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Neural Information Processing and Computations of Two-Input Synapses.

Soon Ho Kim1, Junhyuk Woo2, Kiri Choi3

  • 1Laboratory of Computational Neurophysics, Convergence Research Center for Brain Science, Brain Science Institute, Korea Institute of Science and Technology, Seoul 02792, South Korea soonho.eric.kim@gmail.com.

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Biological neurons perform nonlinear computations, unlike artificial neural networks. Combining linear and nonlinear models enhances artificial intelligence performance on tasks like fashion-MNIST, advancing brain-inspired computing.

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence algorithms
  • Neuromorphic systems

Background:

  • Artificial neural networks commonly use linear integration of synaptic inputs.
  • Biological neurons exhibit nonlinear computations through homo- and heterosynaptic mechanisms.
  • Nonlinear neuronal computation is crucial for complex neural circuit information processing.

Purpose of the Study:

  • To characterize the dynamics and coding properties of neuron models with synaptic transmissions from two hidden states.
  • To investigate the influence of synaptic interactions and hidden state coherence on neuronal information processing.
  • To demonstrate the mapping of neuronal information processing to logical operations like XOR, AND, and OR.

Main Methods:

  • Characterization of neuron model dynamics and coding properties under dual-state synaptic input.
  • Analysis of cooperative and competitive synaptic interactions and hidden state coherence effects.
  • Mapping two-input synaptic transmission processing to Boolean logic operations.

Main Results:

  • Neuronal information processing is modulated by synaptic interactions and hidden state coherence.
  • Two-input synaptic transmission can implement XOR, AND, and OR logical operations.
  • Hybrid networks of linear and nonlinear neuron models outperform single-type models on the fashion-MNIST test.

Conclusions:

  • Nonlinear neuronal computation is vital for advanced information processing.
  • A computational framework for evaluating neuron and synapse models is presented.
  • Findings support the design of brain-inspired AI algorithms and neuromorphic systems.