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Firing clamp: a novel method for single-trial estimation of excitatory and inhibitory synaptic neuronal conductances.

ABSTRACT: Understanding non-stationary neuronal activity as seen in vivo requires estimation of both excitatory and inhibitory synaptic conductances from a single trial of recording. For this purpose, we propose a new intracellular recording method, called "firing clamp." Synaptic conductances are estimated from the characteristics of artificially evoked probe spikes, namely the spike amplitude and the mean subthreshold potential, which are sensitive to both excitatory and inhibitory synaptic input signals. The probe spikes, timed at a fixed rate, are evoked in the dynamic-clamp mode by injected meander-like current steps, with the step duration depending on neuronal membrane voltage. We test the method with perforated-patch recordings from isolated cells stimulated by external application or synaptic release of transmitter, and validate the method with simulations of a biophysically-detailed neuron model. The results are compared with the conductance estimates based on conventional current-clamp recordings.

SUBMITTER: Chizhov AV

PROVIDER: S-EPMC3973923 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Firing clamp: a novel method for single-trial estimation of excitatory and inhibitory synaptic neuronal conductances.

Chizhov Anton V AV Malinina Evgenya E Druzin Michael M Graham Lyle J LJ Johansson Staffan S

Frontiers in cellular neuroscience 20140327

Understanding non-stationary neuronal activity as seen in vivo requires estimation of both excitatory and inhibitory synaptic conductances from a single trial of recording. For this purpose, we propose a new intracellular recording method, called "firing clamp." Synaptic conductances are estimated from the characteristics of artificially evoked probe spikes, namely the spike amplitude and the mean subthreshold potential, which are sensitive to both excitatory and inhibitory synaptic input signal ...[more]

PMID: 24734000

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Project description:Appropriate excitatory/inhibitory (E/I) balance is essential for normal cortical function and is altered in some psychiatric disorders, including autism spectrum disorders (ASDs). Cell-autonomous molecular mechanisms that control the balance of excitatory and inhibitory synapse function remain poorly understood; no proteins that regulate excitatory and inhibitory synapse strength in a coordinated reciprocal manner have been identified. Using super-resolution imaging, electrophysiology, and molecular manipulations, we show that cadherin-10, encoded by CDH10 within the ASD risk locus 5p14.1, maintains both excitatory and inhibitory synaptic scaffold structure in cultured cortical neurons from rats of both sexes. Cadherin-10 localizes to both excitatory and inhibitory synapses in neocortex, where it is organized into nanoscale puncta that influence the size of their associated PSDs. Knockdown of cadherin-10 reduces excitatory but increases inhibitory synapse size and strength, altering the E/I ratio in cortical neurons. Furthermore, cadherin-10 exhibits differential participation in complexes with PSD-95 and gephyrin, which may underlie its role in maintaining the E/I ratio. Our data provide a new mechanism whereby a protein encoded by a common ASD risk factor controls E/I ratios by regulating excitatory and inhibitory synapses in opposing directions.SIGNIFICANCE STATEMENT The correct balance between excitatory/inhibitory (E/I) is crucial for normal brain function and is altered in psychiatric disorders such as autism. However, the molecular mechanisms that underlie this balance remain elusive. To address this, we studied cadherin-10, an adhesion protein that is genetically linked to autism and understudied at the cellular level. Using a combination of advanced microscopy techniques and electrophysiology, we show that cadherin-10 forms nanoscale puncta at excitatory and inhibitory synapses, maintains excitatory and inhibitory synaptic structure, and is essential for maintaining the correct balance between excitation and inhibition in neuronal dendrites. These findings reveal a new mechanism by which E/I balance is controlled in neurons and may bear relevance to synaptic dysfunction in autism.

Project description:Neurons use sequences of action potentials (spikes) to convey information across neuronal networks. In neurophysiology experiments, information about external stimuli or behavioral tasks has been frequently characterized in term of neuronal firing rate. The firing rate is conventionally estimated by averaging spiking responses across multiple similar experiments (or trials). However, there exist a number of applications in neuroscience research that require firing rate to be estimated on a single trial basis. Estimating firing rate from a single trial is a challenging problem and current state-of-the-art methods do not perform well. To address this issue, we develop a new method for estimating firing rate based on a kernel smoothing technique that considers the bandwidth as a random variable with prior distribution that is adaptively updated under an empirical Bayesian framework. By carefully selecting the prior distribution together with Gaussian kernel function, an analytical expression can be achieved for the kernel bandwidth. We refer to the proposed method as Bayesian Adaptive Kernel Smoother (BAKS). We evaluate the performance of BAKS using synthetic spike train data generated by biologically plausible models: inhomogeneous Gamma (IG) and inhomogeneous inverse Gaussian (IIG). We also apply BAKS to real spike train data from non-human primate (NHP) motor and visual cortex. We benchmark the proposed method against established and previously reported methods. These include: optimized kernel smoother (OKS), variable kernel smoother (VKS), local polynomial fit (Locfit), and Bayesian adaptive regression splines (BARS). Results using both synthetic and real data demonstrate that the proposed method achieves better performance compared to competing methods. This suggests that the proposed method could be useful for understanding the encoding mechanism of neurons in cognitive-related tasks. The proposed method could also potentially improve the performance of brain-machine interface (BMI) decoder that relies on estimated firing rate as the input.

Dataset Information

Firing clamp: a novel method for single-trial estimation of excitatory and inhibitory synaptic neuronal conductances.

Publications

Firing clamp: a novel method for single-trial estimation of excitatory and inhibitory synaptic neuronal conductances.

Similar Datasets

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