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The somatosensory cortex in the parietal lobes is crucial for interpreting sensory data such as touch, temperature, and proprioception. The somatosensory cortex, situated in the parietal lobes, plays a vital role in interpreting sensory information like touch, temperature, and proprioception—awareness of body position. This specialized brain region features an organized structure wherein neurons at the top primarily process sensations originating from the lower body. In contrast, those at...
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Two-Argument Activation Functions Learn Soft XOR Operations Like Cortical Neurons.

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This study introduces biologically realistic artificial neurons with two inputs, mimicking brain complexity. These novel neurons learn faster and perform better than traditional models, enhancing artificial intelligence robustness.

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Machine learning

Background:

  • Biological neurons possess complex, nonlinear interactions within distinct compartments.
  • Artificial neural networks (ANNs) typically simplify neuronal complexity using scalar activation functions.
  • Existing ANNs lack the nuanced input processing observed in biological neurons.

Purpose of the Study:

  • To develop artificial neurons that more accurately emulate the complex, nonlinear interactions of biological neurons.
  • To investigate the impact of biologically inspired nonlinearities on ANN learning and performance.
  • To enhance the robustness of ANNs against various perturbations.

Main Methods:

  • Employed a network-in-network architecture where each artificial neuron is a shared multilayer perceptron with two inputs.
  • Learned canonical activation functions with two input arguments, analogous to basal and apical dendrites.
  • Optimized hyperparameters for networks utilizing these novel nonlinearities.

Main Results:

  • The learned nonlinearities frequently resulted in soft XOR functions, aligning with experimental findings in human cortical neurons.
  • Networks with biologically inspired nonlinearities demonstrated faster learning and superior performance compared to conventional Rectified Linear Unit (ReLU) nonlinearities.
  • These advanced networks exhibited increased robustness against both natural and adversarial perturbations.

Conclusions:

  • Biologically realistic artificial neurons with two-input nonlinearities offer significant advantages over traditional ANN models.
  • This approach enhances learning efficiency, performance, and robustness in artificial neural networks.
  • The findings support the potential of emulating neuronal complexity for advancing AI capabilities.