Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

@article{Broccard2017NeuromorphicNI,
  title={Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems},
  author={Fr{\'e}d{\'e}ric D. Broccard and Siddharth Joshi and Jun Wang and Gert Cauwenberghs},
  journal={Journal of Neural Engineering},
  year={2017},
  volume={14}
}
Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement… 

Figures and Tables from this paper

Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain

Some of the most significant neuromorphic spiking emulators are described, the different architectures and approaches used by them are compared, their advantages and drawbacks are illustrated, and the capabilities that each can deliver to neural modelers are highlighted.

A Biohybrid Setup for Coupling Biological and Neuromorphic Neural Networks

This work presents a biohybrid experimental setting, where the activity of a biological neural network is coupled to a biomimetic hardware network, and demonstrates the feasibility to functionally couple the two networks and to implement control circuits to modify the biohybrids activity.

Neuromorphic synapses with reconfigurable voltage-gated dynamics for biohybrid neural circuits

A biophysical model of a chemical synapse with reconfigurable pre- synaptic and post-synaptic voltage-gated dynamics implemented on a neuromorphic VLSI chip is described and its versatility is evaluated with measurements from the chip reproducing response characteristics from ionotropic inhibitory GABAa and excitatory NMDA synapses.

Assimilation of Biophysical Neuronal Dynamics in Neuromorphic VLSI

A set of procedures assimilating and emulating neurobiological data on a neuromorphic very large scale integrated (VLSI) circuit to enable the use of NeuroDyn as a tool to probe electrical and molecular properties of functional neural circuits.

Optimized Real-Time Biomimetic Neural Network on FPGA for Bio-hybridization

A different approach to optimize the Hodgkin-Huxley equations adapted for Field Programmable Gate Array (FPGA) implementation to create a neuromorphic system which can be used for the simulation of bio-hybridization and for the study of neurological disorders or the advanced research on neuroprosthesis to regain lost function.

A neuroprosthetic system to restore neuronal communication in modular networks

This work demonstrates the first exploitation of a neuromorphic prosthesis to restore bidirectional interactions between two neuronal populations, even when one is damaged or completely missing, and employs this prosthesis for two specific applications with future clinical implications.

Energy efficiency in adaptive neural circuits

  • G. Cauwenberghs
  • Computer Science
    2017 Fifth Berkeley Symposium on Energy Efficient Electronic Systems & Steep Transistors Workshop (E3S)
  • 2017
Recent progress in neuromorphic computing efforts are surveyed towards 10–100x improvement in energy efficiency over the most advanced special-purpose cognitive computing platforms available today, through highly optimized neuromorphic integrated circuits.

References

SHOWING 1-10 OF 209 REFERENCES

Neuromorphic analogue VLSI.

The significance of neuromorphic systems is that they offer a method of exploring neural computation in a medium whose physical behavior is analogous to that of biological nervous systems and that operates in real time irrespective of size.

Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems

This work reviews neuromorphic circuits for emulating neural and synaptic dynamics in real time and discusses the role of biophysically realistic temporal dynamics in hardware neural processing architectures; it is argued how the circuits and networks presented represent a useful set of components for efficiently and elegantly implementing neuromorphic cognition.

A neuromorphic network for generic multivariate data classification

This work makes use of neuromorphic hardware—electronic versions of neurons and synapses on a microchip—to implement a neural network inspired by the sensory processing architecture of the nervous system of insects, and demonstrates that this neuromorphic network achieves classification of generic multidimensional data—a widespread problem with many technical applications.

Six Networks on a Universal Neuromorphic Computing Substrate

This study presents a highly configurable neuromorphic computing substrate and uses it for emulating several types of neural networks, including a mixed-signal chip, which has been explicitly designed as a universal neural network emulator.

Artificial Cognitive Systems: From VLSI Networks of Spiking Neurons to Neuromorphic Cognition

It is shown how VLSI networks of spiking neurons with spike-based plasticity mechanisms and soft winner-take-all architectures represent important building blocks useful for implementing artificial neural systems able to exhibit basic cognitive abilities.

Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations

The design of Neurogrid, a neuromorphic system for simulating large-scale neural models in real time, is described-for the first time-using 16 Neurocores integrated on a board that consumes three watts.

Tunable Low Energy, Compact and High Performance Neuromorphic Circuit for Spike-Based Synaptic Plasticity

A new accelerated-time circuit that has several advantages over its previous neuromorphic counterparts in terms of compactness, power consumption, and capability to mimic the outcomes of biological experiments, which makes the proposed design an ideal circuit for use in large scale SNNs.

Energy-Efficient Neuromorphic Classifiers

This work provides a set of general prescriptions to enable the practical implementation of neural architectures that compete with state-of-the-art classifiers that are efficiently used in real-world applications and has significant advantages over conventional digital devices when energy consumption is considered.

A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses

This paper presents a full-custom mixed-signal VLSI device with neuromorphic learning circuits that emulate the biophysics of real spiking neurons and dynamic synapses for exploring the properties of computational neuroscience models and for building brain-inspired computing systems.
...