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

@article{Casal2020SoftwiredLM,
  title={Soft-wired long-term memory in a natural recurrent neuronal network},
  author={Miguel A. Casal and Santiago Galella and {\'O}scar Vilarroya and Jordi Garc{\'i}a-Ojalvo},
  journal={bioRxiv},
  year={2020}
}
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… 
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References

SHOWING 1-10 OF 39 REFERENCES

Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations

A new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks, based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry.

Collective stochastic coherence in recurrent neuronal networks

This work shows that a combination of stochastic recurrence-based initiation with deterministic refractoriness in an excitable network can reconcile these two features, leading to maximum collective coherence for an intermediate noise level, and construe the high regularity observed experimentally in the brain as an instance of collective Stochastic coherence.

Neural networks and physical systems with emergent collective computational abilities.

  • J. Hopfield
  • Computer Science
    Proceedings of the National Academy of Sciences of the United States of America
  • 1982
A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.

Mesoscopic Segregation of Excitation and Inhibition in a Brain Network Model

A coupled neural mass model is used to study computationally the dynamics of a network of cortical macrocolumns operating in a partially synchronized, irregular regime and shows that the connector hubs are preferentially inhibitory, the more so the larger the node's connectivity.

Lapicque’s introduction of the integrate-and-fire model neuron (1907)

Cellular and network mechanisms of rhythmic recurrent activity in neocortex

The results demonstrate that the cerebral cortex generates an ‘up’ or depolarized state through recurrent excitation that is regulated by inhibitory networks, thereby allowing local cortical circuits to enter into temporarily activated and self-maintained excitatory states.

Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity

The hypothesis that the temporal variability in the firing of a neuron results from an approximate balance between its excitatory and inhibitory inputs was investigated theoretically.

Special issue on echo state networks and liquid state machines

An experimental unification of reservoir computing methods

Evoked brain responses are generated by feedback loops

It is shown that evoked brain responses are generated by recurrent dynamics in cortical networks, and late components of event-related responses are mediated by backward connections, a generic feature of brain responses to changes in the sensorium and a key architectural component of functional anatomy.