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Liquid state machines and cultured cortical networks: The separation property

Karl P. Dockendorf, Il Park, Ping He, José C. Príncipe, Thomas B. DeMarse
2009 Biosystems (Amsterdam. Print)  
To study those properties, a Liquid State Machine (LSM) approach was employed in which the liquid (typically an artificial neural network) was replaced with a living cortical network and the input and  ...  A key requirement of the LSM architecture is that inputs into the liquid state must result in separable outputs based on the liquid's response (separation property).  ...  Acknowledgment This work was supported in part by the National Science Foundation Collaborative Research in Computational Neuroscience under grant number ECS-0422718.  ... 
doi:10.1016/j.biosystems.2008.08.001 pmid:18761392 fatcat:4h2zv36vvre7rjicqsviygid2m

Spatiotemporal Memory Is an Intrinsic Property of Networks of Dissociated Cortical Neurons

H. Ju, M. R. Dranias, G. Banumurthy, A. M. J. VanDongen
2015 Journal of Neuroscience  
These results show that the ability to process and integrate complex spatiotemporal information is an intrinsic property of generic cortical networks that does not require specifically designed circuits  ...  We have explored the spatiotemporal information-processing capability of networks formed from dissociated rat E18 cortical neurons growing in culture.  ...  The liquid state machine (LSM), a spiking neural network paradigm modeled after cortical microcircuits, can operate in the temporal domain because it implements a fading memory for input stimuli by a combination  ... 
doi:10.1523/jneurosci.3793-14.2015 pmid:25740531 fatcat:lyw5jj7vcbebpjothjfs6r35ua

Short-Term Memory in Networks of Dissociated Cortical Neurons

M. R. Dranias, H. Ju, E. Rajaram, A. M. J. VanDongen
2013 Journal of Neuroscience  
Electrophysiological activity was recorded from primary cultures of dissociated rat cortical neurons plated on multielectrode arrays.  ...  neuronal networks, thereby setting the stage for therapeutic applications using this platform.  ...  We have employed a liquid state machine (LSM) classifier for this purpose (see Materials and Methods).  ... 
doi:10.1523/jneurosci.2718-12.2013 pmid:23365233 pmcid:PMC6619106 fatcat:c2s36rhmvfguxbdntflopv3cmi

Consciousness in Neural Networks?

Edmund T Rolls
1997 Neural Networks  
Freeman Three Centuries of Category Errors in Studies of the Neural Basis of Consciousness and Intentionality [Abstract] [Full text] (PDF 118.1 Kb) Chris Browne, Richard Evans, Nick Sales and Igor Aleksander  ...  Coupling between two sets of two-stage modules of Figure 1 and Figure 2 to achieve competition between the activities on the second stages through the interaction of those stages with the NRT-thalamic  ...  It provided materials for Chapter 2 in ''Societies of Brains'' (1995), and is given here with the permission of the publisher, Lawrence Erlbaum Associates, Hillsdale, NJ.  ... 
doi:10.1016/s0893-6080(97)00049-x pmid:12662513 fatcat:q3rt2km7snbmlcbpkw6lfac3gq

Functional identification of biological neural networks using reservoir adaptation for point processes

Tayfun Gürel, Stefan Rotter, Ulrich Egert
2009 Journal of Computational Neuroscience  
We demonstrate the predictive power of these ESNs on various tasks with cultured and simulated biological neural networks.  ...  The complexity of biological neural networks does not allow to directly relate their biophysical properties to the dynamics of their electrical activity.  ...  We also appreciate the help of Steffen Kandler, Samorah Okujeni and Oliver Weihberger with culture preparations and recordings.  ... 
doi:10.1007/s10827-009-0176-0 pmid:19639401 pmcid:PMC2940037 fatcat:baygyf7opnfvpng7vgn4e4zaoq

Recent advances in physical reservoir computing: A review

Gouhei Tanaka, Toshiyuki Yamane, Jean Benoit Héroux, Ryosho Nakane, Naoki Kanazawa, Seiji Takeda, Hidetoshi Numata, Daiju Nakano, Akira Hirose
2019 Neural Networks  
It is derived from several recurrent neural network models, including echo state networks and liquid state machines.  ...  We discuss the current issues and perspectives related to physical reservoir computing, in order to further expand its practical applications and develop next-generation machine learning systems.  ...  Acknowledgments We would like to thank Ze Hei for his assistance in collecting the relevant papers and the anonymous reviewers for helpful comments that improved the quality of this review.  ... 
doi:10.1016/j.neunet.2019.03.005 fatcat:u4vjykpyxnch3n24kprqoi2tsy

Emergent Criticality in Complex Turing B-Type Atomic Switch Networks

Adam Z. Stieg, Audrius V. Avizienis, Henry O. Sillin, Cristina Martin-Olmos, Masakazu Aono, James K. Gimzewski
2011 Advanced Materials  
to reservoir methods such as liquid state machines and echo state networks.  ...  Emergent behavior was observed during pulsed voltage stimulation, in analogy to methods employed in neuroscience to probe cortical cultures.  ... 
doi:10.1002/adma.201103053 pmid:22329003 fatcat:a3mqd366mzberbtpjhda4yv7hi

Neurodynamics and the mind

Wlodzislaw Duch
2011 The 2011 International Joint Conference on Neural Networks  
Psychology and neural sciences have turned away from such questions and experimentally oriented philosophers discovered formidable obstacles in attempts to answer even simple questions about the nature  ...  What do we really known about ourselves and how do we know it?  ...  In effect initial layers of signal transformation approximate liquid state machine [56] .  ... 
doi:10.1109/ijcnn.2011.6033649 dblp:conf/ijcnn/Duch11 fatcat:2jpjttgtrfaolcf7wbzvguk44i

Co-evolutionary Learning in Liquid Architectures [chapter]

Igal Raichelgauz, Karina Odinaev, Yehoshua Y. Zeevi
2005 Lecture Notes in Computer Science  
A recentlyintroduced Liquid-State-Machine (LSM) paradigm provides a computational framework for applying a model of cortical neural microcircuit as a core computational unit in classification and recognition  ...  This approach is inspired by neurobiological findings from exvivo multi-cellular electrical recordings and injection of dopamine to the neural culture.  ...  Different input streams to the microcircuit cause different internal sates (liquid states) of the system and enable the inputs to be separated. Liquid states of the NM are read by memoryless Readout.  ... 
doi:10.1007/11494669_30 fatcat:s4l722kzwjdotijcmwgqbgqvjy

Revealing architectural order with polarized light imaging and deep neural networks [article]

Syuan-Ming Guo, Anitha Priya Krishnan, Jenny Folkesson, Ivan Ivanov, Bryant Chhun, Nathan Cho, Manuel Leonetti, Shalin B. Mehta
2019 bioRxiv   pre-print
Our open-source python software for reconstruction of optical properties and training the neural networks is available on GitHub.  ...  We report synergistic use of polarized light microscopy, reconstruction of complementary optical properties, and deep neural networks to identify ordered structures.  ...  We thank Greg Huber, Loic Royer, Joshua Batson, Jim Karkanias, Joe DeRisi, and Steve Quake from the Chan Zuckerberg Biohub for numerous discussions.  ... 
doi:10.1101/631101 fatcat:grjmxtbj4bewzjkpakh6zh4jsu

Macromolecular networks and intelligence in microorganisms

Hans V. Westerhoff, Aaron N. Brooks, Evangelos Simeonidis, Rodolfo García-Contreras, Fei He, Fred C. Boogerd, Victoria J. Jackson, Valeri Goncharuk, Alexey Kolodkin
2014 Frontiers in Microbiology  
Biological networks constitute a type of information and communication technology (ICT): they receive information from the outside and inside of cells, integrate and interpret this information, and then  ...  Yet, four billion years of evolution could have selected networks with topologies and dynamics that confer traits analogous to this intelligence, even though they were outside the intercellular networks  ...  Since, in a well-mixed environment such as a stirred liquid culture of planktonic cells, one cell can sense the auto-inducer produced by any other cell, communication among network components should be  ... 
doi:10.3389/fmicb.2014.00379 pmid:25101076 pmcid:PMC4106424 fatcat:mm5xevb23jexrawtydiz5litdu

Reconstruction of Causal Networks by Set Covering [chapter]

Nick Fyson, Tijl De Bie, Nello Cristianini
2011 Lecture Notes in Computer Science  
We hope this can provide support when attempting to understand the influence of dynamics, topology and evolution on the system-level dynamics exhibited by a complex system.  ...  We present a method for the reconstruction of networks, based on the order of nodes visited by a stochastic branching process.  ...  cultured at an air-liquid interface.  ... 
doi:10.1007/978-3-642-20267-4_21 fatcat:yal6pqp7frcxvb4alohkgm4u3y

The F-actin cortical network is a major factor influencing the organization of the secretory machinery in chromaffin cells

C. J. Torregrosa-Hetland, J. Villanueva, D. Giner, I. Lopez-Font, A. Nadal, I. Quesada, S. Viniegra, G. Exposito-Romero, A. Gil, V. Gonzalez-Velez, J. Segura, L. M. Gutierrez
2011 Journal of Cell Science  
We have studied how the F-actin cytoskeleton is involved in establishing the heterogeneous intracellular Ca 2+ levels ([Ca 2+ ] i ) and in the organization of the exocytotic machinery in cultured bovine  ...  The influence of this cortical organization on the propagation of [Ca 2+ ] i can be modelled, illustrating how it serves to define rapid exocytosis.  ...  We also acknowledge the financial support received from the CONSOLIDER programme (CSD07-00023) and CIBERDEM (Instituto de Salud Carlos III).  ... 
doi:10.1242/jcs.078600 pmid:21303931 fatcat:yrdf7nlbsndutmmimxwer7ew2i

A Spiking Neural Network Based Cortex-Like Mechanism and Application to Facial Expression Recognition

Si-Yao Fu, Guo-Sheng Yang, Xin-Kai Kuai
2012 Computational Intelligence and Neuroscience  
By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented.  ...  spiking neural networks (SNNs).  ...  simplest yet most effective model describing the dynamic of the neurons, and its extension, spike response models (SRMs) [13] , liquid state machine (LSM) [13] , and Izhikevich model [14] .  ... 
doi:10.1155/2012/946589 pmid:23193391 pmcid:PMC3501821 fatcat:u6dmyvrx7rfphhzc2znwbip3uq

Natural Signal Classification by Neural Cliques and Phase-Locked Attractors

I. Raichelgauz, K. Odinaev, Y.Y. Zeevi
2006 2006 International Conference of the IEEE Engineering in Medicine and Biology Society  
Cortical neural networks are responsible for identification, recognition and classification of natural signals mediated by various sensory channels.  ...  These tasks are still too complex to be accomplished by state-of-the-art engineering systems.  ...  Moreover, LSM framework is based on separation property, suggesting that injections of any two sufficiently different inputs ( ) u and ( ) v will drive the liquid into sufficiently different liquid-states  ... 
doi:10.1109/iembs.2006.260923 pmid:17959488 fatcat:s5rsyvqm6zafhkexhh7iw4iupa
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