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Unsupervised Learning of Persistent and Sequential Activity.

Ulises Pereira1, Nicolas Brunel1,2,3,4

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Summary
This summary is machine-generated.

Brain networks can learn persistent activity (PA) and sequential activity (SA) through synaptic plasticity. A homeostatic plasticity mechanism stabilizes this learning, enabling networks to flexibly adapt to different input statistics for memory tasks.

Keywords:
Hebbian plasticityhomeostatic plasticitypersistent activitysequential activitysynaptic plasticityunsupervised learning

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Area of Science:

  • Computational Neuroscience
  • Neural Dynamics
  • Synaptic Plasticity

Background:

  • Brain activity during memory tasks exhibits persistent activity (PA) and sequential activity (SA).
  • Synaptic plasticity is hypothesized to enable neural networks to learn these activity patterns from input statistics.
  • Conditions for stable learning of PA and SA by a single plasticity rule remain unclear.

Purpose of the Study:

  • To investigate the conditions under which a single unsupervised learning rule can stably learn both persistent activity (PA) and sequential activity (SA).
  • To explore the role of homeostatic plasticity in stabilizing the learning of these distinct neural dynamics.
  • To determine how input statistics influence the emergence of PA versus SA in a plastic neural network.

Main Methods:

  • Characterized the bifurcation diagram of a firing rate model with excitatory and inhibitory populations.
  • Investigated an unsupervised, temporally asymmetric Hebbian plasticity rule.
  • Incorporated a generalized multiplicative homeostatic plasticity mechanism for stabilization.

Main Results:

  • A homeostatic plasticity mechanism stabilizes learning by masking and unmasking excitatory connections during stimulation and retrieval phases.
  • The network model analytically demonstrates that slow-changing stimuli promote PA, while fast-changing stimuli promote SA.
  • The proposed learning rule enables stable and flexible unsupervised learning of both PA and SA.

Conclusions:

  • A single, unsupervised learning rule combined with homeostatic plasticity can stably learn both persistent and sequential neural activity patterns.
  • The statistics of external inputs critically determine whether PA or SA emerges.
  • This model provides a framework for understanding how neural networks flexibly learn and adapt memory representations.