Information diversity in individual auditory cortical neurons is associated with functionally distinct coordinated neuronal ensembles

Neuronal activity in auditory cortex is often highly synchronous between neighboring neurons. Such coordinated activity is thought to be crucial for information processing. We determined the functional properties of coordinated neuronal ensembles (cNEs) within primary auditory cortical (AI) columns relative to the contributing neurons. Nearly half of AI cNEs showed robust spectro-temporal receptive fields whereas the remaining cNEs showed little or no acoustic feature selectivity. cNEs can therefore capture either specific, time-locked information of spectro-temporal stimulus features or reflect stimulus-unspecific, less-time specific processing aspects. By contrast, we show that individual neurons can represent both of those aspects through membership in multiple cNEs with either high or absent feature selectivity. These associations produce functionally heterogeneous spikes identifiable by instantaneous association with different cNEs. This demonstrates that single neuron spike trains can sequentially convey multiple aspects that contribute to cortical processing, including stimulus-specific and unspecific information.

Revealing Spectrum Features of Stochastic Neuron Spike Trains

Power spectra of spike trains reveal important properties of neuronal behavior. They exhibit several peaks, whose shape and position depend on applied stimuli and intrinsic biophysical properties, such as input current density and channel noise. The position of the spectral peaks in the frequency domain is not straightforwardly predictable from statistical averages of the interspike intervals, especially when stochastic behavior prevails. In this work, we provide a model for the neuronal power spectrum, obtained from Discrete Fourier Transform and expressed as a series of expected value of sinusoidal terms. The first term of the series allows us to estimate the frequencies of the spectral peaks to a maximum error of a few Hz, and to interpret why they are not harmonics of the first peak frequency. Thus, the simple expression of the proposed power spectral density (PSD) model makes it a powerful interpretative tool of PSD shape, and also useful for neurophysiological studies aimed at extracting information on neuronal behavior from spike train spectra.

AVDM: Angular Velocity Decoding Model Accounting for Visually Guided Flight Behaviours of the Bee

We present a new angular velocity estimation model for explaining the honeybee’s flight behaviours of tunnel centring and terrain following, capable of reproducing observations of the large independence to the spatial frequency and contrast of the gratings in visually guide flights of honeybees. The model combines both temporal and texture information to decode the angular velocity well. The angular velocity estimation of the model is little affected by the spatial frequency and contrast in synthetic grating experiments. The model is also tested behaviourally in Unity with the tunnel centring and terrain following paradigms. Together with the proposed angular velocity based control algorithms, the virtual bee navigates well in a patterned tunnel and can keep a certain distance from undulating ground with gratings in a series of controlled trials. The results coincide with both neuron spike recordings and behavioural path recordings of honeybees, demonstrating that the model can explain how visual motion is detected in the bee brain.Author summaryBoth behavioural and electro-physiological experiments indicate that honeybees can estimate the angular velocity of image motion in their retinas to control their flights, while the neural mechanism behind has not been fully understood. In this paper, we present a new model based on previous experiments and models aiming to reproduce similar behaviours as real honeybees in tunnel centring and terrain following simulations. The model shows a large spatial frequency independence which outperforms the previous model, and our model generally reproduces the wanted behaviours in simulations.

FPGA-based hardware simulation of nonlinear autoregressive Volterra model to reconstruct the single neuron spike pattern

This study explores the implementation of the nonlinear autoregressive Volterra (NARV) model using a field programmable gate arrays (FPGAs)-based hardware simulation platform and accomplishes the identification process of the Hodgkin–Huxley (HH) model. First, a physiological detailed single-compartment HH model is applied to generate experiment data sets and the electrical behavior of neurons are described by the membrane potential. Then, based on the injected input current and the output membrane potential, a second-order NARV model is constructed and implemented on FPGA-based simulation platforms. The NARV modeling method is data-driven, requiring no accurate physiological information and the FPGA-based hardware simulation can provide a real time and high-performance platform to deal with the drawbacks of software simulation. Therefore, the proposed method in this paper is capable of handling the nonlinearities and uncertainties in nonlinear neural systems and may help promote the development of clinical treatment devices.