4.3 Article

Effects of Increasing CREB-Dependent Transcription on the Storage and Recall Processes in a Hippocampal CA1 Microcircuit

Journal

HIPPOCAMPUS
Volume 24, Issue 2, Pages 165-177

Publisher

WILEY
DOI: 10.1002/hipo.22212

Keywords

CA1 microcircuit; STDP; CREB; storage; recall; Alzheimer's disease

Categories

Funding

  1. Compagnia di San Paolo
  2. Italian Institute of Technology (IIT)
  3. FIRB

Ask authors/readers for more resources

The involvement of the hippocampus in learning processes and major brain diseases makes it an ideal candidate to investigate possible ways to devise effective therapies for memory-related pathologies like Alzheimer's Disease (AD). It has been previously reported that augmenting CREB activity increases the synaptic Long-Term Potentiation (LTP) magnitude in CA1 pyramidal neurons and their intrinsic excitability in healthy rodents. It has also been suggested that hippocampal CREB signaling is likely to be down-regulated during AD, possibly degrading memory functions. Therefore, the concept of CREB-based memory enhancers, i.e. drugs that would boost memory by activation of CREB, has emerged. Here, using a model of a CA1 microcircuit, we investigate whether hippocampal CA1 pyramidal neuron properties altered by increasing CREB activity may contribute to improve memory storage and recall. With a set of patterns presented to a network, we find that the pattern recall quality under AD-like conditions is significantly better when boosting CREB function with respect to control. The results are robust and consistent upon increasing the synaptic damage expected by AD progression, supporting the idea that the use of CREB-based therapies could provide a new approach to treat AD. (c) 2013 Wiley Periodicals, Inc.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.3
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

Article Computer Science, Information Systems

Comparative investigation of GPU-accelerated triangle-triangle intersection algorithms for collision detection

Lei Xiao, Gang Mei, Salvatore Cuomo, Nengxiong Xu

Summary: This paper introduces a GPU-based parallel collision detection method that efficiently detects collisions between triangulated models by implementing three parallel triangle-triangle intersection algorithms. Experimental results show that the method performs well in detecting collisions between large triangulated models.

MULTIMEDIA TOOLS AND APPLICATIONS (2022)

Article Biophysics

Membrane electrical properties of mouse hippocampal CA1 pyramidal neurons during strong inputs

Daniela Bianchi, Rosanna Migliore, Paola Vitale, Machhindra Garad, Paula A. Pousinha, Helene Marie, Volkmar Lessmann, Michele Migliore

Summary: This paper highlights an electrophysiological feature observed in mouse CA1 pyramidal cells that has been ignored by researchers. The increase in membrane potential during sustained inputs cannot be explained by current computational models. A new model is proposed to address this issue.

BIOPHYSICAL JOURNAL (2022)

Article Biophysics

A machine learning-enhanced biosensor for mercury detection based on an hydrophobin chimera

Anna Pennacchio, Fabio Giampaolo, Francesco Piccialli, Salvatore Cuomo, Eugenio Notomista, Michele Spinelli, Angela Amoresano, Alessandra Piscitelli, Paola Giardina

Summary: The study proposes a novel solution for detecting mercury pollution in sea water by developing a portable biosensor using a hydrophobin-based chimera. This biosensor can accurately detect mercury (II) concentration in sea water, with the advantage of predicting mercury levels without the use of traditional reader devices. The developed biosensor allows for on-site monitoring of marine pollution by non-skilled personnel.

BIOSENSORS & BIOELECTRONICS (2022)

Article Computer Science, Artificial Intelligence

An unsupervised learning framework for marketneutral portfolio

Salvatore Cuomo, Federico Gatta, Fabio Giampaolo, Carmela Iorio, Francesco Piccialli

Summary: This research proposes a portfolio optimization strategy based on assets clustering, aiming to group assets based on their exposure to the same risk factors and construct a market-neutral portfolio. The methodology is applied in various case studies to discuss the results obtained and highlight the strengths and limitations of the proposed strategy.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

Article Computer Science, Information Systems

Heterogeneous data fusion considering spatial correlations using graph convolutional networks and its application in air quality prediction

Zhengjing Ma, Gang Mei, Salvatore Cuomo, Francesco Piccialli

Summary: This paper proposes a deep learning method that combines graph convolutional networks (GCNs) to fuse heterogeneous data for predicting the future state of certain observations. The effectiveness of this approach was verified in an air quality prediction scenario, showing promising results.

JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES (2022)

Article Mathematics, Applied

Scientific Machine Learning Through Physics-Informed Neural Networks: Where we are and What's Next

Salvatore Cuomo, Vincenzo Schiano Di Cola, Fabio Giampaolo, Gianluigi Rozza, Maziar Raissi, Francesco Piccialli

Summary: Physics-Informed Neural Networks (PINN) are a type of neural network that incorporates model equations, such as partial differential equations, as a component. PINNs have been used to solve various types of equations, including fractional equations and stochastic partial differential equations. Current research focuses on optimizing PINN through different aspects, such as activation functions, gradient optimization techniques, neural network structures, and loss function structures. Despite the demonstrated feasibility of PINN in certain cases compared to traditional numerical techniques, there are still unresolved theoretical issues.

JOURNAL OF SCIENTIFIC COMPUTING (2022)

Article Mathematics, Applied

A Sojourn-Based Approach to Semi-Markov Reinforcement Learning

Giacomo Ascione, Salvatore Cuomo

Summary: This paper introduces a new approach based on the sojourn time process for dealing with discrete-time semi-Markov decision processes. With this approach, the agent can consider different actions based on the sojourn time of the process in the current state. A numerical method based on Q-learning algorithms is investigated, and the algorithm is evaluated through two examples.

JOURNAL OF SCIENTIFIC COMPUTING (2022)

Article Multidisciplinary Sciences

Impaired synaptic plasticity in an animal model of autism exhibiting early hippocampal GABAergic-BDNF/TrkB signaling alterations

Martina Sgritta, Beatrice Vignoli, Domenico Pimpinella, Marilena Griguoli, Spartaco Santi, Andrzej Bialowas, Grzegorz Wiera, Paola Zacchi, Francesca Malerba, Cristina Marchetti, Marco Canossa, Enrico Cherubini

Summary: In Neurodevelopmental Disorders, changes in synaptic plasticity can lead to structural alterations in neuronal circuits involved in cognitive functions. This study tested this hypothesis in mice with the human R451C mutation of the Nlgn3 gene, which is found in some families with autistic children. The results showed that these mice failed to exhibit STD-LTP and this effect persisted into adulthood. Similar results were found in mice lacking the Nlgn3 gene. The loss of STD-LTP was associated with a premature shift of GABA and reduced BDNF availability, suggesting a potential mechanism underlying cognitive deficits in forms of Autism caused by synaptic dysfunctions.

ISCIENCE (2023)

Article Neurosciences

Lamotrigine rescues neuronal alterations and prevents seizure-induced memory decline in an Alzheimer's disease mouse model

Emanuela Rizzello, Domenico Pimpinella, Annabella Pignataro, Giulia Titta, Elisabetta Merenda, Michela Saviana, Giovanni Francesco Porcheddu, Chiara Paolantoni, Francesca Malerba, Corinna Giorgi, Giulia Curia, Silvia Middei, Cristina Marchetti

Summary: Epilepsy is a comorbidity associated with Alzheimer's disease, and investigating this association in the early stages of AD can provide insights into the pathology. The study found that repeated seizures caused memory deficits and an increase in A beta levels in pre-symptomatic Alzheimer's mice. It also identified neuronal alterations and suggested the potential use of the antiepileptic drug lamotrigine in countering AD acceleration induced by seizures.

NEUROBIOLOGY OF DISEASE (2023)

Article Mathematics, Applied

Solving groundwater flow equation using physics-informed neural networks

Salvatore Cuomo, Mariapia De Rosa, Fabio Giampaolo, Stefano Izzo, Vincenzo Schiano Di Cola

Summary: In recent years, Scientific Machine Learning (SciML) methods, particularly Physics-Informed Neural Networks (PINNs), have become popular for solving non-linear partial differential equations (PDEs). This paper numerically tackles the groundwater flow equations using a PINN approach, approximating the Dirac distribution and analyzing its computational ability in higher-dimensional cases. The effectiveness of PINNs is demonstrated through numerical experiments in hydrological applications, comparing the results with the Finite Difference Method (FDM) and highlighting the advantages of PINNs in solving PDEs without discretization.

COMPUTERS & MATHEMATICS WITH APPLICATIONS (2023)

Article Biology

A stochastic model of hippocampal synaptic plasticity with geometrical readout of enzyme dynamics

Yuri Elias Rodrigues, Cezar M. Tigaret, Helene Marie, Cian O'Donnell, Romain Veltz

Summary: Discovering the rules of synaptic plasticity is crucial for understanding brain learning. Existing models either lack flexibility to fit experimental data or are too complex to interpret. To overcome these limitations, we propose a new plasticity rule based on a geometrical readout mechanism that accurately predicts plasticity outcomes. Our model successfully reproduces various experimental conditions and suggests that spike timing irregularity strongly affects plasticity outcome. This modelling approach can be applied to other synapses to discover their plasticity rules.

ELIFE (2023)

Article Mathematics, Applied

Time discretization in the solution of parabolic PDEs with ANNs

Francesco Calabro, Salvatore Cuomo, Daniela di Serafino, Giuseppe Izzo, Eleonora Messina

Summary: This paper investigates the resolution of parabolic PDEs using Extreme Learning Machine (ELMs) Neural Networks. The ELMs setting is considered, and a single hidden layer is admitted, making the ANN trainable at a modest computational cost compared to Deep Learning Neural Networks. The results of numerical experiments confirm that ELM-based solution techniques combined with BDF methods can provide high-accuracy solutions of parabolic PDEs.

APPLIED MATHEMATICS AND COMPUTATION (2023)

Article Mechanics

Physics-informed neural networks approach for 1D and 2D Gray-Scott systems

Fabio Giampaolo, Mariapia De Rosa, Pian Qi, Stefano Izzo, Salvatore Cuomo

Summary: This study proposes a computational approach based on Physics-Informed Neural Networks (PINNs) to deal with nonlinear partial differential equation systems. Using this method, the researchers successfully solve a reaction-diffusion system involving an irreversible chemical reaction and approximate the characteristic Turing patterns for different parameter configurations.

ADVANCED MODELING AND SIMULATION IN ENGINEERING SCIENCES (2022)

Article Physics, Multidisciplinary

Magnetic Force-Free Theory: Nonlinear Case

Brunello Tirozzi, Paolo Buratti

Summary: In this paper, a theory of force-free magnetic field useful for explaining the formation of convex closed sets, bounded by a magnetic separatrix in the plasma, is developed. The theory utilizes an analytic method based on a first-order expansion of the poloidal magnetic flux function, and solves the Grad-Shafranov equation to obtain an analytic expression for the solution. This work is important for understanding the application of force-free magnetic fields in laboratory and astrophysical plasmas.

PHYSICS (2022)

Article Computer Science, Theory & Methods

Effects of spatial decomposition on the efficiency of kNN search in spatial interpolations

Fan Naijie, Mei Gang, Ding Zengyu, Salvatore Cuomo, Xu Nengxiong

Summary: This study evaluates the impact of the size of uniform grid cells on the efficiency of k Nearest Neighbor (kNN) search and measures the spatial distribution of scattered points using the standard deviation of point coordinates. The results indicate that as the standard deviation of point coordinates increases, the relatively optimal size of grid cells decreases and eventually converges.

INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS (2022)

No Data Available