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- research-articleFebruary 2021
Stability Conditions of Bicomplex-Valued Hopfield Neural Networks
Hopfield neural networks have been extended using hypercomplex numbers. The algebra of bicomplex numbers, also referred to as commutative quaternions, is a number system of dimension 4. Since the multiplication is commutative, many notions and theories of ...
- research-articleFebruary 2021
Predicting the Ease of Human Category Learning Using Radial Basis Function Networks
Our goal is to understand and optimize human concept learning by predicting the ease of learning of a particular exemplar or category. We propose a method for estimating ease values, quantitative measures of ease of learning, as an alternative to ...
- research-articleFebruary 2021
Enhanced Signal Detection by Adaptive Decorrelation of Interspike Intervals
Spike trains with negative interspike interval (ISI) correlations, in which long/short ISIs are more likely followed by short/long ISIs, are common in many neurons. They can be described by stochastic models with a spike-triggered adaptation variable. We ...
- research-articleFebruary 2021
Enhanced Equivalence Projective Simulation: A Framework for Modeling Formation of Stimulus Equivalence Classes
Formation of stimulus equivalence classes has been recently modeled through equivalence projective simulation (EPS), a modified version of a projective simulation (PS) learning agent. PS is endowed with an episodic memory that resembles the internal ...
- research-articleFebruary 2021
A Novel Neural Model With Lateral Interaction for Learning Tasks
We propose a novel neural model with lateral interaction for learning tasks. The model consists of two functional fields: an elementary field to extract features and a high-level field to store and recognize patterns. Each field is composed of some ...
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- research-articleFebruary 2021
Deeply Felt Affect: The Emergence of Valence in Deep Active Inference
The positive-negative axis of emotional valence has long been recognized as fundamental to adaptive behavior, but its origin and underlying function have largely eluded formal theorizing and computational modeling. Using deep active inference, a ...
- research-articleJanuary 2021
Robust Stability Analysis of Delayed Stochastic Neural Networks via Wirtinger-Based Integral Inequality
We discuss stability analysis for uncertain stochastic neural networks (SNNs) with time delay in this letter. By constructing a suitable Lyapunov-Krasovskii functional (LKF) and utilizing Wirtinger inequalities for estimating the integral inequalities, ...
- research-articleJanuary 2021
NMDA Receptor Alterations After Mild Traumatic Brain Injury Induce Deficits in Memory Acquisition and Recall
Mild traumatic brain injury (mTBI) presents a significant health concern with potential persisting deficits that can last decades. Although a growing body of literature improves our understanding of the brain network response and corresponding underlying ...
- research-articleJanuary 2021
Information-Theoretic Representation Learning for Positive-Unlabeled Classification
Recent advances in weakly supervised classification allow us to train a classifier from only positive and unlabeled (PU) data. However, existing PU classification methods typically require an accurate estimate of the class-prior probability, a critical ...
- research-articleJanuary 2021
An EM Algorithm for Capsule Regression
We investigate a latent variable model for multinomial classification inspired by recent capsule architectures for visual object recognition (Sabour, Frosst, & Hinton, 2017). Capsule architectures use vectors of hidden unit activities to encode the pose ...
- research-articleJanuary 2021
Associated Learning: Decomposing End-to-End Backpropagation Based on Autoencoders and Target Propagation
Backpropagation (BP) is the cornerstone of today's deep learning algorithms, but it is inefficient partially because of backward locking, which means updating the weights of one layer locks the weight updates in the other layers. Consequently, it is ...
- research-articleJanuary 2021
New Insights Into Learning With Correntropy-Based Regression
Stemming from information-theoretic learning, the correntropy criterion and its applications to machine learning tasks have been extensively studied and explored. Its application to regression problems leads to the robustness-enhanced regression paradigm: ...
- research-articleJanuary 2021
Efficient Actor-Critic Reinforcement Learning With Embodiment of Muscle Tone for Posture Stabilization of the Human Arm
This letter proposes a new idea to improve learning efficiency in reinforcement learning (RL) with the actor-critic method used as a muscle controller for posture stabilization of the human arm. Actor-critic RL (ACRL) is used for simulations to realize ...
- research-articleDecember 2020
Resonator Networks, 1: An Efficient Solution for Factoring High-Dimensional, Distributed Representations of Data Structures
The ability to encode and manipulate data structures with distributed neural representations could qualitatively enhance the capabilities of traditional neural networks by supporting rule-based symbolic reasoning, a central property of cognition. Here we ...
- research-articleDecember 2020
Redundancy-Aware Pruning of Convolutional Neural Networks
Pruning is an effective way to slim and speed up convolutional neural networks. Generally previous work directly pruned neural networks in the original feature space without considering the correlation of neurons. We argue that such a way of pruning still ...
- research-articleDecember 2020
Resonator Networks, 2: Factorization Performance and Capacity Compared to Optimization-Based Methods
We develop theoretical foundations of resonator networks, a new type of recurrent neural network introduced in Frady, Kent, Olshausen, and Sommer (2020), a companion article in this issue, to solve a high-dimensional vector factorization problem arising ...
- research-articleDecember 2020
Toward a Unified Framework for Cognitive Maps
In this study, we integrated neural encoding and decoding into a unified framework for spatial information processing in the brain. Specifically, the neural representations of self-location in the hippocampus (HPC) and entorhinal cortex (EC) play crucial ...
- research-articleDecember 2020
Analyzing and Accelerating the Bottlenecks of Training Deep SNNs With Backpropagation
Spiking neural networks (SNNs) with the event-driven manner of transmitting spikes consume ultra-low power on neuromorphic chips. However, training deep SNNs is still challenging compared to convolutional neural networks (CNNs). The SNN training ...
- research-articleNovember 2020
Effect of Top-Down Connections in Hierarchical Sparse Coding
Hierarchical sparse coding (HSC) is a powerful model to efficiently represent multidimensional, structured data such as images. The simplest solution to solve this computationally hard problem is to decompose it into independent layer-wise subproblems. ...
- research-articleNovember 2020
Inferring Neuronal Couplings From Spiking Data Using a Systematic Procedure With a Statistical Criterion
Recent remarkable advances in experimental techniques have provided a background for inferring neuronal couplings from point process data that include a great number of neurons. Here, we propose a systematic procedure for pre- and postprocessing generic ...