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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Toward a Biologically Plausible SNN-Based Associative Memory with Context-Dependent Hebbian Connectivity.

S Yu Makovkin1, S Yu Gordleeva2,3,4, I A Kastalskiy5,6

  • 1Department of Applied Mathematics, Institute of Information Technology, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, Nizhny Novgorod 603022, Russia.

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We developed an energy-efficient spiking neural network for associative memory using Hebbian learning. This model uses synchronized neuron oscillations to recognize binary images, paving the way for advanced AI hardware.

Keywords:
Hebbian connectivityHodgkin–Huxley–Mainen neuronSpiking neural networkpattern retrievalphase locking

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Neurocomputing

Background:

  • Associative memory is crucial for cognitive functions.
  • Existing models often lack energy efficiency.
  • Spiking neural networks offer a biologically plausible and potentially efficient alternative.

Purpose of the Study:

  • To propose a novel spiking neural network model for energy-efficient associative memory.
  • To implement Hebbian learning for information storage and retrieval.
  • To explore context-dependent signal processing for pattern recognition.

Main Methods:

  • A three-layer spiking neural network using Hodgkin-Huxley-Mainen neurons.
  • Hebbian learning implemented via a symmetric connectivity matrix.
  • Binary image encoding using in-phase/anti-phase oscillations and phase-locking for synchronization.
  • Interneurons for context-dependent filtering of synaptic pathways.

Main Results:

  • Demonstrated information pattern retrieval via stimulus response.
  • Achieved cluster synchronization in input and output layers through phase-locking.
  • Showcased context-dependent engagement of synaptic connections for recognition.
  • Investigated oscillation phase stability for direct and inverse image recognition.

Conclusions:

  • The proposed model offers an energy-efficient approach to associative memory.
  • Context-dependent processing enhances recognition capabilities.
  • The model shows potential for analog hardware implementation in neurocomputing and AI.