OBJECTIVE: Although biological synapses express a large variety of receptors in neuronal membranes, the current hardware implementation of neuromorphic synapses often rely on simple models ignoring the heterogeneity of synaptic transmission. Our objective is to emulate different types of synapses with distinct properties. METHODS: Conductance-based chemical and electrical synapses were implemented between silicon neurons on a fully programmable and reconfigurable, biophysically realistic neuromorphic VLSI chip. Different synaptic properties were achieved by configuring on-chip digital parameters for the conductances, reversal potentials, and voltage dependence of the channel kinetics. The measured I-V characteristics of the artificial synapses were compared with biological data. RESULTS: We reproduced the response properties of five different types of chemical synapses, including both excitatory ( AMPA, NMDA) and inhibitory ( GABAA, GABAC, glycine) ionotropic receptors. In addition, electrical synapses were implemented in a small network of four silicon neurons. CONCLUSION: Our work extends the repertoire of synapse types between silicon neurons, providing greater flexibility for the design and implementation of biologically realistic neural networks on neuromorphic chips. SIGNIFICANCE: A higher synaptic heterogeneity in neuromorphic chips is relevant for the hardware implementation of energy-efficient population codes as well as for dynamic clamp applications where neural models are implemented in neuromorphic VLSI hardware.
OBJECTIVE: Although biological synapses express a large variety of receptors in neuronal membranes, the current hardware implementation of neuromorphic synapses often rely on simple models ignoring the heterogeneity of synaptic transmission. Our objective is to emulate different types of synapses with distinct properties. METHODS: Conductance-based chemical and electrical synapses were implemented between silicon neurons on a fully programmable and reconfigurable, biophysically realistic neuromorphic VLSI chip. Different synaptic properties were achieved by configuring on-chip digital parameters for the conductances, reversal potentials, and voltage dependence of the channel kinetics. The measured I-V characteristics of the artificial synapses were compared with biological data. RESULTS: We reproduced the response properties of five different types of chemical synapses, including both excitatory ( AMPA, NMDA) and inhibitory ( GABAA, GABAC, glycine) ionotropic receptors. In addition, electrical synapses were implemented in a small network of four silicon neurons. CONCLUSION: Our work extends the repertoire of synapse types between silicon neurons, providing greater flexibility for the design and implementation of biologically realistic neural networks on neuromorphic chips. SIGNIFICANCE: A higher synaptic heterogeneity in neuromorphic chips is relevant for the hardware implementation of energy-efficient population codes as well as for dynamic clamp applications where neural models are implemented in neuromorphic VLSI hardware.