scholarly journals Spiking recurrent neural networks represent task-relevant neural sequences in rule-dependent computation

2021 ◽  
Author(s):  
Xiaohe Xue ◽  
Michael M. Halassa ◽  
Zhe S. Chen
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Working Memory ◽  
Recurrent Neural Network ◽  
Cortical Neurons ◽  
Gradient Methods ◽  
Cognitive Tasks ◽  
Brain Functions ◽  
Neural Representations ◽  
Biological Constraints

AbstractPrefrontal cortical neurons play in important roles in performing rule-dependent tasks and working memory-based decision making. Motivated by experimental data, we develop an excitatory-inhibitory spiking recurrent neural network (SRNN) to perform a rule-dependent two-alternative forced choice (2AFC) task. We imposed several important biological constraints onto the SRNN, and adapted the spike frequency adaptation (SFA) and SuperSpike gradient methods to update the network parameters. These proposed strategies enabled us to train the SRNN efficiently and overcome the vanishing gradient problem during error back propagation through time. The trained SRNN produced rule-specific tuning in single-unit representations, showing rule-dependent population dynamics that strongly resemble experimentally observed data in rodent and monkey. Under varying test conditions, we further manipulated the parameters or configuration in computer simulation setups and investigated the impacts of rule-coding error, delay duration, weight connectivity and sparsity, and excitation/inhibition (E/I) balance on both task performance and neural representations. Overall, our modeling study provides a computational framework to understand neuronal representations at a fine timescale during working memory and cognitive control.Author SummaryWorking memory and decision making are fundamental cognitive functions of the brain, but the circuit mechanisms of these brain functions remain incompletely understood. Neuroscientists have trained animals (rodents or monkeys) to perform various cognitive tasks while simultaneously recording the neural activity from specific neural circuits. To complement the experimental investigations, computational modeling may provide an alternative way to examine the neural representations of neuronal assemblies during task behaviors. Here we develop and train a spiking recurrent neural network (SRNN) consisting of balanced excitatory and inhibitory neurons to perform the rule-dependent working memory tasks Our computer simulations produce qualitatively similar results as the experimental findings. Moreover, the imposed biological constraints on the trained network provide additional channel to investigate cell type-specific population responses, cortical connectivity and robustness. Our work provides a computational platform to investigate neural representations and dynamics of cortical circuits a fine timescale during complex cognitive tasks.

scholarly journals A Spiking Recurrent Neural Network with Phase Change Memory Synapses for Decision Making

2020 ◽  
Author(s):  
G. Pedretti ◽  
V. Milo ◽  
S. Hashemkhani ◽  
P. Mannocci ◽  
O. Melnic ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Phase Change ◽  
Recurrent Neural Network ◽  
Phase Change Memory ◽  
Change Memory

scholarly journals A Novel Recurrent Neural Network to Classify EEG Signals for Customers' Decision-Making Behavior Prediction in Brand Extension Scenario

2021 ◽  
Vol 15 ◽  
Author(s):  
Qingguo Ma ◽  
Manlin Wang ◽  
Linfeng Hu ◽  
Linanzi Zhang ◽  
Zhongling Hua
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Recurrent Neural Network ◽  
Brand Extension ◽  
Component Analysis ◽  
Independent Component ◽  
Support Vector ◽  
Good Prediction ◽  
Eeg Signals ◽  
Decision Making Behavior

It was meaningful to predict the customers' decision-making behavior in the field of market. However, due to individual differences and complex, non-linear natures of the electroencephalogram (EEG) signals, it was hard to classify the EEG signals and to predict customers' decisions by using traditional classification methods. To solve the aforementioned problems, a recurrent t-distributed stochastic neighbor embedding (t-SNE) neural network was proposed in current study to classify the EEG signals in the designed brand extension paradigm and to predict the participants' decisions (whether to accept the brand extension or not). The recurrent t-SNE neural network contained two steps. In the first step, t-SNE algorithm was performed to extract features from EEG signals. Second, a recurrent neural network with long short-term memory (LSTM) layer, fully connected layer, and SoftMax layer was established to train the features, classify the EEG signals, as well as predict the cognitive performance. The proposed network could give a good prediction with accuracy around 87%. Its superior in prediction accuracy as compared to a recurrent principal component analysis (PCA) network, a recurrent independent component correlation algorithm [independent component analysis (ICA)] network, a t-SNE support vector machine (SVM) network, a t-SNE back propagation (BP) neural network, a deep LSTM neural network, and a convolutional neural network were also demonstrated. Moreover, the performance of the proposed network with different activated channels were also investigated and compared. The results showed that the proposed network could make a relatively good prediction with only 16 channels. The proposed network would become a potentially useful tool to help a company in making marketing decisions and to help uncover the neural mechanisms behind individuals' decision-making behavior with low cost and high efficiency.

scholarly journals PsychRNN: An Accessible and Flexible Python Package for Training Recurrent Neural Network Models on Cognitive Tasks

eNeuro ◽  
2020 ◽  
pp. ENEURO.0427-20.2020
Author(s):  
Daniel B. Ehrlich ◽  
Jasmine T. Stone ◽  
David Brandfonbrener ◽  
Alexander Atanasov ◽  
John D. Murray
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Cognitive Tasks ◽  
Neural Network Models ◽  
Python Package

scholarly journals Learning the synaptic and intrinsic membrane dynamics underlying working memory in spiking neural network models

2020 ◽  
Author(s):  
Yinghao Li ◽  
Robert Kim ◽  
Terrence J. Sejnowski
Keyword(s):  
Neural Network ◽  
Working Memory ◽  
Complex Dynamics ◽  
Network Models ◽  
Membrane Dynamics ◽  
Membrane Properties ◽  
Cognitive Tasks ◽  
Neural Network Models ◽  
Wide Range ◽  
Synaptic Dynamics

SummaryRecurrent neural network (RNN) model trained to perform cognitive tasks is a useful computational tool for understanding how cortical circuits execute complex computations. However, these models are often composed of units that interact with one another using continuous signals and overlook parameters intrinsic to spiking neurons. Here, we developed a method to directly train not only synaptic-related variables but also membrane-related parameters of a spiking RNN model. Training our model on a wide range of cognitive tasks resulted in diverse yet task-specific synaptic and membrane parameters. We also show that fast membrane time constants and slow synaptic decay dynamics naturally emerge from our model when it is trained on tasks associated with working memory (WM). Further dissecting the optimized parameters revealed that fast membrane properties and slow synaptic dynamics are important for encoding stimuli and WM maintenance, respectively. This approach offers a unique window into how connectivity patterns and intrinsic neuronal properties contribute to complex dynamics in neural populations.

scholarly journals Chemogenetic dissection of the primate prefronto-subcortical pathways for working memory and decision-making

2021 ◽  
Author(s):  
Kei Oyama ◽  
Yukiko Hori ◽  
Yuji Nagai ◽  
Naohisa Miyakawa ◽  
Koki Mimura ◽  
...  
Keyword(s):  
Decision Making ◽  
Working Memory ◽  
Neural Circuits ◽  
Spatial Working Memory ◽  
Memory Task ◽  
Projection Mapping ◽  
Impaired Performance ◽  
Brain Functions ◽  
Neuronal Projection

The primate prefrontal cortex (PFC) is situated at the core of higher brain functions by linking and cooperating with the caudate nucleus (CD) and mediodorsal thalamus (MD) via neural circuits. However, the distinctive roles of these prefronto-subcortical pathways remain elusive. Combining in vivo neuronal projection mapping with chemogenetic synaptic silencing, we reversibly dissected key pathways from PFC to the CD and MD individually in single monkeys. We found that silencing the bilateral PFC-MD projections, but not the PFC-CD projections, impaired performance in a spatial working memory task. Conversely, silencing the unilateral PFC-CD projection, but not the PFC-MD projection, altered preference in a free-choice task. These results revealed dissociable roles of the prefronto-subcortical pathways in working memory and decision-making, representing the technical advantage of imaging-guided pathway-selective chemogenetic manipulation for dissecting neural circuits underlying cognitive functions in primates.

scholarly journals Chemogenetic dissection of the primate prefronto-subcortical pathways for working memory and decision-making

2021 ◽  
Vol 7 (26) ◽  
pp. eabg4246
Author(s):  
Kei Oyama ◽  
Yukiko Hori ◽  
Yuji Nagai ◽  
Naohisa Miyakawa ◽  
Koki Mimura ◽  
...  
Keyword(s):  
Decision Making ◽  
Working Memory ◽  
Neural Circuits ◽  
Spatial Working Memory ◽  
Memory Task ◽  
Mediodorsal Thalamus ◽  
Projection Mapping ◽  
Impaired Performance ◽  
Brain Functions

The primate prefrontal cortex (PFC) is situated at the core of higher brain functions via neural circuits such as those linking the caudate nucleus and mediodorsal thalamus. However, the distinctive roles of these prefronto-subcortical pathways remain elusive. Combining in vivo neuronal projection mapping with chemogenetic synaptic silencing, we reversibly dissected key pathways from dorsolateral part of the PFC (dlPFC) to the dorsal caudate (dCD) and lateral mediodorsal thalamus (MDl) individually in single monkeys. We found that silencing the bilateral dlPFC-MDl projections, but not the dlPFC-dCD projections, impaired performance in a spatial working memory task. Conversely, silencing the unilateral dlPFC-dCD projection, but not the unilateral dlPFC-MDl projection, altered preference in a decision-making task. These results revealed dissociable roles of the prefronto-subcortical pathways in working memory and decision-making, representing the technical advantage of imaging-guided pathway-selective chemogenetic manipulation for dissecting neural circuits underlying cognitive functions in primates.

Residue buildup predictive modeling for stencil cleaning profile decision-making using recurrent neural network

2021 ◽  
Vol 68 ◽  
pp. 102041
Author(s):  
Shrouq Alelaumi ◽  
Nourma Khader ◽  
Jingxi He ◽  
Sarah Lam ◽  
Sang Won Yoon
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Predictive Modeling ◽  
Recurrent Neural Network

scholarly journals Adjustable and Adaptive Control for an Unstable Mobile Robot Using Imitation Learning with Trajectory Optimization

Robotics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 29
Author(s):  
Christian Dengler ◽  
Boris Lohmann
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Trajectory Optimization ◽  
Inverted Pendulum ◽  
Gradient Methods ◽  
Real System ◽  
Fine Tuning ◽  
Imitation Learning ◽  
Parametric Function ◽  
The Real

In this contribution, we develop a feedback controller in the form of a parametric function for a mobile inverted pendulum. The control both stabilizes the system and drives it to target positions with target orientations. A design of the controller based only on a cost function is difficult for this task, which is why we choose to train the controller using imitation learning on optimized trajectories. In contrast to popular approaches like policy gradient methods, this approach allows us to shape the behavior of the system by including equality constraints. When transferring the parametric controller from simulation to the real mobile inverted pendulum, the control performance is degraded due to the reality gap. A robust control design can reduce the degradation. However, for the framework of imitation learning on optimized trajectories, methods that explicitly consider robustness do not yet exist to the knowledge of the authors. We tackle this research gap by presenting a method to design a robust controller in the form of a recurrent neural network, to improve the transferability of the trained controller to the real system. As a last step, we make the behavior of the parametric controller adjustable to allow for the fine tuning of the behavior of the real system. We design the controller for our system and show in the application that the recurrent neural network has increased performance compared to a static neural network without robustness considerations.

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