A Look at Spikey — Heidelberg University - Neuromorphic Chip

The Spikey chip is an accelerated spiking neuromorphic system integrating 384 integrate-and-fire neurons, 98k plastic synapses, and event routing. It enables fast emulation of complex neural dynamics and exploration of STDP-type synaptic plasticity.

Developed By:

Heidelberg University

The Spikey accelerated neuromorphic system is an integrated circuit architecture for emulating biologically-inspired spiking neural networks.
It was developed by researchers at the Heidelberg University.
Key features of the Spikey system include:

System Architecture

Single-chip ASIC integrating a custom analog core with 384 neuron circuits, 98k plastic synapses, analog parameter storage, and an event routing network
Synapses support STDP-type long-term and STP-type short-term plasticity.

Neural and Synapse Circuits

Implements the Leaky Integrate-and-Fire (LIF) neuron model with individually configurable model parameters
On-chip synapse correlation and plasticity measurement enables programmable spike-timing dependent plasticity

Applications and Experiments

Accelerated (50,000–100,000-fold compared to biological real time) emulation of complex spiking neuron dynamics
Exploration of synaptic plasticity models and critical network dynamics at biological timescales

The accelerated operation and flexible architecture facilitate applications in computational neuroscience research.

Date	Title	Authors	Venue/Source
February 2013	Six networks on a universal neuromorphic computing substrate	Thomas Pfeil, Andreas Grübl, Sebastian Jeltsch, Eric Müller, Paul Müller, Mihai A. Petrovici, Michael Schmuker, Daniel Brüderle, Johannes Schemmel, Karlheinz Meier	Frontiers in Neuroscience (Neuromorphic Engineering)
July 2012	Is a 4-bit synaptic weight resolution enough? – constraints on enabling spike-timing dependent plasticity in neuromorphic hardware	Thomas Pfeil, Tobias C. Potjans, Sven Schrader, Wiebke Potjans, Johannes Schemmel, Markus Diesmann, Karlheinz Meier	Frontiers in Neuroscience (Neuromorphic Engineering)
June 2009	Establishing a Novel Modeling Tool: a Python-based Interface for a Neuromorphic Hardware System	Daniel Brüderle, Eric Müller, Andrew Davison, Eilif Muller, Johannes Schemmel, Karlheinz Meier	Frontiers Neuroinformatics
June 2007	Modeling Synaptic Plasticity within Networks of Highly Accelerated I&F Neurons	Johannes Schemmel, Daniel Bruderle, Karlheinz Meier, Boris Ostendorf	2007 IEEE International Symposium on Circuits and Systems (ISCAS)
July 2006	Implementing Synaptic Plasticity in a VLSI Spiking Neural Network Model	Johannes Schemmel, Andreas Grübl, Karlheinz Meier, Eilif Mueller	2006 IEEE International Joint Conference on Neural Network (IJCNN)

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