Single-trial classification of NIRS data from prefrontal cortex during working memory tasks

2014 ◽  
Author(s):  
Kai Keng Ang ◽  
Juanhong Yu ◽  
Cuntai Guan
Keyword(s):  
Working Memory ◽  
Prefrontal Cortex ◽  
Single Trial ◽  
Nirs Data

Single-Trial Classification of fNIRS Signals in Four Directions Motor Imagery Tasks Measured From Prefrontal Cortex

2018 ◽  
Vol 17 (3) ◽  
pp. 181-190 ◽  
Author(s):  
Hong Peng ◽  
Jinlong Chao ◽  
Sirui Wang ◽  
Jie Dang ◽  
Fengqi Jiang ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Motor Imagery ◽  
Single Trial

scholarly journals Classification of load in visual working memory using single-trial EEG data

2019 ◽  
Vol 19 (10) ◽  
pp. 247a
Author(s):  
Kirsten Adam ◽  
Edward Awh ◽  
Edward K. Vogel
Keyword(s):  
Working Memory ◽  
Visual Working Memory ◽  
Single Trial ◽  
Eeg Data

scholarly journals Trial-to-trial variability of spiking delay activity in prefrontal cortex constrains burst-coding models of working memory

2021 ◽  
Author(s):  
Daming Li ◽  
Christos Constantinidis ◽  
John D. Murray
Keyword(s):  
Working Memory ◽  
Prefrontal Cortex ◽  
Spike Train ◽  
Single Neuron ◽  
Theoretical Models ◽  
Neuronal Firing ◽  
Persistent Activity ◽  
Single Trial ◽  
Modeling Framework ◽  
Spike Train Data

AbstractA hallmark neuronal correlate of working memory (WM) is stimulus-selective spiking activity of neurons in prefrontal cortex (PFC) during mnemonic delays. These observations have motivated an influential computational modeling framework in which WM is supported by persistent activity. Recently this framework has been challenged by arguments that observed persistent activity may be an artifact of trial-averaging, which potentially masks high variability of delay activity at the single-trial level. In an alternative scenario, WM delay activity could be encoded in bursts of selective neuronal firing which occur intermittently across trials. However, this alternative proposal has not been tested on single-neuron spike-train data. Here, we developed a framework for addressing this issue by characterizing the trial-to-trial variability of neuronal spiking quantified by Fano factor (FF). By building a doubly stochastic Poisson spiking model, we first demonstrated that the burst-coding proposal implies a significant increase in FF positively correlated with firing rate, and thus loss of stability across trials during the delay. Simulation of spiking cortical circuit WM models further confirmed that FF is a sensitive measure that can well dissociate distinct WM mechanisms. We then tested these predictions on datasets of single-neuron recordings from macaque prefrontal cortex during three WM tasks. In sharp contrast to the burst-coding model predictions, we only found a small fraction of neurons showing increased WM-dependent burstiness, and stability across trials during delay was strengthened in empirical data. Therefore, reduced trial-to-trial variability during delay provides strong constraints on the contribution of single-neuron intermittent bursting to WM maintenance.Significance StatementThere are diverging classes of theoretical models explaining how information is maintained in working memory by cortical circuits. In an influential model class, neurons fire exhibit persistent elevated memorandum-selective firing, whereas a recently developed class of burst-coding models suggests that persistent activity is an artifact of trial-averaging, and spiking is sparse in each single trial, subserved by brief intermittent bursts. However, this alternative picture has not been characterized or tested on empirical spike-train data. Here we combine mathematical analysis, computational model simulation and experimental data analysis to test empirically theses two classes of models and show that the trial-to-trial variability of empirical spike trains is not consistent with burst coding. These findings provide constraints for theoretical models of working memory.

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