Cellular and network contributions to vestibular signal processing: impact of ion conductances, synaptic inhibition, and noise.
نویسندگان
چکیده
Head motion-related sensory signals are transformed by second-order vestibular neurons (2°VNs) into appropriate commands for retinal image stabilization during body motion. In frogs, these 2°VNs form two distinct subpopulations that have either tonic or highly phasic intrinsic properties, essentially compatible with low-pass and bandpass filter characteristics, respectively. In the present study, physiological data on cellular properties of 2°VNs of the grass frog (Rana temporaria) have been used to construct conductance-based spiking cellular models that were fine-tuned by fitting to recorded spike-frequency data. The results of this approach suggest that low-threshold, voltage-dependent potassium channels in phasic and spike-dependent potassium channels in tonic 2°VNs are important contributors to the differential, yet complementary response characteristics of the two vestibular subtypes. Extension of the cellular model with conductance-based synapses allowed simulation of afferent excitation and evaluation of the emerging properties of local feedforward inhibitory circuits. This approach revealed the relative contributions of intrinsic and synaptic factors on afferent signal processing in phasic 2°VNs. Additional extension of the single-cell model to a population model allowed testing under more natural conditions including asynchronous afferent labyrinthine input and synaptic noise. This latter approach indicated that the feedforward inhibition from the local inhibitory network acts as a high-pass filter, which reinforces the impact of the intrinsic membrane properties of phasic 2°VNs on peak response amplitude and timing. Thus, the combination of cellular and network properties enables phasic 2°VNs to work as a noise-resistant detector, suitable for central processing of short-duration vestibular signals.
منابع مشابه
Reverse Engineering the Vestibular System: Intrinsic and Synaptic Contribution to Signal Processing in Frog Central Vestibular Neurons
Head motion-related sensory signals are transformed by second-order vestibular neurons (2°VN) into appropriate extraocular motor commands for retinal image stabilization during body motion. In frog, these 2°VN form two distinct subpopulations that have either linear (tonic 2°VN) or highly non-linear intrinsic properties (phasic 2°VN), compatible with lowpass and band-pass filter characteristics...
متن کاملEstimation of synaptic conductances and their variances from intracellular recordings of neocortical neurons in vivo
During intense network activity, neocortical neurons are in a “high-conductance” state. To estimate the respective contributions of excitatory and inhibitory conductances in generating such states, we combined computational models with intracellular recordings obtained in cat parietal cortex in vivo. Fitting a 6uctuating-conductance model to the recordings revealed that inhibitory conductances ...
متن کاملModeling of Substrate Noise Impact on a Single-Ended Cascode LNA in a Lightly Doped Substrate (RESEARCH NOTE)
Substrate noise generated by digital circuits on mixed-signal ICs can disturb the sensitiveanalog/RF circuits, such as Low Noise Amplifier (LNA), sharing the same substrate. This paperinvestigates the adverse impact of the substrate noise on a high frequency cascode LNA laid out on alightly doped substrate. By studying the major noise coupling mechanisms, a new and efficientmodeling method is p...
متن کاملActive subthreshold dendritic conductances shape the local field potential
KEY POINTS The local field potential (LFP), the low-frequency part of extracellular potentials recorded in neural tissue, is often used for probing neural circuit activity. Interpreting the LFP signal is difficult, however. While the cortical LFP is thought mainly to reflect synaptic inputs onto pyramidal neurons, little is known about the role of the various subthreshold active conductances in...
متن کاملA Convolutional Neural Network based on Adaptive Pooling for Classification of Noisy Images
Convolutional neural network is one of the effective methods for classifying images that performs learning using convolutional, pooling and fully-connected layers. All kinds of noise disrupt the operation of this network. Noise images reduce classification accuracy and increase convolutional neural network training time. Noise is an unwanted signal that destroys the original signal. Noise chang...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- The Journal of neuroscience : the official journal of the Society for Neuroscience
دوره 31 23 شماره
صفحات -
تاریخ انتشار 2011