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Neuromorphic hardware databases for exploring structure-function relationships in the brain.

C Breslin1, A O'Lenskie

  • 1Department of Computing Science and Mathematics, University of Stirling, Stirling FK9 4LA, UK.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|September 8, 2001
PubMed
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Neuromorphic hardware uses custom silicon chips to mimic biological neurons and networks, enabling the study of structure-function relationships. A proposed database will facilitate the reuse and fabrication of these bio-inspired circuits for neuroscience research.

Area of Science:

  • Neuroscience
  • Computer Engineering
  • Materials Science

Background:

  • Neuromorphic engineering synthesizes biologically inspired systems, such as neurons and neural networks, using custom-designed integrated circuits (silicon chips).
  • These neuromorphic hardware systems can be built using digital, analog, or mixed-signal circuitry, with current implementations often utilizing analog complementary metal-oxide-semiconductor technology.
  • The design of neuromorphic hardware involves choices in the level of biological correspondence, from ion channels to whole neuron models like Hodgkin-Huxley or integrate-and-fire neurons.

Purpose of the Study:

  • To outline a framework for constructing a neuromorphic hardware database.
  • To support the exploration of structure-function relationships in biological systems using neuromorphic hardware.
  • To provide a valuable resource for the neuromorphic engineering community and the broader neuroscience field.

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Main Methods:

  • Implementation of neurons and networks of neurons using custom-designed integrated circuits.
  • Exploration of structure-function relationships, including correlation detection and direction selectivity.
  • Design of silicon neurons with dendritic, somatic, and axonal structures, incorporating ionic, synaptic, and morphological properties.

Main Results:

  • Neuromorphic hardware can be constructed at various levels of biological correspondence, from ion channels to complex neuronal models.
  • The intrinsic properties of materials can be leveraged to create neuronal membrane properties in silicon neurons.
  • Existing neuromorphic hardware has been used to explore specific structure-function relationships.

Conclusions:

  • A database for neuromorphic hardware is essential for design reuse, re-fabrication, and to support analogue designers.
  • Such a database can house designs of silicon neurons replicating specific neuronal types and morphologies, potentially enabling automated translation from morphometric data.
  • Neuromorphic hardware databases will contribute to interfacing digital systems with the analogue environment, neural-silicon interfaces, prosthetics, and potentially accelerate design discovery through evolving circuits.