scholarly journals A distance constrained synaptic plasticity model of C. elegans neuronal network

2017 ◽  
Vol 469 ◽  
pp. 313-322 ◽  
Author(s):  
Rahul Badhwar ◽  
Ganesh Bagler
Keyword(s):  
Synaptic Plasticity ◽  
Neuronal Network ◽  
Plasticity Model ◽  
C Elegans

scholarly journals STDP-driven networks and the C. elegans neuronal network

2010 ◽  
Vol 389 (18) ◽  
pp. 3900-3914 ◽  
Author(s):  
Quansheng Ren ◽  
Kiran M. Kolwankar ◽  
Areejit Samal ◽  
Jürgen Jost
Keyword(s):  
Neuronal Network ◽  
C Elegans

scholarly journals Soft-wired long-term memory in a natural recurrent neuronal network

2020 ◽  
Author(s):  
Miguel A. Casal ◽  
Santiago Galella ◽  
Oscar Vilarroya ◽  
Jordi Garcia-Ojalvo
Keyword(s):  
Neuronal Network ◽  
Neuronal Networks ◽  
Initial Conditions ◽  
Permutation Entropy ◽  
Long Term Memory ◽  
Term Memory ◽  
C Elegans ◽  
Living Organisms ◽  
Recurrent Connections

Neuronal networks provide living organisms with the ability to process information. They are also characterized by abundant recurrent connections, which give rise to strong feed-back that dictates their dynamics and endows them with fading (short-term) memory. The role of recurrence in long-term memory, on the other hand, is still unclear. Here we use the neuronal network of the roundworm C. elegans to show that recurrent architectures in living organisms can exhibit long-term memory without relying on specific hard-wired modules. A genetic algorithm reveals that the experimentally observed dynamics of the worm’s neuronal network exhibits maximal complexity (as measured by permutation entropy). In that complex regime, the response of the system to repeated presentations of a time-varying stimulus reveals a consistent behavior that can be interpreted as soft-wired long-term memory.A common manifestation of our ability to remember the past is the consistence of our responses to repeated presentations of stimuli across time. Complex chaotic dynamics is known to produce such reliable responses in spite of its characteristic sensitive dependence on initial conditions. In neuronal networks, complex behavior is known to result from a combination of (i) recurrent connections and (ii) a balance between excitation and inhibition. Here we show that those features concur in the neuronal network of a living organism, namely C. elegans. This enables long-term memory to arise in an on-line manner, without having to be hard-wired in the brain.

scholarly journals Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network of Caenorhabditis elegans

2018 ◽  
Vol 373 (1758) ◽  
pp. 20170377 ◽  
Author(s):  
Hexuan Liu ◽  
Jimin Kim ◽  
Eli Shlizerman
Keyword(s):  
Time Series ◽  
Nervous System ◽  
Caenorhabditis Elegans ◽  
Graphical Models ◽  
Neuronal Network ◽  
Graphical Model ◽  
Time Series Data ◽  
Series Data ◽  
Neuronal Responses ◽  
C Elegans

We propose an approach to represent neuronal network dynamics as a probabilistic graphical model (PGM). To construct the PGM, we collect time series of neuronal responses produced by the neuronal network and use singular value decomposition to obtain a low-dimensional projection of the time-series data. We then extract dominant patterns from the projections to get pairwise dependency information and create a graphical model for the full network. The outcome model is a functional connectome that captures how stimuli propagate through the network and thus represents causal dependencies between neurons and stimuli. We apply our methodology to a model of the Caenorhabditis elegans somatic nervous system to validate and show an example of our approach. The structure and dynamics of the C. elegans nervous system are well studied and a model that generates neuronal responses is available. The resulting PGM enables us to obtain and verify underlying neuronal pathways for known behavioural scenarios and detect possible pathways for novel scenarios. This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.

scholarly journals Voltage-based inhibitory synaptic plasticity: network regulation, diversity, and flexibility

2020 ◽  
Author(s):  
Victor Pedrosa ◽  
Claudia Clopath
Keyword(s):  
Synaptic Plasticity ◽  
Pyramidal Cell ◽  
Brain Regions ◽  
Postsynaptic Membrane ◽  
Network Activity ◽  
Inhibitory Neurons ◽  
Plasticity Model ◽  
Feedforward Network ◽  
Voltage Dependent ◽  
Homeostatic Mechanisms

AbstractNeural networks are highly heterogeneous while homeostatic mechanisms ensure that this heterogeneity is kept within a physiologically safe range. One of such homeostatic mechanisms, inhibitory synaptic plasticity, has been observed across different brain regions. Computationally, however, inhibitory synaptic plasticity models often lead to a strong suppression of neuronal diversity. Here, we propose a model of inhibitory synaptic plasticity in which synaptic updates depend on presynaptic spike arrival and postsynaptic membrane voltage. Our plasticity rule regulates the network activity by setting a target value for the postsynaptic membrane potential over a long timescale. In a feedforward network, we show that our voltage-dependent inhibitory synaptic plasticity (vISP) model regulates the excitatory/inhibitory ratio while allowing for a broad range of postsynaptic firing rates and thus network diversity. In a feedforward network in which excitatory and inhibitory neurons receive correlated input, our plasticity model allows for the development of co-tuned excitation and inhibition, in agreement with recordings in rat auditory cortex. In recurrent networks, our model supports memory formation and retrieval while allowing for the development of heterogeneous neuronal activity. Finally, we implement our vISP rule in a model of the hippocampal CA1 region whose pyramidal cell excitability differs across cells. This model accounts for the experimentally observed variability in pyramidal cell features such as the number of place fields, the fields sizes, and the portion of the environment covered by each cell. Importantly, our model supports a combination of sparse and dense coding in the hippocampus. Therefore, our voltage-dependent inhibitory plasticity model accounts for network homeostasis while allowing for diverse neuronal dynamics observed across brain regions.

scholarly journals Regulation of memory storage through epigenetic alterations: a new role for RNA

2018 ◽  
Vol 40 (5) ◽  
pp. 12-15
Author(s):  
Alexis Bédécarrats ◽  
David L. Glanzman
Keyword(s):  
Synaptic Plasticity ◽  
Significant Loss ◽  
Memory Storage ◽  
Long Term Memory ◽  
Plasticity Model ◽  
Epigenetic Alterations ◽  
Physical Changes ◽  
Ramón Y Cajal

A fundamental assumption in modern psychology and neuroscience is that memory is stored as physical changes in the brain. More than a century ago, the famous neuroanatomist Ramón Y Cajal (see the article entitled “Santiago Ramón y Cajal, the ultimate scientist?” in this issue of The Biochemist) postulated that changes in the strength of synaptic connections between neurons were the physical substrate for memory. Extensive experimental evidence has since established the dominance of this connectionist view, referred to as the “synaptic plasticity” model. However, although the synaptic plasticity model broadly accords with the results of neurobiological studies of learning and memory, it does not fully account for the extraordinary resilience of memory despite the significant loss of synapses during such phenomena as development, trauma and ageing. Here, we will focus on the newly discovered role of small non-coding RNAs (ncRNAs) as potential master regulators of learning-induced epigenesis, neuronal plasticity and, ultimately, memory. In support of this idea, recent data from our lab indicate that RNA can promote the transfer of long-term memory from a trained to an untrained (naïve) animal.

The collective bursting dynamics in a modular neuronal network with synaptic plasticity

2017 ◽  
Vol 89 (4) ◽  
pp. 2593-2602 ◽  
Author(s):  
Xiao Li Yang ◽  
Jia Yi Wang ◽  
Zhong Kui Sun
Keyword(s):  
Synaptic Plasticity ◽  
Neuronal Network ◽  
Modular Neuronal Network
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