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Inhibitory properties underlying non-monotonic input-output relationship in low-frequency spherical bushy neurons of the gerbil.

Kuenzel T, Nerlich J, Wagner H, Rübsamen R, Milenkovic I - Front Neural Circuits (2015)

Bottom Line: Moreover, tonic inhibition elevated the action potentials (AP) threshold and improved the temporal precision of output functions in a SBC model with phase-dependent input conductance.We conclude that activity-dependent, summating inhibition contributes to high temporal precision of SBC spiking by filtering out weak and poorly timed EPSP.Moreover, inhibitory parameters determined in slice recordings provide a good estimate of inhibitory mechanisms apparently active in vivo.

View Article: PubMed Central - PubMed

Affiliation: Department of Zoology/Animal Physiology, Institute of Biology II, RWTH Aachen University Aachen, Germany.

ABSTRACT
Spherical bushy cells (SBCs) of the anteroventral cochlear nucleus (AVCN) receive input from large excitatory auditory nerve (AN) terminals, the endbulbs of Held, and mixed glycinergic/GABAergic inhibitory inputs. The latter have sufficient potency to block action potential firing in vivo and in slice recordings. However, it is not clear how well the data from slice recordings match the inhibition in the intact brain and how it contributes to complex phenomena such as non-monotonic rate-level functions (RLF). Therefore, we determined the input-output relationship of a model SBC with simulated endbulb inputs and a dynamic inhibitory conductance constrained by recordings in brain slice preparations of hearing gerbils. Event arrival times from in vivo single-unit recordings in gerbils, where 70% of SBC showed non-monotonic RLF, were used as input for the model. Model output RLFs systematically changed from monotonic to non-monotonic shape with increasing strength of tonic inhibition. A limited range of inhibitory synaptic properties consistent with the slice data generated a good match between the model and recorded RLF. Moreover, tonic inhibition elevated the action potentials (AP) threshold and improved the temporal precision of output functions in a SBC model with phase-dependent input conductance. We conclude that activity-dependent, summating inhibition contributes to high temporal precision of SBC spiking by filtering out weak and poorly timed EPSP. Moreover, inhibitory parameters determined in slice recordings provide a good estimate of inhibitory mechanisms apparently active in vivo.

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Activity-dependent inhibition in the SBC model increased the EPSP amplitude necessary to initiate an AP. (A,B) Measurement of threshold EPSP in the SBC model. EPSP component amplitudes of successful (blue) and failed (red) events are plotted against inter-event intervals. Optimal boundary between red and blue data points was used to estimate the threshold EPSP. (A) Simulations with low (1 nS) and (B) high (24 nS) inhibitory conductances. Note the threshold increase with stronger inhibitory conductance (black vs. light gray line). On average 525 ± 1.5 events were analyzed per condition. (C,D) EPSP threshold depends on inhibitory conductance characteristics (C, ginh 1–24 nS, decay tau fixed at 10 ms; (D), decay time constant 1–24 ms, initial inhibitory conductance of 10 nS). The blue and green lines respectively show data for the static and dynamic synapse models. (E,F) Threshold EPSPs calculated for 256 combinations of inhibitory conductance (1–24 nS) and inhibitory decay time-constant (1–24 ms). Threshold EPSP ranged from 20 mV (blue) to 28 mV (red). Simulations in (E) are performed with the static synapse model, simulations in (F) with the dynamic synapse model.
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Figure 7: Activity-dependent inhibition in the SBC model increased the EPSP amplitude necessary to initiate an AP. (A,B) Measurement of threshold EPSP in the SBC model. EPSP component amplitudes of successful (blue) and failed (red) events are plotted against inter-event intervals. Optimal boundary between red and blue data points was used to estimate the threshold EPSP. (A) Simulations with low (1 nS) and (B) high (24 nS) inhibitory conductances. Note the threshold increase with stronger inhibitory conductance (black vs. light gray line). On average 525 ± 1.5 events were analyzed per condition. (C,D) EPSP threshold depends on inhibitory conductance characteristics (C, ginh 1–24 nS, decay tau fixed at 10 ms; (D), decay time constant 1–24 ms, initial inhibitory conductance of 10 nS). The blue and green lines respectively show data for the static and dynamic synapse models. (E,F) Threshold EPSPs calculated for 256 combinations of inhibitory conductance (1–24 nS) and inhibitory decay time-constant (1–24 ms). Threshold EPSP ranged from 20 mV (blue) to 28 mV (red). Simulations in (E) are performed with the static synapse model, simulations in (F) with the dynamic synapse model.

Mentions: A simple auditory nerve (AN) model was created to generate synthetic input spike trains for the SBC model. The AN model was implemented as a gamma-tone filterbank driving leaky integrate-and-fire neurons with noise and refractoriness (1.2 ms; threshold = 0.65) using the tools provided by the spiking neural-network simulator “Brian” (Goodman and Brette, 2008) under Python. Simulated sound stimuli were 2 ms ramped pure tones. Stimulus levels and output spike rates were adjusted to achieve output rates similar to the sustained part of AVCN responses. For the analysis of EPSP threshold (Figure 7), 5 s of sound stimulation was simulated for every condition. Here, both the CF of the simulated input fiber and the stimulus frequency were 5.5 kHz, to avoid phase-locking effects on ISI. For the analysis of phase-locking precision (Figure 8), 60 s of sound stimulation was calculated per condition. The CF of the simulated input fiber was 1.2 kHz.


Inhibitory properties underlying non-monotonic input-output relationship in low-frequency spherical bushy neurons of the gerbil.

Kuenzel T, Nerlich J, Wagner H, Rübsamen R, Milenkovic I - Front Neural Circuits (2015)

Activity-dependent inhibition in the SBC model increased the EPSP amplitude necessary to initiate an AP. (A,B) Measurement of threshold EPSP in the SBC model. EPSP component amplitudes of successful (blue) and failed (red) events are plotted against inter-event intervals. Optimal boundary between red and blue data points was used to estimate the threshold EPSP. (A) Simulations with low (1 nS) and (B) high (24 nS) inhibitory conductances. Note the threshold increase with stronger inhibitory conductance (black vs. light gray line). On average 525 ± 1.5 events were analyzed per condition. (C,D) EPSP threshold depends on inhibitory conductance characteristics (C, ginh 1–24 nS, decay tau fixed at 10 ms; (D), decay time constant 1–24 ms, initial inhibitory conductance of 10 nS). The blue and green lines respectively show data for the static and dynamic synapse models. (E,F) Threshold EPSPs calculated for 256 combinations of inhibitory conductance (1–24 nS) and inhibitory decay time-constant (1–24 ms). Threshold EPSP ranged from 20 mV (blue) to 28 mV (red). Simulations in (E) are performed with the static synapse model, simulations in (F) with the dynamic synapse model.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4379913&req=5

Figure 7: Activity-dependent inhibition in the SBC model increased the EPSP amplitude necessary to initiate an AP. (A,B) Measurement of threshold EPSP in the SBC model. EPSP component amplitudes of successful (blue) and failed (red) events are plotted against inter-event intervals. Optimal boundary between red and blue data points was used to estimate the threshold EPSP. (A) Simulations with low (1 nS) and (B) high (24 nS) inhibitory conductances. Note the threshold increase with stronger inhibitory conductance (black vs. light gray line). On average 525 ± 1.5 events were analyzed per condition. (C,D) EPSP threshold depends on inhibitory conductance characteristics (C, ginh 1–24 nS, decay tau fixed at 10 ms; (D), decay time constant 1–24 ms, initial inhibitory conductance of 10 nS). The blue and green lines respectively show data for the static and dynamic synapse models. (E,F) Threshold EPSPs calculated for 256 combinations of inhibitory conductance (1–24 nS) and inhibitory decay time-constant (1–24 ms). Threshold EPSP ranged from 20 mV (blue) to 28 mV (red). Simulations in (E) are performed with the static synapse model, simulations in (F) with the dynamic synapse model.
Mentions: A simple auditory nerve (AN) model was created to generate synthetic input spike trains for the SBC model. The AN model was implemented as a gamma-tone filterbank driving leaky integrate-and-fire neurons with noise and refractoriness (1.2 ms; threshold = 0.65) using the tools provided by the spiking neural-network simulator “Brian” (Goodman and Brette, 2008) under Python. Simulated sound stimuli were 2 ms ramped pure tones. Stimulus levels and output spike rates were adjusted to achieve output rates similar to the sustained part of AVCN responses. For the analysis of EPSP threshold (Figure 7), 5 s of sound stimulation was simulated for every condition. Here, both the CF of the simulated input fiber and the stimulus frequency were 5.5 kHz, to avoid phase-locking effects on ISI. For the analysis of phase-locking precision (Figure 8), 60 s of sound stimulation was calculated per condition. The CF of the simulated input fiber was 1.2 kHz.

Bottom Line: Moreover, tonic inhibition elevated the action potentials (AP) threshold and improved the temporal precision of output functions in a SBC model with phase-dependent input conductance.We conclude that activity-dependent, summating inhibition contributes to high temporal precision of SBC spiking by filtering out weak and poorly timed EPSP.Moreover, inhibitory parameters determined in slice recordings provide a good estimate of inhibitory mechanisms apparently active in vivo.

View Article: PubMed Central - PubMed

Affiliation: Department of Zoology/Animal Physiology, Institute of Biology II, RWTH Aachen University Aachen, Germany.

ABSTRACT
Spherical bushy cells (SBCs) of the anteroventral cochlear nucleus (AVCN) receive input from large excitatory auditory nerve (AN) terminals, the endbulbs of Held, and mixed glycinergic/GABAergic inhibitory inputs. The latter have sufficient potency to block action potential firing in vivo and in slice recordings. However, it is not clear how well the data from slice recordings match the inhibition in the intact brain and how it contributes to complex phenomena such as non-monotonic rate-level functions (RLF). Therefore, we determined the input-output relationship of a model SBC with simulated endbulb inputs and a dynamic inhibitory conductance constrained by recordings in brain slice preparations of hearing gerbils. Event arrival times from in vivo single-unit recordings in gerbils, where 70% of SBC showed non-monotonic RLF, were used as input for the model. Model output RLFs systematically changed from monotonic to non-monotonic shape with increasing strength of tonic inhibition. A limited range of inhibitory synaptic properties consistent with the slice data generated a good match between the model and recorded RLF. Moreover, tonic inhibition elevated the action potentials (AP) threshold and improved the temporal precision of output functions in a SBC model with phase-dependent input conductance. We conclude that activity-dependent, summating inhibition contributes to high temporal precision of SBC spiking by filtering out weak and poorly timed EPSP. Moreover, inhibitory parameters determined in slice recordings provide a good estimate of inhibitory mechanisms apparently active in vivo.

Show MeSH
Related in: MedlinePlus