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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
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Neuromorphic Dynamical Synapses With Reconfigurable Voltage-Gated Kinetics.
IEEE Transactions on Bio-Medical Engineering
|October 25, 2019
Summary
Researchers developed diverse artificial synapses for neuromorphic chips, mimicking biological synapse heterogeneity. This advances biologically realistic neural networks and energy-efficient computing.
Area of Science:
- Neuroscience
- Computer Engineering
- Artificial Intelligence
Background:
- Biological synapses exhibit diverse receptor types, crucial for neural communication.
- Current neuromorphic hardware often uses simplified synaptic models, lacking biological realism.
- Synaptic heterogeneity is key for advanced neural network functionality.
Purpose of the Study:
- To emulate diverse biological synapse types with distinct properties in hardware.
- To overcome limitations of simple models in current neuromorphic synapse implementations.
Main Methods:
- Implemented conductance-based chemical and electrical synapses on a reconfigurable neuromorphic VLSI chip.
- Configured on-chip digital parameters (conductances, reversal potentials, kinetics) to achieve distinct synaptic properties.
- Validated artificial synapse behavior by comparing measured I-V characteristics with biological data.
Main Results:
- Successfully reproduced response properties of five chemical synapse types (excitatory: AMPA, NMDA; inhibitory: GABAA, GABAC, glycine).
- Implemented electrical synapses within a small network of four silicon neurons.
- Demonstrated the ability to emulate a range of biologically relevant synaptic behaviors.
Conclusions:
- Expanded the variety of synapse types implementable on silicon neurons.
- Enhanced flexibility for designing and deploying biologically realistic neural networks on neuromorphic hardware.
- Highlighted the importance of synaptic heterogeneity for energy-efficient population coding and dynamic clamp applications.
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