Irregular Firing and High-Conductance States in Spinal Motoneurons during Scratching and Swimming

We introduce a two-dimensional integrate-and-fire model that combines an exponential spike mechanism with an adaptation equation, based on recent theoretical findings. We describe a systematic method to estimate its parameters with simple electrophysiological protocols (current-clamp injection of pulses and ramps) and apply it to a detailed conductance-based model of a regular spiking neuron. Our simple model predicts correctly the timing of 96% of the spikes (±2 ms) of the detailed model in response to injection of noisy synaptic conductances. The model is especially reliable in high-conductance states, typical of cortical activity in vivo, in which intrinsic conductances were found to have a reduced role in shaping spike trains. These results are promising because this simple model has enough expressive power to reproduce qualitatively several electrophysiological classes described in vitro.

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Cross-Correlations in High-Conductance States of a Model Cortical Network

Neural Computation ◽

10.1162/neco.2009.06-08-806 ◽

2010 ◽

Vol 22 (2) ◽

pp. 427-447 ◽

Cited By ~ 39

Author(s):

John Hertz

Keyword(s):

Correlation Coefficients ◽

Neuronal Firing ◽

Synaptic Conductance ◽

Synaptic Currents ◽

High Conductance State ◽

Systematic Behavior ◽

Cross Correlations ◽

Conductance States ◽

Simple Network ◽

High Conductance

Neuronal firing correlations are studied using simulations of a simple network model for a cortical column in a high-conductance state with dynamically balanced excitation and inhibition. Although correlations between individual pairs of neurons exhibit considerable heterogeneity, population averages show systematic behavior. When the network is in a stationary state, the average correlations are generically small: correlation coefficients are of order 1/N, where N is the number of neurons in the network. However, when the input to the network varies strongly in time, much larger values are found. In this situation, the network is out of balance, and the synaptic conductance is low, at times when the strongest firing occurs. However, examination of the correlation functions of synaptic currents reveals that after these bursts, balance is restored within a few milliseconds by a rapid increase in inhibitory synaptic conductance. These findings suggest an extension of the notion of the balanced state to include balanced fluctuations of synaptic currents, with a characteristic timescale of a few milliseconds.

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Phase response analysis during in vivo-like high conductance states; dendritic SK determines the mean and variance of responses to dendritic excitation

BMC Neuroscience ◽

10.1186/1471-2202-11-s1-o12 ◽

2010 ◽

Vol 11 (S1) ◽

Author(s):

Nathan W Schultheiss ◽

Jeremy R Edgerton ◽

Dieter Jaeger

Keyword(s):

Phase Response ◽

Response Analysis ◽

Mean And Variance ◽

The Mean ◽

Conductance States ◽

High Conductance

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High-conductance states and A-type K+ channels are potential regulators of the conductance-current balance triggered by HCN channels

Journal of Neurophysiology ◽

10.1152/jn.00601.2013 ◽

2015 ◽

Vol 113 (1) ◽

pp. 23-43 ◽

Cited By ~ 21

Author(s):

Poonam Mishra ◽

Rishikesh Narayanan

Keyword(s):

Dominant Role ◽

Input Resistance ◽

Resting Membrane Potential ◽

Voltage Response ◽

Hcn Channels ◽

Type K ◽

Conductance States ◽

The Impact ◽

High Conductance

An increase in the hyperpolarization-activated cyclic nucleotide-gated (HCN) channel conductance reduces input resistance, whereas the consequent increase in the inward h current depolarizes the membrane. This results in a delicate and unique conductance-current balance triggered by the expression of HCN channels. In this study, we employ experimentally constrained, morphologically realistic, conductance-based models of hippocampal neurons to explore certain aspects of this conductance-current balance. First, we found that the inclusion of an experimentally determined gradient in A-type K+ conductance, but not in M-type K+ conductance, tilts the HCN conductance-current balance heavily in favor of conductance, thereby exerting an overall restorative influence on neural excitability. Next, motivated by the well-established modulation of neuronal excitability by synaptically driven high-conductance states observed under in vivo conditions, we inserted thousands of excitatory and inhibitory synapses with different somatodendritic distributions. We measured the efficacy of HCN channels, independently and in conjunction with other channels, in altering resting membrane potential (RMP) and input resistance ( Rin) when the neuron received randomized or rhythmic synaptic bombardments through variable numbers of synaptic inputs. We found that the impact of HCN channels on average RMP, Rin, firing frequency, and peak-to-peak voltage response was severely weakened under high-conductance states, with the impinging synaptic drive playing a dominant role in regulating these measurements. Our results suggest that the debate on the role of HCN channels in altering excitability should encompass physiological and pathophysiological neuronal states under in vivo conditions and the spatiotemporal interactions of HCN channels with other channels.

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Thalamocortical synapses in the cat visual system in vivo are weak and unreliable

eLife ◽

10.7554/elife.41925 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 1

Author(s):

Madineh Sedigh-Sarvestani ◽

Larry A Palmer ◽

Diego Contreras

Keyword(s):

Visual System ◽

Dynamic Properties ◽

Short Term Plasticity ◽

V1 Neurons ◽

Membrane Fluctuations ◽

Conductance States ◽

Constrained Models ◽

High Conductance

The thalamocortical synapse of the visual system has been central to our understanding of sensory computations in the cortex. Although we have a fair understanding of the functional properties of the pre and post-synaptic populations, little is known about their synaptic properties, particularly in vivo. We used simultaneous recordings in LGN and V1 in cat in vivo to characterize the dynamic properties of thalamocortical synaptic transmission in monosynaptically connected LGN-V1 neurons. We found that thalamocortical synapses in vivo are unreliable, highly variable and exhibit short-term plasticity. Using biologically constrained models, we found that variable and unreliable synapses serve to increase cortical firing by means of increasing membrane fluctuations, similar to high conductance states. Thus, synaptic variability and unreliability, rather than acting as system noise, do serve a computational function. Our characterization of LGN-V1 synaptic properties constrains existing mathematical models, and mechanistic hypotheses, of a fundamental circuit in computational neuroscience.

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Computational models of O-LM cells are recruited by low or high theta frequency inputs depending on h-channel distributions

eLife ◽

10.7554/elife.22962 ◽

2017 ◽

Vol 6 ◽

Cited By ~ 17

Author(s):

Vladislav Sekulić ◽

Frances K Skinner

Keyword(s):

Computational Models ◽

Inhibitory Neurons ◽

Delayed Rectifier ◽

Wide Range ◽

Theta Frequency ◽

Link Model ◽

Conductance States ◽

Model Database ◽

H Channels ◽

High Conductance

Although biophysical details of inhibitory neurons are becoming known, it is challenging to map these details onto function. Oriens-lacunosum/moleculare (O-LM) cells are inhibitory cells in the hippocampus that gate information flow, firing while phase-locked to theta rhythms. We build on our existing computational model database of O-LM cells to link model with function. We place our models in high-conductance states and modulate inhibitory inputs at a wide range of frequencies. We find preferred spiking recruitment of models at high (4–9 Hz) or low (2–5 Hz) theta depending on, respectively, the presence or absence of h-channels on their dendrites. This also depends on slow delayed-rectifier potassium channels, and preferred theta ranges shift when h-channels are potentiated by cyclic AMP. Our results suggest that O-LM cells can be differentially recruited by frequency-modulated inputs depending on specific channel types and distributions. This work exposes a strategy for understanding how biophysical characteristics contribute to function.

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