Memory formation: from network structure to neural dynamics

Feldt, Sarah; Wang, Jane X.; Hetrick, Vaughn L.; Berke, Joshua D.; Żochowski, Michał

doi:10.1098/rsta.2010.0033

Cited by 4 publications

(4 citation statements)

References 39 publications

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“…Although experimental investigation in living developing neuronal networks offers great possibilities for understanding structure-function relationships, most structure-function studies so far have analyzed networks using neurons growing in cultures and not in vivo [50,55,[90][91][92]. Indeed, cultures are relatively easy to manipulate and record from, and therefore seem to be a more attractive model for many researchers interested in studying systems neuroscience.…”

Section: Statistical Relationshipmentioning

confidence: 99%

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Dissecting functional connectivity of neuronal microcircuits: experimental and theoretical insights

Feldt

Bonifazi

Cossart

2011

Trends in Neurosciences

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172

157

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Section: Statistical Relationshipmentioning

confidence: 99%

“…Much theoretical and computational work has studied the important relationship between network topology and dynamics [33,[48][49][50][51][52][53][54][55]. For example, consider a network of neurons built on a lattice with regular (local), random (global) or small-world connectivity.…”

Section: Statistical Relationshipmentioning

confidence: 99%

Dissecting functional connectivity of neuronal microcircuits: experimental and theoretical insights

Feldt

Bonifazi

Cossart

2011

Trends in Neurosciences

Self Cite

172

157

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“…The changes in the neuronal structure and cell cycle are linked to neurodegeneration and memory function in the brain [ 48 – 50 ]. Therefore, we evaluated the memory function of wild-type and obese ( ob / ob ) mice after the knockdown of circTshz2-2 in the brain.…”

Section: Resultsmentioning

confidence: 99%

Obesity-linked circular RNA circTshz2-2 regulates the neuronal cell cycle and spatial memory in the brain

Yoon

Lim

et al. 2021

Mol Psychiatry

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Metabolic syndromes, including obesity, cause neuropathophysiological changes in the brain, resulting in cognitive deficits. Only a few studies explored the contribution of non-coding genes in these pathophysiologies. Recently, we identified obesity-linked circular RNAs (circRNA) by analyzing the brain cortices of high-fat-fed obese mice. In this study, we scrutinized a conserved and neuron-specific circRNA, circTshz2-2, which affects neuronal cell cycle and spatial memory in the brain. Transcriptomic and cellular analysis indicated that circTshz2-2 dysregulation altered the expression of cell division-related genes and induced cell cycle arrest at the G2/M phase of the neuron. We found that circTshz2-2 bound to the YY1 transcriptional complex and suppressed Bdnf transcription. Suppression of circTshz2-2 increased BDNF expression and reduced G2/M checkpoint proteins such as Cyclin B2 and CDK1 through BDNF/TrkB signaling pathway, resulting in cell cycle arrest and neurite elongation. Inversely, overexpression of circTshz2-2 decreased BDNF expression, induced cell cycle proteins, and shortened the neurite length, indicating that circTshz2-2 regulates neuronal cell cycle and structure. Finally, we showed that circTshz2-2 affects spatial memory in wild-type and obese mice. Our data have revealed potential regulatory roles of obesity-related circTshz2-2 on the neuronal cell cycle and memory function providing a novel link between metabolic syndromes and cognitive deficits.

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“…Decoding the full neural correlates of brain function will require an understanding not only of how information is coded and transformed, but also of how it is stored in the short term and how the network transforms itself to remember in the long term. Feldt et al (2010) address in their article novel analysis techniques for determining both related-element dynamics from many-neuron recordings and to track memory formation in evolving or learning networks.…”

Section: Experimental Nonlinear Dynamicsmentioning

confidence: 99%

Experimental nonlinear dynamics

Gluckman

2010

Phil. Trans. R. Soc. A.

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Although it is difficult to point to a definite origin of the field of nonlinear dynamics, it evolved in part from attempts to understand two fundamentally different types of deterministic systems-very simple systems with as few as three variables on the one side that generate erratic non-periodic dynamics, exemplified by the three-body problem; and nearly infinite dimensional systems, such as fluids, that transition from very well-behaved dynamics through a range of increasingly complex space and time-varying dynamics, exemplified by the transitions from laminar to turbulent hydrodynamic flows.By the 1980s, the nonlinear dynamics community fitted roughly into two camps, those looking at and harnessing low-dimensional chaos-the emergence of complex unpredictable behaviour from systems with few degrees of freedom; and those looking at pattern formation-or the emergence of order, or coherentstructured dynamics in space and time-from systems with infinite degrees of freedom. The classic experimental systems for the study of, and application of, chaos were circuits and pendulums, lasers and population dynamics. Pattern formation was studied in fluid flows, crystal growth dynamics, chemical reactions and biology. Often, there was cross-pollination between these camps in developed theoretical tools, computational hardware, data analysis and visualization technology and experimental approach. The objectives overall were to both understand the origins of the phenomena and provide insight into how to harness the origins of complexity, exemplified in the 1990s by the development of chaos control techniques.The current issue is a cross-sectional sampling of the broad experimental applications now addressed in the nonlinear dynamics community. It starts with a solution to a classic hydrodynamic problem in Metcalfe et al. (2010). The challenge addressed is how to optimally mix fluids-critical, for example, in chemical reactors and heat exchangers-with simple laminar, low-energy, flows. Efficient mixing brings arbitrary pairs of fluid particles together. The simplest of such flows does not have the mixture of convergent and divergent points needed to do so. But, as the authors demonstrate, super-positions of such flow patterns, achieved by alternating between boundary conditions, yield highly chaotic fluid trajectories and therefore ideal mixing.The next two articles address issues and applications in chaotic dynamics. In the 1990s, atomic physics was essentially re-born with the perfection of techniques to greatly cool and trap individual, or small numbers of, atoms (Letokhov et al. 1995). Such systems then allow for real experimental systems Networks abound both in nature and in our man-made world. Understanding the underlying structure of such networks, how the individual elements' dynamics determines and is modulated by the group dynamics, how information is processed and transmitted through such networks and how robust they are to changes in connections is currently a major undertaking. Kocarev et al. (2010) address one ...

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Memory formation: from network structure to neural dynamics

Cited by 4 publications

References 39 publications

Dissecting functional connectivity of neuronal microcircuits: experimental and theoretical insights

Dissecting functional connectivity of neuronal microcircuits: experimental and theoretical insights

Obesity-linked circular RNA circTshz2-2 regulates the neuronal cell cycle and spatial memory in the brain

Experimental nonlinear dynamics

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