AB003.Pulvinar mediates the transmission of gamma and alpha oscillatory bandsbetween areas 17 and 21a

Background:Cortical activity across the visual hierarchy has different oscillatory ranges.While 25-90 Hz gamma band influences the feedforward processing,6-13 Hz alpha band travels in the feedback direction.Furthermore,gamma band acts in supragranular layers,whereas alpha range is localized in infragranular cortical layers.Is the pulvinar,the largest visual thalamic nucleus,mediating this oscillatory cortical coupling?We investigated this question by inactivating pharmacologically the pulvinar in cats and analyzing its impact on the oscillatory flow of neural activity in the visual cortex.Methods:Extracellular responses to full-field 100%contrast gratings were recorded in cortical areas 17 and 21a,from anesthetized cats using linear silicon probes before,during and after the pulvinar inactivation(injection of GABA solution).Visual stimuli were presented in one selected direction.Local field potentials(LFPs)were obtained from low-pass filtering of raw recordings.Wavelet and Granger causality analyses were performed on LFPs to determine the oscillatory coupling between cortical layers.Results:We found that cortical oscillatory activity was enhanced during LPl inactivation.These increases were observed for alpha and gamma bands in areas 17 and 21a.In area 17,alpha and gamma bands significantly increased in layers IV,V,and IV.In area 21a,this increase was observed in all layers except layer I,with a substantial increase of gamma activity in layer IV.Granger causality analysis showed that the pulvinar inactivation caused enhanced of feedforward gamma band signals from area 17(layer III)to area 21a(layer IV).For the feedback coupling,the alpha band rose from area 21a(layer V)to area 17(layers III,V,and VI).Conclusions:Our findings suggest that the pulvinar mediates the cortical oscillatory transmission between areas 17 and 21a.In particular,during the visual stimulation,the pulvinar mediates,to some extent,the bottom-up regulation from layer III of area 17 to layer IV in area 21a.Furthermore,the LPl regulates the feedback directionality of the alpha band from layer V in area 21a to layers II,V,and VI in area 17.These results contribute to our understanding of the mechanism underlying the oscillatory coupling of the feedforward and feedback processing throughout the visual cortical hierarchy.

Oscillation neuron based on a low-variability threshold switching device for high-performance neuromorphic computing

Low-power and low-variability artificial neuronal devices are highly desired for high-performance neuromorphic computing.In this paper,an oscillation neuron based on a low-variability Ag nanodots(NDs)threshold switching(TS)device with low operation voltage,large on/off ratio and high uniformity is presented.Measurement results indicate that this neuron demonstrates self-oscillation behavior under applied voltages as low as 1 V.The oscillation frequency increases with the applied voltage pulse amplitude and decreases with the load resistance.It can then be used to evaluate the resistive random-access memory(RRAM)synaptic weights accurately when the oscillation neuron is connected to the output of the RRAM crossbar array for neuromorphic computing.Meanwhile,simulation results show that a large RRAM crossbar array(>128×128)can be supported by our oscillation neuron owing to the high on/off ratio(>10^(8))of Ag NDs TS device.Moreover,the high uniformity of the Ag NDs TS device helps improve the distribution of the output frequency and suppress the degradation of neural network recognition accuracy(<1%).Therefore,the developed oscillation neuron based on the Ag NDs TS device shows great potential for future neuromorphic computing applications.

Noise-Induced Transition in a Voltage-Controlled Oscillator Neuron Model

In the presence of Gaussian white noise,we study the properties of voltage-controlled oscillator neuronmodel and discuss the effects of the additive and multiplicative noise.It is found that the additive noise can accelerate andcounterwork the firing of neuron,which depends on the value of central frequency of neuron itself,while multiplicativenoise can induce the continuous change or mutation of membrane potential.

Reduced information transmission in the internal segment of the globus pallidus of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-induced rhesus monkey models of Parkinson's disease

Rhesus monkey models of Parkinson＇s disease were induced by injection of N-methyl-4-phenyl- 1,2,3,6-tetrahydropyridine. Neural firings were recorded using microelectrodes placed in the interna segment of the globus pallidus. The wavelets and power spectra show gradual power reduction during the disease process along with increased firing rates in the Parkinson＇s disease state. Singular values of coefficients decreased considerably during tremor-related activity as well as in the Parkinson＇s disease state compared with normal signals, revealing that higher-frequency components weaken when Parkinson＇s disease occurs. We speculate that the death of neurons could be reflected by irregular frequency spike trains, and that wavelet packet decomposition can effectively detect the degradation of neurons and the loss of information transmission in the neural circuitry.

Harnessing GABAergic Transmission for Slow Oscillations

Oscillatory neuronal activity is the fundamental signature of neural networks.In the EEG literature,the oscillations are described as belonging to different frequency bands that are commonly used in clinical monitoring.

Prefrontal cortex and sensory cortices during working memory: quantity and quality

The activity in sensory cortices and the prefrontal cortex （PFC） throughout the delay interval of working memory （WM） tasks reflect two aspects of WM-quality and quantity, respectively. The delay activity in sensory cortices is fine-tuned to sensory information and forms the neural basis of the precision of WM storage, while the delay activity in the PFC appears to represent behavioral goals and filters out irrelevant distractions, forming the neural basis of the quantity of task-relevant information in WM. The PFC and sensory cortices interact through different frequency bands of neuronal oscillation （theta, alpha, and gamma） to fulfill goal-directed behaviors.