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Slow dynamics in structured neural network models

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Network Alignment: Theory, Algorithms, and Applications

Ehsan Kazemi

Networks are central in the modeling and analysis of many large-scale human and technical systems, and they have applications in diverse fields such as computer science, biology, social sciences, and economics. Recently, network mining has been an active a ...

EPFL2016

Scalable Neuron Circuit Using Conductive-Bridge RAM for Pattern Reconstructions

Behnoush Attarimashalkoubeh

— A novel neuron circuit using a Cu/Ti/Al2O3-based conductive-bridge random access memory (CBRAM) device for hardware neural networks that utilize nonvolatile memories as synaptic weights is introduced. The neuronal operations are designed and proved using ...

Institute of Electrical and Electronics Engineers2016

Review of Advances in Neural Networks: Neural Design Technology Stack

Yusuf Leblebici, Stanislaw Andrzej Wozniak

This review provides a high-level synthesis of significant recent advances in artificial neural network research, as well as multi-disciplinary concepts connected to the far-reaching goal of obtaining intelligent systems. We assume that a global outlook of ...

Elsevier2016

Local trapping for elliptic random walks in random environments in Z(d)

Daniel Kious

We consider elliptic random walks in i.i.d. random environments on . The main goal of this paper is to study under which ellipticity conditions local trapping occurs. Our main result is to exhibit an ellipticity criterion for ballistic behavior which exten ...

Springer Heidelberg2016

Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze–like Environments

Wulfram Gerstner, Dane Sterling Corneil

Rodents navigating in a well-known environment can rapidly learn and revisit observed reward locations, often after a single trial. While the mechanism for rapid path planning is unknown, the CA3 region in the hippocampus plays an important role, and emerg ...

Massachusetts Institute of Technology Press2015

Memory formation and recall in recurrent spiking neural networks

Friedemann Zenke

Our brain has the capacity to analyze a visual scene in a split second, to learn how to play an instrument, and to remember events, faces and concepts. Neurons underlie all of these diverse functions. Neurons, cells within the brain that generate and trans ...

EPFL2014

Statistical models of effective connectivity in neural microcircuits

Joao Emanuel Felipe Gerhard

To appreciate how neural circuits in the brain control behaviors, we must identify how the neurons comprising the circuit are connected. Neuronal connectivity is difficult to determine experimentally, whereas neuronal activity can often be readily measured ...

EPFL2014

Measuring Symmetry, Asymmetry and Randomness in Neural Network Connectivity

Michele Giugliano, Eleni Vasilaki

Cognitive functions are stored in the connectome, the wiring diagram of the brain, which exhibits non-random features, so-called motifs. In this work, we focus on bidirectional, symmetric motifs, i.e. two neurons that project to each other via connections ...

Public Library of Science2014

Cortical Columns, Models of

Sean Lewis Hill

The annual Computational Neuroscience Meeting (CNS) began in 1990 as a small workshop called Analysis and Modeling of Neural Systems. The goal of the workshop was to explore the boundary between neuroscience and computation. Riding on the success of severa ...

Springer2014

Computing the size and number of neuronal clusters in local circuits

Henry Markram, Rodrigo de Campos Perin, Martin Telefont

The organization of connectivity in neuronal networks is fundamental to understanding the activity and function of neural networks and information processing in the brain. Recent studies show that the neocortex is not only organized in columns and layers b ...

Frontiers Research Foundation2013