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Concept

Synaptic weight

Publications associées (24)

Modeling and simulation of neocortical micro- and mesocircuitry. Part II: Physiology and experimentation

Henry Markram, Felix Schürmann, Daniel Keller, Eilif Benjamin Muller, Michael Reimann, Werner Alfons Hilda Van Geit, Srikanth Ramaswamy, Matthias Wolf, James Gonzalo King, Alexis Arnaudon, András Ecker, Rajnish Ranjan, Jean-Denis Georges Emile Courcol, Armando Romani, Pramod Shivaji Kumbhar, Judit Planas Carbonell, Giuseppe Chindemi, Michael Emiel Gevaert, Christian Andreas Rössert, Fernando Joaquim Leite Pereira, Omar Awile, Mustafa Anil Tuncel, Daniela Egas Santander, James Bryden Isbister, Natali Barros Zulaica, Samuel Lieven D. Lapere, Sirio Bolaños Puchet, Maria Reva, Genrich Ivaska, Tanguy Pierre Louis Damart, Darshan Mandge, Joni Henrikki Herttuainen, Christoph Pokorny, Elvis Boci, Vishal Sood, Thomas Brice Delemontex, Aleksandra Zuzanna Teska, Polina Litvak, Alexander Dietz, Jorge Blanco Alonso, Gianluca Ficarelli, Weina Ji

Cortical dynamics underlie many cognitive processes and emerge from complex multi-scale interactions, which are challenging to study in vivo. Large-scale, biophysically detailed models offer a tool which can complement laboratory approaches. We present a m ...

2024

Mesoscopic population equations for spiking neural networks with synaptic short-term plasticity

Wulfram Gerstner, Tilo Schwalger, Valentin Marc Schmutz

Coarse-graining microscopic models of biological neural networks to obtain mesoscopic models of neural activities is an essential step towards multi-scale models of the brain. Here, we extend a recent theory for mesoscopic population dynamics with static s ...

2020

Mixed-Precision Deep Learning Based on Computational Memory

Irem Boybat Kara, Abu Sebastian

Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally intensive and this has ...

FRONTIERS MEDIA SA2020

Multi-memristive synaptic architectures for training neural networks

Irem Boybat Kara

Highly data-centric AI workloads require that new computing paradigms be adopted because the performance of traditional CPU- and GPU-based systems are limited by data access and transfer. Training deep neural networks with millions of tunable parameters ta ...

EPFL2020

Non-filamentary non-volatile memory elements as synapses in neuromorphic systems

Yusuf Leblebici, Severin Sidler, Alessandro Fumarola

Crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing highly energy-efficient neuromorphic computing systems. For Deep Neural Networks (DNN), where information can be encoded as analog voltage and current levels, ...

IEEE2019

Impact of conductance drift on multi-PCM synaptic architectures

Yusuf Leblebici, Irem Boybat Kara, Abu Sebastian

In-memory computing with nanoscale memristive devices such as phase-change memory (PCM) has emerged as an alternative to conventional von Neumann systems to train deep neural networks (DNN) where a synaptic weight is represented by the device conductance. ...

IEEE2018

Mixed-precision architecture based on computational memory for training deep neural networks

Irem Boybat Kara, Abu Sebastian

Deep neural networks (DNN) have revolutionized the field of machine learning by providing unprecedented human-like performance in solving many real-world problems such as image or speech recognition. Training of large DNNs, however, is a computationally in ...

IEEE2018

Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function

Henry Markram, Kathryn Hess Bellwald, Michael Reimann, Rodrigo de Campos Perin, Giuseppe Chindemi, Martina Scolamiero, Max Christian Nolte, Ran Levi, Katharine Felicity Turner

The lack of a formal link between neural network structure and its emergent function has hampered our understanding of how the brain processes information. We have now come closer to describing such a link by taking the direction of synaptic transmission i ...

Frontiers Media Sa2017

Rich cell-type-specific network topology in neocortical microcircuitry

Henry Markram, Eilif Benjamin Muller, Idan Segev, Srikanth Ramaswamy, Michael Reimann

Uncovering structural regularities and architectural topologies of cortical circuitry is vital for understanding neural computations. Recently, an experimentally constrained algorithm generated a dense network reconstruction of a similar to 0.3-mm(3) volum ...

Nature Publishing Group2017

Hebbian plasticity requires compensatory processes on multiple timescales

Wulfram Gerstner, Friedemann Zenke

We review a body of theoretical and experimental research on Hebbian and homeostatic plasticity, starting from a puzzling observation: while homeostasis of synapses found in experiments is a slow compensatory process, most mathematical models of synaptic p ...

Royal Soc2017

Neuromorphic Computing Using Non-Volatile Memory

Yusuf Leblebici, Irem Boybat Kara, Abu Sebastian, Severin Sidler, Alessandro Fumarola

Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing massively-parallel and highly energy-efficient neuromorphic computing systems. We first review recent advances in the application of NVM devices to thre ...

2016

When Neurons Fail - Technical Report

Rachid Guerraoui, El Mahdi El Mhamdi

Neural networks have been traditionally considered robust in the sense that their precision degrades gracefully with the failure of neurons and can be compensated by additional learning phases. Nevertheless, critical applications for which neural networks ...

EPFL2016

Learning with Surprise : Theory and Applications

Mohammadjavad Faraji

Everybody knows what it feels to be surprised. Surprise raises our attention and is crucial for learning. It is a ubiquitous concept whose traces have been found in both neuroscience and machine learning. However, a comprehensive theory has not yet been de ...

EPFL2016

Theory of representation learning in cortical neural networks

Carlos Stein Naves de Brito

Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same f ...

EPFL2016

PCM for Neuromorphic Applications: Impact of Device Characteristics on Neural Network Performance

Yusuf Leblebici, Irem Boybat Kara, Severin Sidler

The impact of Phase Change Memory (PCM) as well as other Non-Volatile Memory (NVM) device characteristics on the quantitative classification performance of artificial neural networks is studied. Our results show that any NVM-based neural network — not just ...

2015

Statistical structure of neural spiking under non-Poissonian or other non-white stimulation

Tilo Schwalger

Nerve cells in the brain generate sequences of action potentials with a complex statistics. Theoretical attempts to understand this statistics were largely limited to the case of a temporally uncorrelated input (Poissonian shot noise) from the neurons in t ...

Springer2015

Measuring Symmetry, Asymmetry and Randomness in Neural Network Connectivity

Michele Giugliano

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

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

Network Self-Organization Explains the Statistics and Dynamics of Synaptic Connection Strengths in Cortex

Christos Dimitrakakis

The information processing abilities of neural circuits arise from their synaptic connection patterns. Understanding the laws governing these connectivity patterns is essential for understanding brain function. The overall distribution of synaptic strength ...

Public Library Science2013

Successful Reconstruction of a Physiological Circuit with Known Connectivity from Spiking Activity Alone

Joao Emanuel Felipe Gerhard

Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired re ...

Public Library of Science2013