Functional relevance of homeostatic intrinsic plasticity in neurons and networks

Maintaining the intrinsic excitability of neurons is crucial for stable brain activity. This can be achieved by the homeostatic regulation of membrane ion channel conductances, although it is not well understood how these processes influence broader aspects of neuron and network function. One of the many mechanisms which contribute towards this task is the modulation of potassium channel conductances by activity-dependent nitric oxide signalling. Here, we first investigate this mechanism in a conductance-based neuron model. By fitting the model to experimental data we find that nitric oxide signalling improves synaptic transmission fidelity at high firing rates, but that there is an increase in the metabolic cost of action potentials associated with this improvement. Although the improvement in function had been observed previously in experiment, the metabolic constraint was unknown. This additional constraint provides a plausible explanation for the selective activation of nitric oxide signalling only at high firing rates. In addition to mediating homeostatic control of intrinsic excitability, nitric oxide can diffuse freely across cell membranes, providing a unique mechanism for neurons to communicate within a network, independent of synaptic connectivity. We next conduct a theoretical investigation of the distinguishing roles of diffusive homeostasis mediated by nitric oxide in comparison with canonical non-diffusive homeostasis in cortical networks. We find that both forms of homeostasis robustly maintain stable activity. However, the resulting networks differ, with diffusive homeostasis maintaining substantial heterogeneity in activity levels of individual neurons, a feature disrupted in networks with non-diffusive homeostasis. This results in networks capable of representing input heterogeneity, and linearly responding over a broader range of inputs than those undergoing non-diffusive homeostasis.We further show that diffusive homeostasis interferes less than non-diffusive homeostasis in the synaptic weight dynamics of networks undergoing Hebbian plasticity. Overall, these results suggest a novel homeostatic mechanism for maintaining stable network activity while simultaneously minimising metabolic cost and conserving network functionality

A multi-decade record of high quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT)

The Surface Ocean CO2 Atlas (SOCAT) is a synthesis of quality-controlled fCO2 (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with regular updates. Version 3 of SOCAT has 14.7 million fCO2 values from 3646 data sets covering the years 1957 to 2014. This latest version has an additional 4.6 million fCO2 values relative to version 2 and extends the record from 2011 to 2014. Version 3 also significantly increases the data availability for 2005 to 2013. SOCAT has an average of approximately 1.2 million surface water fCO2 values per year for the years 2006 to 2012. Quality and documentation of the data has improved. A new feature is the data set quality control (QC) flag of E for data from alternative sensors and platforms. The accuracy of surface water fCO2 has been defined for all data set QC flags. Automated range checking has been carried out for all data sets during their upload into SOCAT. The upgrade of the interactive Data Set Viewer (previously known as the Cruise Data Viewer) allows better interrogation of the SOCAT data collection and rapid creation of high-quality figures for scientific presentations. Automated data upload has been launched for version 4 and will enable more frequent SOCAT releases in the future. High-profile scientific applications of SOCAT include quantification of the ocean sink for atmospheric carbon dioxide and its long-term variation, detection of ocean acidification, as well as evaluation of coupled-climate and ocean-only biogeochemical models. Users of SOCAT data products are urged to acknowledge the contribution of data providers, as stated in the SOCAT Fair Data Use Statement. This ESSD (Earth System Science Data) “living data” publication documents the methods and data sets used for the assembly of this new version of the SOCAT data collection and compares these with those used for earlier versions of the data collection (Pfeil et al., 2013; Sabine et al., 2013; Bakker et al., 2014). Individual data set files, included in the synthesis product, can be downloaded here: doi:10.1594/PANGAEA.849770. The gridded products are available here: doi:10.3334/CDIAC/OTG.SOCAT_V3_GRID

A Diffusive Homeostatic Signal Maintains Neural Heterogeneity and Responsiveness in Cortical Networks

Gaseous neurotransmitters such as nitric oxide (NO) provide a unique and often overlooked mechanism for neurons to communicate through diffusion within a network, independent of synaptic connectivity. NO provides homeostatic control of intrinsic excitability. Here we conduct a theoretical investigation of the distinguishing roles of NO-mediated diffusive homeostasis in comparison with canonical non-diffusive homeostasis in cortical networks. We find that both forms of homeostasis provide a robust mechanism for maintaining stable activity following perturbations. However, the resulting networks differ, with diffusive homeostasis maintaining substantial heterogeneity in activity levels of individual neurons, a feature disrupted in networks with non-diffusive homeostasis. This results in networks capable of representing input heterogeneity, and linearly responding over a broader range of inputs than those undergoing non-diffusive homeostasis. We further show that these properties are preserved when homeostatic and Hebbian plasticity are combined. These results suggest a mechanism for dynamically maintaining neural heterogeneity, and expose computational advantages of non-local homeostatic processes

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Functional Relevance of Homeostatic Intrinsic Plasticity in Neurons and Networks

Maintaining the intrinsic excitability of neurons is crucial for stable brain activity. This can be achieved by the homeostatic regulation of membrane ion channel conductances, although it is not well understood how these processes influence broader aspects of neuron and network function. One of the many mechanisms which contribute towards this task is the modulation of potassium channel conductances by activity-dependent nitric oxide signalling. Here, we first investigate this mechanism in a conductance-based neuron model. By fitting the model to experimental data we find that nitric oxide signalling improves synaptic transmission fidelity at high firing rates, but that there is an increase in the metabolic cost of action potentials associated with this improvement. Although the improvement in function had been observed previously in experiment, the metabolic constraint was unknown. This additional constraint provides a plausible explanation for the selective activation of nitric oxide signalling only at high firing rates. In addition to mediating homeostatic control of intrinsic excitability, nitric oxide can diffuse freely across cell membranes, providing a unique mechanism for neurons to communicate within a network, independent of synaptic connectivity. We next conduct a theoretical investigation of the distinguishing roles of diffusive homeostasis mediated by nitric oxide in comparison with canonical non-diffusive homeostasis in cortical networks. We find that both forms of homeostasis robustly maintain stable activity. However, the resulting networks differ, with diffusive homeostasis maintaining substantial heterogeneity in activity levels of individual neurons, a feature disrupted in networks with non-diffusive homeostasis. This results in networks capable of representing input heterogeneity, and linearly responding over a broader range of inputs than those undergoing non-diffusive homeostasis. We further show that diffusive homeostasis interferes less than non-diffusive homeostasis in the synaptic weight dynamics of networks undergoing Hebbian plasticity. Overall, these results suggest a novel homeostatic mechanism for maintaining stable network activity while simultaneously minimising metabolic cost and conserving network functionality.Joint Doctoral Program in Neuroinformatics.  https://www.kth.se/eurospinPublic defence Monday, 23 May 2016, at 9.00 a.m. in Room 1,15, Meeting and Training suite, 1st Floor, Library, Univ Edinburgh, School of Informatics (can be joined via videoconference from Konstantinbågen, Drottning Kristinas väg 4, Kungliga Tekniska högskolan, Stockholm.QC 20160426</p

sweeney-2015-fig-4

<p>Sample data for https://github.com/yannaodh/sweeney-2015 , Figure 4</p

Steady-state firing thresholds of diffusive and non-diffusive homeostasis.

<p><b>(A)</b> Distributions of firing thresholds after homeostasis from network simulations, receiving independent Poisson input drawn from a Gaussian distribution. <b>(B)</b> Steady-state firing thresholds plotted against external inputs received during homeostasis. <b>(C)</b> Variance of the steady-state firing rate distribution as the diffusion coefficient <i>D</i> is varied (<i>D</i> = 1000 <i>μ</i>m²s⁻¹ in panels A-B). <b>(D)</b> Variance of the steady-state firing rate distribution as the input rate width is varied, for networks with diffusive (<i>D</i> = 1000 <i>μ</i>m²s⁻¹) and non-diffusive homeostasis (input rate width = 10 Hz in panels A-B).</p

Performance advantages of networks with diffusive over non-diffusive homeostasis.

<p><b>(A)</b> Response of each network to a time-varying input pattern (dotted black line). Colored lines show normalized firing rate deviations from the mean population rate of those neurons receiving inputs. <b>(B)</b> RMS errors between the normalized input pattern and normalized response of each network (1000 s pattern presentation and 11 networks in each case). ** <i>p</i> < 0.01 from a two-sided Kolmogorov-Smirnov test. <b>(C)</b> Diagram showing network inputs during an orientation decoding trial. Each neuron is randomly assigned a preferred orientation, and receives external input at a rate given by the stimulus. <b>(D-F)</b> Example response of a population of orientation-selective neurons to a stimulus at an orientation of 90° (grey line), for networks with each type of homeostasis. Individual neural responses and their preferred orientation are given by the radii and direction of the colored areas respectively. Orientation decoded using the population vector is shown by the dashed line. <b>(G)</b> Standard deviation of errors in the orientation of the population vector in response to a stimulus (100 trials for 24 networks in each case). * <i>p</i> < 0.05 from a two-sided Kolmogorov-Smirnov test. The box encompasses the inter-quartile range and the whiskers extend to 1.5 times the inter-quartile range in all boxplots.</p

Comparison of the effects of diffusive and non-diffusive homeostasis on neural transfer functions.

<p><b>(A)</b> For non-diffusive HIP the transfer function of each neuron is brought to center around its input in order to avoid response saturation or runaway excitation. Arrows show the action of HIP on neurons (circles) with a given input (adapted from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004389#pcbi.1004389.ref017" target="_blank">17</a>]). <b>(B)</b> Diffusive homeostasis decorrelates the input and threshold of individual neurons, resulting in a population of neurons residing along the entire transfer function. The non-linear shape of the transfer function causes broad and heavy-tailed rate distributions observed with diffusive homeostasis. <b>(C-D)</b> Transfer functions following homeostasis of neurons receiving small (light grey) to large (dark grey) input are shown by the dotted lines. Following an instantaneous input change, the output ranges across the entire range of inputs are shown as vertical grey lines, with the shade of grey corresponding to the neuron’s previous input. Networks with non-diffusive homeostasis (C) exhibit response saturation (dark grey line) and superlinear responses (light grey line), while diffusive homeostasis (D) causes transfer functions to shift towards the center of the input distribution, leading to approximately linear responses.</p