Toward Silicon-Based Cognitive Neuromorphic ICs—A Survey <s> Information transmission <s> In this paper, we demonstrate how a particular spike-based learning rule (where exact temporal relations between input and output spikes of a spiking model neuron determine the changes of the synaptic weights) can be tuned to express rate-based classical Hebbian learning behavior (where the average input and output spike rates are sufficient to describe the synaptic changes). This shift in behavior is controlled by the input statistic and by a single time constant. The learning rule has been implemented in a neuromorphic very large scale integration (VLSI) chip as part of a neurally inspired spike signal image processing system. The latter is the result of the European Union research project Convolution AER Vision Architecture for Real-Time (CAVIAR). Since it is implemented as a spike-based learning rule (which is most convenient in the overall spike-based system), even if it is tuned to show rate behavior, no explicit long term average signals are computed on the chip. We show the rule's rate-based Hebbian learning ability in a classification task in both simulation and chip experiment, first with artificial stimuli and then with sensor input from the CAVIAR system <s> BIB001 </s> Toward Silicon-Based Cognitive Neuromorphic ICs—A Survey <s> Information transmission <s> This paper describes CAVIAR, a massively parallel hardware implementation of a spike-based sensing-processing-learning-actuating system inspired by the physiology of the nervous system. CAVIAR uses the asynchronous address-event representation (AER) communication framework and was developed in the context of a European Union funded project. It has four custom mixed-signal AER chips, five custom digital AER interface components, 45 k neurons (spiking cells), up to 5 M synapses, performs 12 G synaptic operations per second, and achieves millisecond object recognition and tracking latencies. <s> BIB002 </s> Toward Silicon-Based Cognitive Neuromorphic ICs—A Survey <s> Information transmission <s> 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. <s> BIB003 </s> Toward Silicon-Based Cognitive Neuromorphic ICs—A Survey <s> Information transmission <s> The application of biologically inspired methods in design and control has a long tradition in robotics. Unlike previous approaches in this direction, the emerging field of neurorobotics not only mimics biological mechanisms at a relatively high level of abstraction but employs highly realistic simulations of actual biological nervous systems. Even today, carrying out these simulations efficiently at appropriate timescales is challenging. Neuromorphic chip designs specially tailored to this task therefore offer an interesting perspective for neurorobotics. Unlike Von Neumann CPUs, these chips cannot be simply programmed with a standard programming language. Like real brains, their functionality is determined by the structure of neural connectivity and synaptic efficacies. Enabling higher cognitive functions for neurorobotics consequently requires the application of neurobiological learning algorithms to adjust synaptic weights in a biologically plausible way. In this paper, we therefore investigate how to program neuromorphic chips by means of learning. First, we provide an overview over selected neuromorphic chip designs and analyze them in terms of neural computation, communication systems and software infrastructure. On the theoretical side, we review neurobiological learning techniques. Based on this overview, we then examine on-die implementations of these learning algorithms on the considered neuromorphic chips. A final discussion puts the findings of this work into context and highlights how neuromorphic hardware can potentially advance the field of autonomous robot systems. The paper thus gives an in-depth overview of neuromorphic implementations of basic mechanisms of synaptic plasticity which are required to realize advanced cognitive capabilities with spiking neural networks. <s> BIB004
The number of connections between neurons in the brain is staggering. The human brain contains about 10 11 neurons, while a cortical neuron typically makes 10 4 connections with other neurons. Thus, there is a total of 10 15 point-to-point connections between neurons in the brain. Accordingly, the density of connections in large neural system implementations using analog integrated circuits comprises a major bottleneck. Although connections on an integrated circuit can be thinner than an axon, the brain uses its 3-D volume to route those connections, whereas integrated circuits and/or printed circuit boards have been limited, until recently, to a discrete number of 2-D layers. To overcome this obstacle, the basic idea of AER, shown in Figure 6 , is widely used. It practically trades in the advantage of speed of electronics for its inferior interconnection density. Instead of an individual connection between each pair of neurons, two assemblies of neurons share one digital bus. An event (i.e., an action potential from a neuron) is encoded as a digital ID/address (i.e., a number that identifies the neuron producing the action potential) and is transmitted on this time-multiplexed, digital bus. On the receiver side, this address is, then, again converted into pulses, which are distributed to the receiving neurons that are connected to the sender BIB001 . The AER event-driven data representation and communication protocol was initially studied in Mead's lab by Mahowald and Sivilotti. It is an asynchronous handshaking protocol used to transmit signals between neuromorphic systems over a common communication bus, which is shared between chips and in which addresses are explicitly transmitted. Each transmission of an address is referred to as a spike. A unit in a system triggers a request for transmission when its internal state has crossed a threshold; its address is transmitted onto a common bus once this request is granted. Arbitration circuits on the periphery of the chip ensure that the addresses are sent off sequentially. The AER handshaking protocol ensures that the sender and the receiver write and read from the bus, respectively, only when they are allowed to. The activity level of each unit is represented by the frequency at which its address is transmitted. The information being transmitted may be analog or digital, but must be communicated via spikes, thus raising the critical and exciting issue of signal encoding, which is currently a very active topic in neuroscience. Digital AER infrastructures allow construction of large multichip networks with nearly arbitrary connectivity and dynamic reconfiguration of the network topology for experimentation BIB003 . The AER protocol has been utilized in several neuromorphic systems. In cases of large neuromorphic chips, such as the ROLLS neuromorphic processor, SpiNNaker, HICANN, which are discussed in the following section, or in systems like CAVIAR BIB002 , implementation variants of the AER technique can be found BIB004 .