Dynamics and Robustness of Familiarity Memory

Cortes, Jesús M.; Greve, Andrea; Barrett, Adam B.; Rossum, Mark C. W. van

doi:10.1162/neco.2009.12-08-921

Cited by 10 publications

(7 citation statements)

References 31 publications

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“…For this “yes or no” discrimination task, the storage capacity has a different nature in the size scaling; it scales with N∧2 versus the N scaling occurring for recollection [70]–[72]. Thus, the same Hopfield network can both perform recollection (full pattern retrieval) and familiarity (the discrimination between one memory which was encoded before or just a novel one, never-seen before), and very interestingly the different memory nature has a very different storage capacity (N∧2 for familiarity vs N for recollection).…”

Section: Discussionmentioning

confidence: 99%

Attractor Metabolic Networks

et al. 2013

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BackgroundThe experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a Systemic Metabolic Structure in the cell, characterized by a set of different enzymatic reactions always locked into active states (metabolic core) while the rest of the catalytic processes are only intermittently active. This global metabolic structure was verified for Escherichia coli, Helicobacter pylori and Saccharomyces cerevisiae, and it seems to be a common key feature to all cellular organisms. In concordance with these observations, the cell can be considered a complex metabolic network which mainly integrates a large ensemble of self-organized multienzymatic complexes interconnected by substrate fluxes and regulatory signals, where multiple autonomous oscillatory and quasi-stationary catalytic patterns simultaneously emerge. The network adjusts the internal metabolic activities to the external change by means of flux plasticity and structural plasticity.Methodology/Principal FindingsIn order to research the systemic mechanisms involved in the regulation of the cellular enzymatic activity we have studied different catalytic activities of a dissipative metabolic network under different external stimuli. The emergent biochemical data have been analysed using statistical mechanic tools, studying some macroscopic properties such as the global information and the energy of the system. We have also obtained an equivalent Hopfield network using a Boltzmann machine. Our main result shows that the dissipative metabolic network can behave as an attractor metabolic network.Conclusions/SignificanceWe have found that the systemic enzymatic activities are governed by attractors with capacity to store functional metabolic patterns which can be correctly recovered from specific input stimuli. The network attractors regulate the catalytic patterns, modify the efficiency in the connection between the multienzymatic complexes, and stably retain these modifications. Here for the first time, we have introduced the general concept of attractor metabolic network, in which this dynamic behavior is observed.

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Section: Discussionmentioning

confidence: 99%

Attractor Metabolic Networks

et al. 2013

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“…(Lisman and Idiart, 1995) as well as the multiple subpopulations network presented in this paper. Additionally, a different scaling of sensory coding capacity may arise if networks with lateral inhibition are employed in familiarity memory (Cortes et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

Brain-Based Devices for Neuromorphic Computer Systems

Gall¹

2013

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Using Government drawings, specifications, or other data included in this document for any purpose other than Government procurement does not in any way obligate the U.S. Government. The fact that the Government formulated or supplied the drawings, specifications, or other data does not license the holder or any other person or corporation; or convey any rights or permission to manufacture, use, or sell any patented invention that may relate to them. This report is the result of contracted fundamental research deemed exempt from public affairs security and policy review in accordance with SAF/AQR memorandum dated 10 Dec 08 and AFRL/CA policy clarification memorandum dated 16 Jan 09. This report is available to the general public, including foreign nationals. Copies may be obtained from the Defense Technical Information Center (DTIC) (http://www.dtic.mil).

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“…, where µ i and σ 2 i represent mean and variance in group i (for further details see [54]). Therefore, the higher the SNR, the bigger separability of the two distributions.…”

Section: Separability Of Group Matrices Measured By the Signal To Noimentioning

confidence: 99%

Hyperconnectivity of the default mode network in multiorgan dysfunction syndrome

Jiménez-Marín

Rivera

Boado

et al. 2018

Preprint

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Multiple Organ Dysfunction Syndrome (MODS) is a systemic physiological disorder affecting two or more body organs triggered after an insult complication. Beyond the systemic failure, patients who survive MODS present cognitive and neurological impairments that remain stable even several years after Intensive Care Unit (ICU) discharge. Here, we focus on the specific situation of MODS patients with no apparent brain damage (NABD), where the mechanisms driving cognitive impairment at long term are not well-understood. We recruit N 1 = 13 MODS patients with NABD at 6 months after ICU discharge, together with N 2 = 13 healthy controls (matched by age, sex and years

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Dynamics and Robustness of Familiarity Memory

Cited by 10 publications

References 31 publications

Attractor Metabolic Networks

Attractor Metabolic Networks

Brain-Based Devices for Neuromorphic Computer Systems

Hyperconnectivity of the default mode network in multiorgan dysfunction syndrome

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