Review
Computer Science, Artificial Intelligence
Anthony Triche, Anthony S. Maida, Ashok Kumar
Summary: Recent works have connected Hebbian plasticity with reinforcement learning, resulting in a class of trial-and-error learning called neo-Hebbian plasticity. Inspired by the role of dopamine in synaptic modification, neo-Hebbian RL methods selectively reinforce associations to enable learning exploitative behaviors. This review focuses on the exploration-exploitation balance under the neo-Hebbian RL framework and suggests potential improvements through stronger incorporation of intrinsic motivators.
Article
Engineering, Electrical & Electronic
Mahmudul Alam Shakib, Zhaolin Gao, Caterina Lamuta
Summary: This study investigates different types of synaptic plasticity properties of geopolymer memristors, demonstrating short-term and long-term memory, Hebbian learning inspired spike-timing-dependent plasticity, spike-rate-dependent plasticity, history-dependent plasticity, paired-pulse facilitation, paired-pulse depression, and post-tetanic potentiation. These properties are attributed to the movement of ions induced by electro-osmosis in the capillaries and pores of the geopolymer memristors. They show great potential for the use of geopolymers in neuromorphic computing applications.
ACS APPLIED ELECTRONIC MATERIALS
(2023)
Article
Materials Science, Ceramics
Anhe Bamao, Yaxin Xia, Guokun Ma, Xiaoxu Yuan, Zisheng Yang, Ao Chen, Chun-Chu Lin, Ting-Chang Chang, Hao Wang
Summary: A new nonvolatile memory device Pt/LiNbOx/TiN was fabricated by doping lithium metal atoms in solid electrolyte NbOx, which exhibited typical bipolar resistive storage characteristics. Continuous conductance states were obtained in both DC and pulsed modes, and the paired-pulse facilitation (PPF) and spike-timing-dependent plasticity learning (STDP) behavior were successfully simulated. Furthermore, the LiNbOx memristor-based convolutional neural network (CNN) achieved high accuracy in MNIST dataset, indicating its potential for neuromorphic computations.
CERAMICS INTERNATIONAL
(2023)
Article
Neurosciences
Lingfei Mo, Gang Wang, Erhong Long, Mingsong Zhuo
Summary: This paper proposes a supervised learning method for SNNs based on associative learning, using improved STDP rules to strengthen and weaken synaptic connections, and achieves high accuracy in supervised learning classification tasks on IRIS and MNIST datasets.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Neurosciences
Nikhil Garg, Ismael Balafrej, Terrence C. Stewart, Jean-Michel Portal, Marc Bocquet, Damien Querlioz, Dominique Drouin, Jean Rouat, Yann Beilliard, Fabien Alibart
Summary: This study proposes a voltage-dependent synaptic plasticity learning rule for the implementation of Hebb's plasticity mechanism on neuromorphic hardware. The rule reduces the number of updates and does not require additional memory storage. The system-level performance is validated through a handwriting recognition task, and it does not require manual tuning of hyperparameters.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Fernando M. Quintana, Fernando Perez-Pena, Pedro L. Galindo
Summary: This paper presents a learning method called Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) that modulates the synaptic plasticity in a Spiking Neural Network (SNN) using an external learning signal. By combining the advantages of reinforcement learning and the biological plausibility of STDP, online learning on SNN in real-world scenarios is enabled. The hardware implementation on an FPGA demonstrates comparable results to software simulations and achieves high accuracy and resource efficiency in an obstacle avoidance problem on mobile robotics.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Neurosciences
M. Ganesh Kumar, Cheston Tan, Camilo Libedinsky, Shih-Cheng Yen, Andrew Y. Y. Tan
Summary: The study shows that classic agents can learn to navigate to a single reward location and adapt to reward location displacement, but are unable to learn multiple cue-reward location tasks. By improving the agent's architecture and learning methods, this limitation can be overcome.
Article
Computer Science, Information Systems
Guilei Ma, Menghua Man, Yongqiang Zhang, Shanghe Liu
Summary: This paper presents a circuit design with a fast homeostatic inhibitory plasticity rule by imitating the mechanism of the biological nervous system. The circuit is validated using a memristive synapse, and the simulation results show that it can achieve similar weight update curves as the biological mechanism but with a significant improvement in time scale. Additionally, the circuit has wide applicability due to its adjustable homeostatic learning window, scaling factor, and homeostatic factor. This study offers new opportunities for building fast and reliable neuromorphic hardware.
Review
Neurosciences
Yanis Inglebert, Dominique Debanne
Summary: The importance of considering physiological levels of extracellular calcium concentration in studying functional plasticity is discussed in this study.
FRONTIERS IN CELLULAR NEUROSCIENCE
(2021)
Article
Multidisciplinary Sciences
Michael E. Rule, Timothy O'Leary
Summary: Recent experiments have shown that neural activity patterns in the brain associated with fixed behavioral variables and percepts can change dramatically over time, leading to questions about how plastic coding interacts with stable long-term representations. By exploring the interactions between Hebbian learning and single-cell homeostasis, it has been found that redundancy in neural coding can compensate for gradual changes and stabilize neural tuning in the short term. Recurrent feedback of partially stabilized readouts can further correct inconsistencies introduced by representational drift, providing a plausible explanation for how plastic neural codes integrate with long-term representations.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Biochemistry & Molecular Biology
He-Hai Jiang, Anni Guo, Arthur Chiu, Huanhuan Li, Cora Sau Wan Lai, Chunyue Geoffrey Lau
Summary: The information represented by principal neurons in the anterior piriform cortex is regulated by local, recurrent excitation and inhibition, with target- and layer-specific effects observed for semilunar and superficial pyramidal cells. Recurrent inhibition strength is largely mediated by parvalbumin interneurons, and olfactory experience can selectively modulate these inhibitory pathways while maintaining overall target and laminar specificity. These findings suggest the importance of target-specific inhibitory wiring in odor processing and gating the output of the piriform cortex.
Article
Neurosciences
Simon Leipold, Carina Klein, Lutz Jancke
Summary: A comprehensive assessment of the effects of musicianship and absolute pitch on intrinsic functional and structural connectivity showed robust effects of musicianship on brain networks, while the effects of absolute pitch appeared to be more subtle. Long-term musical training is associated with significant changes in large-scale brain networks.
JOURNAL OF NEUROSCIENCE
(2021)
Article
Nanoscience & Nanotechnology
Himadri Nandan Mohanty, Tohru Tsuruoka, Jyoti Ranjan Mohanty, Kazuya Terabe
Summary: Proton-gated synaptic transistors fabricated using Nafion electrolyte thin film can emulate various synaptic functions and achieve high image recognition accuracy. They successfully mimic paired-pulse facilitation and depression, Hebbian spike-timing-dependent plasticity, and Pavlovian associative learning followed by extinction activities. These results indicate that EBL patternable Nafion electrolytes have great potential for use in the fabrication and circuit-level integration of synaptic devices for neuromorphic computing applications.
ACS APPLIED MATERIALS & INTERFACES
(2023)
Article
Mathematics, Applied
Lulu Lu, Zhuoheng Gao, Zhouchao Wei, Ming Yi
Summary: The study investigates the effects of excitatory-inhibitory balance and neural network structures on working memory tasks using a neuron-astrocyte network model. The results reveal that performance metrics are higher for scale-free networks compared to other structures, and the tasks can be successfully completed when the proportion of excitatory neurons exceeds 30%. An optimal region is identified for excitatory neuron proportion and synaptic weight, where memory performance metrics are higher. The study also highlights the overlap of spatial calcium patterns in the astrocyte network for different items in multi-item working memory tasks, suggesting similarities to cognitive memory formation in the brain. Additionally, cued recall in complex image tasks reduces systematic noise and maintains task stability.
Article
Neurosciences
Xue Dong, Lijuan Liu, Xinxin Du, Yue Wang, Peng Zhang, Zhihao Li, Min Bao
Summary: This study found that altered-reality adaptation training can improve both the vision and functional connections in the visual cortex of amblyopes. After the training, visual acuities improved in amblyopes and the improvement continued to strengthen for at least one month.
HUMAN BRAIN MAPPING
(2023)
Article
Mathematical & Computational Biology
Pavel Sountsov, Paul Miller
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
(2015)
Review
Neurosciences
Paul Miller
CURRENT OPINION IN NEUROBIOLOGY
(2016)
Article
Neurosciences
Jonathan Cannon, Paul Miller
JOURNAL OF NEUROPHYSIOLOGY
(2016)
Article
Neurosciences
Brian F. Sadacca, Narendra Mukherjee, Tony Vladusich, Jennifer X. Li, Donald B. Katz, Paul Miller
JOURNAL OF NEUROSCIENCE
(2016)
Article
Neurosciences
Ian K. Christie, Paul Miller, Stephen D. Van Hooser
JOURNAL OF NEUROPHYSIOLOGY
(2017)
Article
Mathematical & Computational Biology
Jonathan Cannon, Paul Miller
JOURNAL OF MATHEMATICAL NEUROSCIENCE
(2017)
Article
Computer Science, Cybernetics
Paul Miller, Jonathan Cannon
BIOLOGICAL CYBERNETICS
(2019)
Article
Mathematical & Computational Biology
Paul Miller, Donald B. Katz
JOURNAL OF COMPUTATIONAL NEUROSCIENCE
(2013)
Article
Neurosciences
Stephen D. Van Hooser, Gina M. Escobar, Arianna Maffei, Paul Miller
JOURNAL OF NEUROPHYSIOLOGY
(2014)
Article
Mathematical & Computational Biology
Benjamin Ballintyn, Benjamin Shlaer, Paul Miller
JOURNAL OF COMPUTATIONAL NEUROSCIENCE
(2019)
Article
Mathematical & Computational Biology
Bolun Chen, Paul Miller
JOURNAL OF MATHEMATICAL NEUROSCIENCE
(2020)
Article
Biochemical Research Methods
John Ksander, Donald B. Katz, Paul Miller
Summary: The study investigated decision models on whether to continue sampling a stimulus or switch, finding that highly hedonic stimuli can reduce time spent on following stimuli and neural activity patterns could predict choice to leave a stimulus. The models offer testable predictions and propose a neural circuit-based framework for explaining foraging choices.
PLOS COMPUTATIONAL BIOLOGY
(2021)
Article
Neurosciences
Abuzar Mahmood, Jessica Steindler, Hannah Germaine, Paul Miller, Donald B. Katz
Summary: This study demonstrates the coupled dynamics of basolateral amygdala (BLA) and gustatory cortical (GC) during taste processing in rats. BLA and GC responses are correlated in terms of response magnitude across trials and within single responses, and changes in the coherence of local field potentials between BLA and GC are epoch-specific. The simultaneous transitions in BLA and GC, despite trial-to-trial variability in transition latencies, suggest collective processing in a distributed neural network.
JOURNAL OF NEUROSCIENCE
(2023)
Article
Behavioral Sciences
Benjamin Ballintyn, John Ksander, Donald B. B. Katz, Paul Miller
Summary: Food or taste preference tests are similar to naturalistic decisions, where animals select stimuli to sample and determine how long to sample them. The data from these tests indicate the preference for each stimulus based on the relative amounts sampled and consumed. Analysis of the ongoing sampling dynamics reveals hidden aspects of the decision-making process and its underlying neural circuit mechanisms. In this study, the authors performed a dynamic analysis of two factors contributing to preferences in a two-alternative task: the duration distribution of sampling bouts for each stimulus and the likelihood of returning to the same stimulus or switching to the alternative. The results support a specific computational model of decision making, where the duration of sampling bouts follows an exponential distribution correlated with the palatability of the stimulus and its alternative, with the influence of the alternative stimulus on bout durations decaying over time.
BEHAVIORAL NEUROSCIENCE
(2023)
Article
Psychology, Experimental
Katheryn A. Q. Cousins, Hayim Dar, Arthur Wingfield, Paul Miller
MEMORY & COGNITION
(2014)