AIMC Topic: Electrophysiological Phenomena

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Implications of asymmetric neural activity patterns in the basal ganglia outflow in the integrative neural network model for cervical dystonia.

Progress in brain research
Cervical dystonia (CD) is characterized by abnormal twisting and turning of the head with associated head oscillations. It is the most common form of dystonia, which is a third most common movement disorder. Despite frequent occurrence there is pauci...

Hidden Bursting Firings and Bifurcation Mechanisms in Memristive Neuron Model With Threshold Electromagnetic Induction.

IEEE transactions on neural networks and learning systems
Memristors can be employed to mimic biological neural synapses or to describe electromagnetic induction effects. To exhibit the threshold effect of electromagnetic induction, this paper presents a threshold flux-controlled memristor and examines its ...

PCNN Mechanism and its Parameter Settings.

IEEE transactions on neural networks and learning systems
The pulse-coupled neural network (PCNN) model is a third-generation artificial neural network without training that uses the synchronous pulse bursts of neurons to process digital images, but the lack of in-depth theoretical research limits its exten...

PatcherBot: a single-cell electrophysiology robot for adherent cells and brain slices.

Journal of neural engineering
OBJECTIVE: Intracellular patch-clamp electrophysiology, one of the most ubiquitous, high-fidelity techniques in biophysics, remains laborious and low-throughput. While previous efforts have succeeded at automating some steps of the technique, here we...

Autonomous patch-clamp robot for functional characterization of neurons in vivo: development and application to mouse visual cortex.

Journal of neurophysiology
Patch clamping is the gold standard measurement technique for cell-type characterization in vivo, but it has low throughput, is difficult to scale, and requires highly skilled operation. We developed an autonomous robot that can acquire multiple cons...

A supervised machine learning approach to characterize spinal network function.

Journal of neurophysiology
Spontaneous activity is a common feature of immature neuronal networks throughout the central nervous system and plays an important role in network development and consolidation. In postnatal rodents, spontaneous activity in the spinal cord exhibits ...

Unsupervised identification of states from voltage recordings of neural networks.

Journal of neuroscience methods
BACKGROUND: Modern techniques for multi-neuronal recording produce large amounts of data. There is no automatic procedure for the identification of states in recurrent voltage patterns.

A coarse-graining framework for spiking neuronal networks: from strongly-coupled conductance-based integrate-and-fire neurons to augmented systems of ODEs.

Journal of computational neuroscience
Homogeneously structured, fluctuation-driven networks of spiking neurons can exhibit a wide variety of dynamical behaviors, ranging from homogeneity to synchrony. We extend our partitioned-ensemble average (PEA) formalism proposed in Zhang et al. (Jo...

Network structure and input integration in competing firing rate models for decision-making.

Journal of computational neuroscience
Making a decision among numerous alternatives is a pervasive and central undertaking encountered by mammals in natural settings. While decision making for two-option tasks has been studied extensively both experimentally and theoretically, characteri...

Electrophysiological Muscle Classification Using Multiple Instance Learning and Unsupervised Time and Spectral Domain Analysis.

IEEE transactions on bio-medical engineering
OBJECTIVE: Electrophysiological muscle classification (EMC) is a crucial step in the diagnosis of neuromuscular disorders. Existing quantitative techniques are not sufficiently robust and accurate to be reliably clinically used. Here, EMC is modeled ...