Neurobiological processing systems are remarkable computational devices. They use slow, stochastic, and inhomogeneous computing elements and yet they outperform today’s most powerful computers at tasks such as vision, audition, and motor control, tasks that we perform nearly every moment that we are awake without much conscious thought or concern. Despite the vast amount of resources dedicated to the research and development of computing, information, and communication technologies, today’s fastest and largest computers are still not able to match biological systems at robustly accomplishing real-worl

Horiuchi, Timothy K.

Indiveri, Giacomo

Frontiers in Neuroscience

English

PubMed

Giacomo eIndiveri

Timothy K Horiuchi

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Frontiers in Neuromorphic Engineering

Indiveri, G

Horiuchi, T K

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Frontiers in neuromorphic engineering

Giacomo Indiveri

Timothy K. Horiuchi

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Frontiers in neuromorphic engineering
Giacomo Indiveri1* and Timothy K. Horiuchi2
1 Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
2 Department of Electrical and Computer Engineering, Institute for Systems Research, University of Maryland, College Park, MD, USA
*Correspondence: giacomo@ini.phys.ethz.ch
Neurobiological processing systems are 
remarkable computational devices. They 
use slow, stochastic, and inhomogeneous 
computing elements and yet they outper-
form today’s most powerful computers at 
tasks such as vision, audition, and motor 
control, tasks that we perform nearly every 
moment that we are awake without much 
conscious thought or concern. Despite the 
vast amount of resources dedicated to the 
research and development of computing, 
information, and communication technol-
ogies, today’s fastest and largest computers 
are still not able to match biological sys-
tems at robustly accomplishing real-world 
tasks. While the specific algorithms and 
representations that biological brains use 
are still largely unknown, it is clear that 
instead of Boolean logic, precise digital 
representations, and synchronous opera-
tions, nervous systems use hybrid analog/
digital components, distributed represen-
tations, massively parallel mechanisms, 
combine communications with memory 
and computation, and make extensive use 
of adaptation, self-organization, and learn-
ing. On the other hand, as with many suc-
cessful man-made systems, it is clear that 
biological brains have been co-designed 
with the body to operate under a specific 
range of conditions and assumptions about 
the world.
Understanding the computational prin-
ciples used by the brain and how they are 
physically embodied is crucial for develop-
ing novel computing paradigms and guid-
ing a new generation of technologies that 
can combine the strengths of industrial-
scale electronics with the computational 
performance of brains.
Neuromorphic eNgiNeeriNg
While the history of implementing electronic 
models of neural circuits extends back to the 
construction of perceptrons (Rosenblatt, 
1958) and retinas (Fukushima et al., 1970), 
the modern wave of research utilizing VLSI 
technology and  emphasizing the non- linear 
current characteristics of the transistor 
began in the mid 1980s with the collabo-
ration that sprung up between prominent 
scientists Max Delbrück, John Hopfield, 
Carver Mead, and Richard Feynman (Hey, 
1999). Inspired by graded synaptic trans-
mission in the retina, Mead sought to use 
the graded (analog) properties of transis-
tors, rather than simply operating them as 
on–off (digital) switches. He showed that 
analog neuromorphic circuits share many 
common physical properties with protein 
channels in neurons (Mead, 1989). As a con-
sequence, these types of circuits require far 
fewer transistors than digital approaches to 
emulating neural systems.
Through the Physics of Computation 
course at Caltech (led by Carver Mead, John 
Hopfield, and Richard Feynman), Mead 
(1989)’s textbook Analog VLSI and Neural 
Systems, and the creation of the Telluride 
Neuromorphic Engineering Workshop, the 
field of Neuromorphic Engineering was 
established. Prominent in the early expansion 
of the field were scientists and engineers such 
as Christof Koch, Terry Sejnowski, Rodney 
Douglas, Andreas Andreou, Paul Mueller, Jan 
van der Spiegel, and Eric Vittoz, training a 
generation of cross-disciplinary students.
It has been argued that neuromorphic 
circuits are ideal for developing a new gen-
eration of computing technologies that 
use the same organizing principles of the 
biological nervous system (Douglas et al., 
1995; Boahen, 2005; Sarpeshkar, 2006). In 
addition to the computations of a single 
neuron, many neuromorphic circuits also 
utilize spiking representations for commu-
nication, learning and memory, and com-
putation. The use of asynchronous spike- or 
digital event-based representations in elec-
tronic systems can be energy-efficient and 
fault-tolerant, making them ideal for build-
ing modular systems and creating complex 
hierarchies of computation.
The most successful neuromorphic sys-
tems to date have been single chip devices 
that emulate peripheral sensory  transduction 
such as silicon retinas, visual motion sen-
sors, and silicon cochleas for a wide vari-
ety of applications. In recent years, many 
larger multi-chip neuromorphic systems 
have begun to emerge that have raised new 
issues and challenges. These systems typically 
comprise one or more neuromorphic sen-
sors, interfaced to general-purpose neural 
network chips using spiking silicon neurons 
and dynamic synapses.
The method used to transmit spikes 
across chip boundaries in these systems is 
based on the address-event representation 
(AER; Mahowald, 1994). It is an asynchro-
nous digital communication protocol that 
sends the address of the neuron that emitted 
the event in real-time or close to real-time. 
The information being transmitted may 
be analog or digital, but must be commu-
nicated via spikes, thus raising the critical 
and exciting issue of signal encoding that 
is currently a very active topic in neurosci-
ence. Signals can be encoded in the mean 
frequency (rate) of spikes, in their precise 
timing with respect to a time reference, or 
in the population response. In fact, multiple 
signals can be simultaneously encoded in a 
single spike train. Once on a digital bus, the 
address-events can be remapped to multiple 
destinations using commercially available 
synchronous or custom asynchronous pro-
cessing. Digital AER infrastructures allow us 
to construct large multi-chip networks with 
nearly arbitrary connectivity and to dynami-
cally reconfigure the network topology for 
experimentation. By using analog circuits 
for local computations on-chip and digi-
tal circuits for long-distance communica-
tion (off-chip), neuromorphic systems can 
exploit the best of both worlds.
Another distinguishing feature of neuro-
morphic engineering has been the integra-
tion of fine-grained synaptic modification 
mechanisms that both enable these net-
works to change their behavior with experi-
ence (as is ubiquitous in biological nervous 
systems) and to implicitly overcome the 
inherent device parameter variability 
www.frontiersin.org  October 2011 | Volume 5 | Article 118 | 1
Specialty Grand challenGe article
published: 10 October 2011
doi: 10.3389/fnins.2011.00118
•	 neural computation, involving studies of 
spiking winner-take-all networks, attrac-
tor networks, mean-field theory, spike-
based learning mechanisms, probabilistic 
graphical models, cortical development, 
and self-constructing principles;
•	 biologically plausible cognitive architec-
tures for studying attention, working 
memory, state-dependent computa-
tion, action selection mechanisms, 
planning, and multi-agent interaction.
Through this journal, we intend to 
encourage the presentation of these diverse 
perspectives, technical approaches, and 
goals, to facilitate the development of neu-
romorphic cognitive systems, and reach 
new frontiers in neuromorphic engineering.
refereNces
Boahen, K. A. (2005). Neuromorphic microchips. Sci. 
Am. 292, 56–63.
Douglas, R. J., Mahowald, M. A., and Mead, C. (1995). 
Neuromorphic analogue VLSI. Annu. Rev. Neurosci. 
18, 255–281.
Fukushima, K., Yamaguchi, Y., Yasuda, M., and Nagata, S. 
(1970). An electronic model of the retina. Proc. IEEE 
58, 1950–1951.
Hey, T. (1999). Richard Feynman and computation. 
Contemp. Phy. 40, 257–265.
Mahowald, M. (1994). An Analog VLSI System for 
Stereoscopic Vision. Boston, MA: Kluwer.
Mead, C. A. (1989). Analog VLSI and Neural Systems. 
Reading, MA: Addison-Wesley.
Rosenblatt, F. (1958). The perceptron: a probabilistic 
model for information storage and organization in 
the brain. Psychol. Rev. 65, 386–408.
Sarpeshkar, R. (2006). Brain power – borrowing from 
biology makes for low power computing – bionic ear. 
IEEE Spectr. 43, 24–29.
Received: 19 July 2011; accepted: 14 September 2011; pub-
lished online: 10 October 2011.
Citation: Indiveri G and Horiuchi TK (2011) Frontiers 
in neuromorphic engineering. Front. Neurosci. 5:118. doi: 
10.3389/fnins.2011.00118
This article was submitted to Frontiers in Neuromorphic 
Engineering, a specialty of Frontiers in Neuroscience.
Copyright © 2011 Indiveri and Horiuchi. This is an open-
access article subject to a non-exclusive license between the 
authors and Frontiers Media SA, which permits use, dis-
tribution and reproduction in other forums, provided the 
original authors and source are credited and other Frontiers 
conditions are complied with.
While the majority of cognitive archi-
tectures and their software implemen-
tations have avoided detailed neural 
implementations, due to limited computa-
tional power and the assumption that the 
details of single spiking neurons are not 
important at this level, a growing number 
of research groups worldwide have begun 
to consider the consequences of biologi-
cally plausible implementations at both 
the level of neural fields and single spiking 
neurons. By providing real-time spiking 
implementations of core neural circuits, 
neuromorphic engineering will play an 
important role in the development and 
fielding of biologically relevant working 
models of cognition interacting with the 
real-world.
One of the Grand Challenges of 
Neuromorphic Engineering is to 
 demonstrate cognitive systems using 
hardware neural processing architectures 
integrated with physical bodies (e.g., 
humanoid robots) that can solve every-
day tasks in real-time. To be successful 
in this ambitious endeavor, an integrated 
multi-disciplinary approach is critical that 
brings together research in:
•	 VLSI circuits and systems for imple-
menting hardware models of neural 
processing systems, mixed analog/digi-
tal asynchronous AER communication 
infrastructures, spike-based sensory–
motor systems, and event-driven pro-
cessing methods;
•	 emerging technologies including 3D 
VLSI, nanotechnologies, phase-change 
materials, and memristive devices, 
applied to the construction of low-
power neuromorphic systems;
•	 robotic platforms and control with parti-
cular focus on new actuators and mate-
rials, compliant systems, contraction 
theory and controllability of complex 
systems, and on the computational role 
of the physical body in locomotion and 
active sensing;
found in all manufacturing technologies 
whether silicon or mechanical. The most 
prominent storage mechanisms have been: 
on-chip capacitance, on-chip floating-gate 
charge storage, and off-chip AER remap-
ping of the network to either dynamically 
change the connectivity or to implement 
stochastic spike delivery. To implement bio-
logically plausible learning rules (e.g., spike-
timing dependent plasticity), many of these 
implementations also incorporate learning 
circuits directly at the synapse.
froNtiers iN Neuromorphic eNgiNeeriNg
At its heart, neuromorphic engineering is 
about the real-time interaction of the algo-
rithm with its physical implementation and 
the environment in solving tasks. This syn-
ergy is easy to appreciate at the sensory and 
motor interfaces with the world, but more 
subtle and interesting when considering 
cognitive-level tasks.
With increasing knowledge of what 
single neurons and their synapses can do 
computationally, the desire for more sophis-
ticated implementation technologies has 
grown. At present, new technologies such 
as nano-scale transistors, quantum devices, 
organic electronics, memristors, phase-
change materials, 3D integrated circuits, 
and electro-active polymers for actuation 
are all promising directions for research.
Neuromorphic engineering now aims 
to use these technologies for develop-
ing larger-scale neural processing systems 
and move from the predominantly feed- 
forward, reactive neuromorphic systems of 
the past to adaptive behaving ones that can 
be considered cognitive. For example, a key 
mechanism in cognition, selective attention, 
has long been part of the neuromorphic 
engineering toolkit, but has largely operated 
as a bottom-up process, operating on short-
term information and memory. Expanding 
its role in top-down behavior (e.g., guiding 
the learning of more abstract concepts) will 
be important for understanding and imple-
menting context-dependent behavior.
Frontiers in Neuroscience | Neuromorphic Engineering  October 2011 | Volume 5 | Article 118 | 2
Indiveri and Horiuchi Neuromorphic engineering


Neurobiological processing systems are remarkable computational devices. They use slow, stochastic, and inhomogeneous computing elements and yet they outperform today’s most powerful computers at tasks such as vision, audition, and motor control, tasks that we perform nearly every moment that we are awake without much conscious thought or concern. Despite the vast amount of resources dedicated to the research and development of computing, information, and communication technologies, today’s fastest and largest computers are still not able to match biological systems at robustly accomplishing real-world tasks. While the specific algorithms and representations that biological brains use are still largely unknown, it is clear that instead of Boolean logic, precise digital representations, and synchronous operations, nervous systems use hybrid analog/digital components, distributed representations, massively parallel mechanisms, combine communications with memory and computation, and make extensive use of adaptation, self-organization, and learning. On the other hand, as with many successful man-made systems, it is clear that biological brains have been co-designed with the body to operate under a specific range of conditions and assumptions about the world.

Understanding the computational principles used by the brain and how they are physically embodied is crucial for developing novel computing paradigms and guiding a new generation of technologies that can combine the strengths of industrial-scale electronics with the computational performance of brains.ISSN:1662-453XISSN:1662-454

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