere classifiedyet biologically accurateelectrophysiological properties induceddimensionality reduction techniqueadaptive exponential integrateswitching &# 8221scaling &# 8221analyse electrophysiological dataxlink "> firstdifferent neuromodulators remapbrain </ paltering neuronal excitabilityxlink ">excitability landscapedata using&# 8220&# 8211still lacksource pythonseven typesreadily applicablepublished papersparameters spaceparameter spaceoverlapping clustersoffering experimentersneuronal activityneuromodulated conditionsmuch workintegrative perspectivefive neuromodulatorsfive areasfire modelextract featuresexisting behaviourdynamical regimescompact descriptioncohesive picturebased workflowanother speciesanalyses revealedadex ).<p><b>A:</b> Cortical pyramidal neuron in control condition. Electrophysiology data (black), multiple fitted AdEx models (grey), and one selected model (orange, same colour throughout the paper) are presented together (Voltage traces, solid, on bottom, adaptation current, dashed, on top). The features measured over the traces were: time to first, second, third, and last spike (red bars 1, 2, 3, 4), inverse of the first and last interspike interval (bars 5 and 6), and firing frequency. <b>B:</b> The same pyramidal neuron in Methacholine (MCh) bath application. The voltage and adaptation traces are appreciably different. The same features as in A were measured. <b>C:</b> To study the relationship between the two conditions, we applied a dimensional reduction technique (PCA) to the parameters of fitted (grey points) and selected (centroid, same colour as in the respective panels) models. In this space, where the top three features contributing to the first principal component were [V_peak, tau_w, g_L] and the top contributors of the second principal component were [b, Delta_T, t_ref], the control and modulated clusters were distinct (Silhouette score ). <b>D:</b> xcitability landscape, where each fitted model is projected onto two biophysical ratios. The control condition (orange) is near the bottom of the excitability landscape, below the diagonal, in Class II, where the resting state is lost by a subcritical Hopf. Methacholine (violet) scales the mean up and to the right, producing stronger, faster subthreshold adaptation relative to leak. The mean moves closer to the diagonal and to switching (small circles: individual fits; filled symbols with black arrows: condition means and mean Control→Modulated displacement; dashed line: Bogdanov–Takens boundary). <b>E, F:</b> To understand the effects of neuromodulation in terms of excitability dynamics, we plotted the V (solid) and w (dashed) nullclines, with their intersection corresponding to fixed points. With respect to control (orange), MCh (violet) increases the excitability of the neuron by lowering the slope of the <i>V</i>-nullcline left branch, while also reducing the adaptation rate (<i>w</i>-nullcline slope).</p>2025-12-01T18:52:21ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pcbi.1013765.g001https://figshare.com/articles/figure/How_neuromodulators_control_the_excitability_of_neurons_/30756177CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/307561772025-12-01T18:52:21Z |
| spellingShingle | How neuromodulators control the excitability of neurons. Domenico Guarino (20428000) Genetics Neuroscience Evolutionary Biology Science Policy Biological Sciences not elsewhere classified yet biologically accurate electrophysiological properties induced dimensionality reduction technique adaptive exponential integrate switching &# 8221 scaling &# 8221 analyse electrophysiological data xlink "> first different neuromodulators remap brain </ p altering neuronal excitability xlink "> excitability landscape data using &# 8220 &# 8211 still lack source python seven types readily applicable published papers parameters space parameter space overlapping clusters offering experimenters neuronal activity neuromodulated conditions much work integrative perspective five neuromodulators five areas fire model extract features existing behaviour dynamical regimes compact description cohesive picture based workflow another species analyses revealed adex ). |
| status_str | publishedVersion |
| title | How neuromodulators control the excitability of neurons. |
| title_full | How neuromodulators control the excitability of neurons. |
| title_fullStr | How neuromodulators control the excitability of neurons. |
| title_full_unstemmed | How neuromodulators control the excitability of neurons. |
| title_short | How neuromodulators control the excitability of neurons. |
| title_sort | How neuromodulators control the excitability of neurons. |
| topic | Genetics Neuroscience Evolutionary Biology Science Policy Biological Sciences not elsewhere classified yet biologically accurate electrophysiological properties induced dimensionality reduction technique adaptive exponential integrate switching &# 8221 scaling &# 8221 analyse electrophysiological data xlink "> first different neuromodulators remap brain </ p altering neuronal excitability xlink "> excitability landscape data using &# 8220 &# 8211 still lack source python seven types readily applicable published papers parameters space parameter space overlapping clusters offering experimenters neuronal activity neuromodulated conditions much work integrative perspective five neuromodulators five areas fire model extract features existing behaviour dynamical regimes compact description cohesive picture based workflow another species analyses revealed adex ). |