Article
Computer Science, Artificial Intelligence
Zhenzhi Wu, Hehui Zhang, Yihan Lin, Guoqi Li, Meng Wang, Ye Tang
Summary: The LIAF-Net introduces a new neuron model and deep network structure that efficiently integrates analog values for spatiotemporal processing and avoids the performance loss seen in traditional LIF-SNNs.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Effrosyni Doutsi, Lionel Fillatre, Marc Antonini, Panagiotis Tsakalides
Summary: This paper introduces a novel coding/decoding mechanism named Dual-SIM quantizer (Dual-SIMQ) that simulates one of the most important properties of the human visual system to enhance the quality of visual perception. By utilizing neuroscience models, the Dual-SIMQ combines time-SIM and rate-SIM mechanisms to achieve high-quality neural coding and simple decoding, ultimately improving the reconstruction quality and performance of the visual stimulus. The proposed mechanism shows promising results in controlling reconstruction accuracy, numerical comparison with state-of-the-art methods, and enhancing perceptual reconstruction quality.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Optics
Krzysztof Tyszka, Magdalena Furman, Rafal Mirek, Mateusz Krol, Andrzej Opala, Bartlomiej Seredynski, Jan Suffczynski, Wojciech Pacuski, Michal Matuszewski, Jacek Szczytko, Barbara Pietka
Summary: This paper introduces a new approach to neuromorphic photonics using microcavities as building blocks of optical spiking neurons, and demonstrates its feasibility through experimental results. The research shows that exciton-polaritons exhibit characteristics similar to the Leaky Integrate-and-Fire (LIF) mechanism, enabling ultrafast processing on sub-ns timescales.
LASER & PHOTONICS REVIEWS
(2023)
Article
Neurosciences
Mo Shahdloo, Emin Celik, Burcu A. Urgen, Jack L. Gallant, Tolga Cukur
Summary: Object and action perception in cluttered dynamic natural scenes rely on efficient allocation of limited brain resources to prioritize attended targets. Attention directed to action categories elicits tuning shifts in semantic representations across neocortex, interacting with intrinsic selectivity of cortical voxels for target actions.
JOURNAL OF NEUROSCIENCE
(2022)
Article
Engineering, Electrical & Electronic
Elena Adomaitiene, Steponas Asmontas, Skaidra Bumeliene, Arunas Tamasevicius
Summary: The I&F electronic neuron model generates short spikes, can be stabilized by feedback, inhibits spikes with high frequency action, and can synchronize when coupled units.
AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Sung Mo Kang, Donguk Choi, Jason K. Eshraghian, Peng Zhou, Jieun Kim, Bai-Sun Kong, Xiaojian Zhu, Ahmet Samil Demirkol, Alon Ascoli, Ronald Tetzlaff, Wei D. Lu, Leon O. Chua
Summary: Two minimal compact memristive models for spiking neuronal signal generation were presented and experimentally validated using commercially available low-cost components. The models, Memristive Integrate-and-Fire (MIF) and MIF2, show potential for designing a memristive solid-state brain with estimated surface area and power consumption within achievable limits using current technology. This work differentiates itself from previous literature by using generic commercially available memristors, aiming to promote more experimental demonstrations of memristive circuits without relying on prohibitively expensive fabrication processes.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2021)
Article
Computer Science, Theory & Methods
Ioannis E. Venetis, Astero Provata
Summary: This study analyzes the performance issues of implementing the coupled Leaky Integrate-and-Fire model on a GPU, finding that the problem is mainly memory-bound. The results demonstrate that using advanced memory technology on a GPU can achieve better performance.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2022)
Article
Computer Science, Information Systems
Natasa M. Samardzic, Jovan S. Bajic, Dalibor L. Sekulic, Stanisa Dautovic
Summary: This paper proposes and simulates a circuit implementation of a leaky integrate-and-fire neuron model with a volatile memristor. The model is capable of mimicking spatial and temporal integration, firing function, and signal decay, which facilitates the design of complex memristive neural networks.
Article
Engineering, Biomedical
Yabin Deng, Bijing Liu, Zenan Huang, Xiaojie Liu, Shan He, Qiuhong Li, Donghui Guo
Summary: Dendrites play a crucial role in neuronal activities and information processing. However, the current spiking neural network models lack consideration of dendritic morphologies, limiting their ability to accurately simulate neuronal information processing. This study proposes a dendritic fractal model to quantify the effects of dendritic morphologies and successfully demonstrates the same multiple timescale dynamics as biological neurons by integrating this model into fractional leaky integrate-and-fire neuron circuits.
IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS
(2022)
Article
Chemistry, Multidisciplinary
Shi Luo, Lin Shao, Daizong Ji, Yiheng Chen, Xuejun Wang, Yungen Wu, Derong Kong, Meng Guo, Dapeng Wei, Yan Zhao, Yunqi Liu, Dacheng Wei
Summary: A supercapacitively gated artificial neuron is developed to mimic reversible integrate-and-fire (I&F) dynamics in chemical communication, achieving efficient signal transport. An electrochemical reaction occurs on a graphene nanowall (GNW) gate electrode upon neurotransmitter, simulating membrane potential accumulation and recovery for high-efficiency chemical communication down to 2 x 10(-10) M acetylcholine. By combining artificial chemical synapses with axon-hillock circuits, neural spikes can be generated. This artificial neuron establishes chemical communication with other artificial neurons and living cells, promising as a basic unit for constructing a neural network compatible with organisms for artificial intelligence and deep human-machine fusion.
Article
Computer Science, Information Systems
Zhen Xu, Ayrton A. Bernussi, Zhaoyang Fan
Summary: In a hardware-based neuromorphic computation system, using VO2 devices as artificial synapses and neurons has shown potential for mimicking biological neuron behavior, with VO2 devices demonstrating elastic relax behavior similar to that of neurons. Simulation results provide insight into the mechanisms underlying the volatile resistive transition in VO2 devices, offering a promising avenue for artificial neuron applications.
Article
Mathematics
Ghinwa El Masri, Asma Ali, Waad H. Abuwatfa, Maruf Mortula, Ghaleb A. Husseini
Summary: This research compares two methods for estimating the behavior of neurons using the leaky integrate and fire model. The findings show that Heun's method is faster and more accurate, making it more suitable for this model.
Article
Engineering, Electrical & Electronic
Jeeson Kim, Vladmir Kornijcuk, Changmin Ye, Doo Seok Jeong
Summary: This study presents a method to emulate a leaky integrate-and-fire (LIF) model in a hardware-efficient manner using a simplified spike-response model (SRM0) and the template-scaling-based exponential function approximation (TS-EFA). The implementation in FPGA of 512 neurons conforming to SRM0 showcases high precision, low latency, and efficient hardware usage.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2021)
Article
Chemistry, Multidisciplinary
Chunsheng Chen, Yongli He, Huiwu Mao, Li Zhu, Xiangjing Wang, Ying Zhu, Yixin Zhu, Yi Shi, Changjin Wan, Qing Wan
Summary: This study presents a highly bio-realistic photoelectric spiking neuron for visual depth perception. By utilizing memristive spiking encoders and a network of neuromorphic transistors, it imitates the distance-dependent response and eye fatigue of biological visual systems.
ADVANCED MATERIALS
(2022)
Article
Computer Science, Artificial Intelligence
Tomasz Gorski, Damien Depannemaecker, Alain Destexhe
Summary: The study highlights the limitations of the adaptive exponential integrate-and-fire model and proposes the conductance-based adaptive exponential integrate-and-fire model as a solution to avoid unrealistic behaviors, demonstrating its dynamic characteristics and the variety of firing patterns it can produce.
NEURAL COMPUTATION
(2021)
Article
Neurosciences
Edmund T. Rolls, Josef P. Rauschecker, Gustavo Deco, Chu-Chung Huang, Jianfeng Feng
Summary: Study investigated effective connectivity between auditory cortical regions and other cortical regions using data from 171 Human Connectome Project participants. A hierarchy of auditory cortical processing was identified, from core regions to belt regions and onward to higher-level regions. The study also found connections between auditory regions, visual regions, and language-related semantic regions, suggesting the involvement of multimodal processing in object identification and language comprehension.
Article
Neurosciences
L. Bonetti, E. Brattico, S. E. P. Bruzzone, G. Donati, G. Deco, D. Pantazis, P. Vuust, M. L. Kringelbach
Summary: This study used magnetoencephalography to investigate the neural mechanisms of memory for sequences and found that recognition of previously memorized sequences is associated with slower brain processing, while recognition of novel sequences requires faster brain processing.
Article
Neurosciences
Edmund T. Rolls, Gustavo Deco, Chu-Chung Huang, Jianfeng Feng
Summary: The amygdala and orbitofrontal cortex are involved in emotion. Through experiments on 171 humans from the Human Connectome Project, it was found that the human amygdala has fewer effective connections with cortical regions compared to the orbitofrontal cortex. It is proposed that the amygdala is primarily involved in autonomic and conditioned responses, rather than declarative emotion.
PROGRESS IN NEUROBIOLOGY
(2023)
Article
Biochemical Research Methods
Giulio Ruffini, Giada Damiani, Diego Lozano-Soldevilla, Nikolas Deco, Fernando E. E. Rosas, Narsis A. A. Kiani, Adrian Ponce-Alvarez, Morten L. L. Kringelbach, Robin Carhart-Harris, Gustavo Deco
Summary: In this study, the authors characterized two brain states (psychedelics-induced and placebo) using functional magnetic resonance imaging (fMRI) data and features from the Ising model formalism and algorithmic complexity. They found that psychedelics increased BOLD signal complexity and Ising temperature, in agreement with previous findings and predictions. They also discovered that the placebo condition was already in a paramagnetic phase, while ingestion of psychedelics resulted in a shift towards a more disordered state. The study highlights the recovery of the structural connectome through fitting an Ising model and the role of reduced homotopic links in psychedelics-induced disorder.
PLOS COMPUTATIONAL BIOLOGY
(2023)
Article
Neurosciences
Nelly Padilla, Anira Escrichs, Elvira del Agua, Morten Kringelbach, Antonio Donaire, Gustavo Deco, Ulrika Aden
Summary: The brain in extremely preterm (EPT) children has difficulties in adapting and developing after birth. Resting-state functional magnetic resonance imaging revealed reduced neural information propagation and variability in certain brain networks, which were associated with cognitive performance. This study suggests that interventions targeting these resting-state networks could help improve brain function in EPT children.
Article
Neurosciences
Gerard Marti-Juan, Jaume Sastre-Garriga, Eloy Martinez-Heras, Angela Vidal-Jordana, Sara Llufriu, Sergiu Groppa, Gabriel Gonzalez-Escamilla, Maria A. Rocca, Massimo Filippi, Einar A. Hogestol, Hanne F. Harbo, Michael A. Foster, Ahmed T. Toosy, Menno M. Schoonheim, Prejaas Tewarie, Giuseppe Pontillo, Maria Petracca, Alex Rovira, Gustavo Deco, Deborah Pareto
Summary: The relationship between structural connectivity (SC) and functional connectivity (FC) in people with multiple sclerosis (pwMS) and its interaction with disability and cognitive impairment are not well understood. This study used the Virtual Brain (TVB) to explore the SC-FC relationship in MS. Two different model regimes were studied: stable and oscillatory, with the latter including conduction delays in the brain. The results suggested that cognitive impairment in pwMS is associated with a higher SC-FC coupling and that there are compensatory and maladaptive mechanisms between SC and FC in MS.
Article
Neurosciences
Rajanikant Panda, Ane Lopez-Gonzalez, Matthieu Gilson, Olivia Gosseries, Aurore Thibaut, Gianluca Frasso, Benedetta Cecconi, Anira Escrichs, Gustavo Deco, Steven Laureys, Gorka Zamora-Lopez, Jitka Annen
Summary: The study of brain's dynamic activity is helping in the clinical assessment of patients with consciousness disorders. The reduced neural propagation and responsiveness to events in patients with disorders of consciousness is related to severe reduction in glucose metabolism. These findings provide insights into the mechanisms behind consciousness disorders, combining network function with measures of brain integrity and behavior.
HUMAN BRAIN MAPPING
(2023)
Article
Neurosciences
Roser Sanchez-Todo, Andre M. Bastos, Edmundo Lopez-Sola, Borja Mercadal, Emiliano Santarnecchi, Earl K. Miller, Gustavo Deco, Giulio Ruffini
Summary: In this study, a new framework called laminar neural mass models (LaNMM) is proposed to simulate electrophysiological measurements by combining conduction physics with NMMs. Using this framework, the location of oscillatory generators in the prefrontal cortex of the macaque monkey is inferred from laminar-resolved data. A minimal model capable of generating coupled slow and fast oscillations is defined, and LaNMM-specific parameters are optimized to fit the recorded data. The functional connectivity (FC) of the model and data are evaluated using an optimization function, and the family of best solutions reproduces the observed FC by selecting specific locations of pyramidal cells and their synapses.
Article
Neurosciences
Francesca Castaldo, Francisco Pascoa dos Santos, Ryan C. Timms, Joana Cabral, Jakub Vohryzek, Gustavo Deco, Mark Woolrich, Karl Friston, Paul Verschure, Vladimir Litvak
Summary: Existing whole-brain models are tailored to specific data modalities, but we propose that they originate from shared network dynamics. To link distinct features of brain activity across modalities, we consider two large-scale models and compare them against real data. Both models can represent functional connectivity and generate oscillatory modes, demonstrating the importance of balanced dynamics and delays.
Article
Cell Biology
Yonatan Sanz Perl, Carla Pallavicini, Juan Piccinini, Athena Demertzi, Vincent Bonhomme, Charlotte Martial, Rajanikant Panda, Naji Alnagger, Jitka Annen, Olivia Gosseries, Agustin Ibanez, Helmut Laufs, Jacobo D. Sitt, Viktor K. Jirsa, Morten L. Kringelbach, Steven Laureys, Gustavo Deco, Enzo Tagliazucchi
Summary: Researchers use whole-brain modeling, data augmentation, and deep learning to determine a mapping representing states of consciousness in a low-dimensional space. They reveal an orderly trajectory from wakefulness to patients with brain injury in a latent space, where coordinates represent metrics related to functional modularity and structure-function coupling. The effects of model perturbations are investigated, providing a geometrical interpretation for the stability and reversibility of states.
Article
Biochemical Research Methods
Pau Clusella, Gustavo S. Deco, Morten Kringelbach, Giulio S. Ruffini, Jordi Garcia-Ojalvo
Summary: In this study, the authors investigate the complex spatiotemporal dynamics in large-scale brain models. They show that destabilization of a synchronized oscillatory state can lead to the emergence of traveling waves and high-dimensional chaos. This work establishes a general route towards understanding spatiotemporal oscillations in the brain.
PLOS COMPUTATIONAL BIOLOGY
(2023)
Article
Biology
Yonatan Sanz Perl, Sol Fittipaldi, Cecilia Gonzalez Campo, Sebastian Moguilner, Josephine Cruzat, Matias E. Fraile-Vazquez, Ruben Herzog, Morten L. Kringelbach, Gustavo Deco, Pavel Prado, Agustin Ibanez, Enzo Tagliazucchi, Muireann Irish
Summary: To address the lack of interventions for neurodegenerative diseases, this study combined deep learning with a model of whole-brain functional connectivity in Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) patients. Disease-specific atrophy maps were used to modulate local parameters, revealing stable dynamics in the hippocampus and insula as indicators of brain atrophy in AD and bvFTD, respectively. By using variational autoencoders, the evolution of different pathologies and their severity were visualized in a low-dimensional latent space. Perturbing the model identified key regions specific to AD and bvFTD, allowing transitions from pathological to healthy brain states. Overall, this study provides new insights into disease progression and control in neurodegeneration through external stimulation and uncovers the underlying dynamic mechanisms driving functional alterations.
Article
Physics, Multidisciplinary
Gustavo Deco, Samuel Liebana Garcia, Yonatan Sanz Perl, Olaf Sporns, Morten L. L. Kringelbach
Summary: This article investigates the importance of turbulence in brain dynamics for information transfer. The study finds that turbulence exists in fast neuronal whole-brain dynamics and facilitates information transfer across spatiotemporal scales.
COMMUNICATIONS PHYSICS
(2023)
Article
Biology
Adrian Ponce-Alvarez, Morten L. Kringelbach, Gustavo Deco
Summary: Human fMRI and dMRI data were used to test the phenomenological renormalization group (PRG) method and found that the scale invariance of rs-fMRI activity may emerge from criticality and exponentially decaying connectivity between brain regions.
COMMUNICATIONS BIOLOGY
(2023)
Article
Neurosciences
Sonsoles Alonso, Anna Tyborowska, Nessa Ikani, Roel J. T. Mocking, Caroline A. Figueroa, Aart H. Schene, Gustavo Deco, Morten L. Kringelbach, Joana Cabral, Henricus G. Ruhe
Summary: 《Dynamic Changes in Brain Network Connectivity during Recurrence of Major Depressive Disorder》This study investigated the dynamic changes in brain connectivity during the transition from remission to recurrence in major depressive disorder (MDD) patients. The findings showed that during recurrence, there was increased activity in the basal ganglia-anterior cingulate cortex and visuo-attentional networks, as well as a longer duration of activation in the default mode network. Additionally, the synchrony between the reward network and the rest of the brain was significantly reduced during recurrence. These results provide initial evidence of altered dynamical exploration of functional networks during a recurrent depressive episode.
HUMAN BRAIN MAPPING
(2023)