An aVLSI recurrent network of spiking neurons with reconfigurable and plastic synapses

Badoni, D.; Giulioni, Massimiliano; Dante, Vittorio; Giudice, Paolo Del

doi:10.1109/iscas.2006.1692813

Cited by 36 publications

(25 citation statements)

References 14 publications

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“…The synaptic dynamics we propose has been implemented in neuromorphic analog VLSI (very large scale integration) (Mitra, Fusi, & Indiveri, 2006;Badoni, Giulioni, Dante, & Del Giudice, 2006;Indiveri & Fusi, 2007). A similar model has been introduced in Fusi et al (2000) and the other components required to implement massively parallel networks of integrate-and-fire neurons and synapses with the new synaptic dynamics have been previously realized in VLSI in (Indiveri, 2000(Indiveri, , 2001(Indiveri, , 2002Chicca & Fusi, 2001).…”

Section: Parameter Tuningmentioning

confidence: 99%

“…In particular, it is retained indefinitely if no other presynaptic spikes arrive. All the hardware implementations require negligible power consumption to stay in one of the two stable states (Fusi et al, 2000;Indiveri, 2000Indiveri, , 2001Indiveri, , 2002Mitra et al, 2006;Badoni, Giulioni, Dante, & Del Giudice, 2006) and the bistable circuitry does not require nonstandard technology or high voltage, as for the floating gates (Diorio, Hasler, Minch, & Mead, 1996).…”

Section: Parameter Tuningmentioning

confidence: 99%

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Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics

2007

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We present a model of spike-driven synaptic plasticity inspired by experimental observations and motivated by the desire to build an electronic hardware device that can learn to classify complex stimuli in a semisupervised fashion. During training, patterns of activity are sequentially imposed on the input neurons, and an additional instructor signal drives the output neurons toward the desired activity. The network is made of integrate-and-fire neurons with constant leak and a floor. The synapses are bistable, and they are modified by the arrival of presynaptic spikes. The sign of the change is determined by both the depolarization and the state of a variable that integrates the postsynaptic action potentials. Following the training phase, the instructor signal is removed, and the output neurons are driven purely by the activity of the input neurons weighted by the plastic synapses. In the absence of stimulation, the synapses preserve their internal state indefinitely. Memories are also very robust to the disruptive action of spontaneous activity. A network of 2000 input neurons is shown to be able to classify correctly a large number (thousands) of highly overlapping patterns (300 classes of preprocessed Latex characters, 30 patterns per class, and a subset of the NIST characters data set) and to generalize with performances that are better than or comparable to those of artificial neural networks. Finally we show that the synaptic dynamics is compatible with many of the experimental observations on the induction of long-term modifications (spike-timing-dependent plasticity and its dependence on both the postsynaptic depolarization and the frequency of pre- and postsynaptic neurons).

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Section: Parameter Tuningmentioning

confidence: 99%

Section: Parameter Tuningmentioning

confidence: 99%

Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics

2007

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“…VLSI implementations of spike-based learning systems have been previously proposed [15]- [21], but they either lack the combined memory encoding and memory preservation features of the spike-based plasticity mechanism implemented in this device, or cannot cope with highly correlated patterns as efficiently as the system described here. A recent alternative VLSI implementation of the same plasticity mechanism described in this paper has been proposed in [22]. However, the circuits in that device are significantly larger than the ones used here, as they comprise additional digital modules for configuring the synaptic matrix inside the chip.…”

Section: Hardware Implementationmentioning

confidence: 99%

“…However, the circuits in that device are significantly larger than the ones used here, as they comprise additional digital modules for configuring the synaptic matrix inside the chip. In addition the synapses used in [22] lack the biologically realistic temporal dynamics present on the implementation proposed here.…”

Section: Hardware Implementationmentioning

confidence: 99%

Real-Time Classification of Complex Patterns Using Spike-Based Learning in Neuromorphic VLSI

Mitra

Fusi

Indiveri

2009

IEEE Trans. Biomed. Circuits Syst.

193

152

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Real-time classification of patterns of spike trains is a difficult computational problem that both natural and artificial networks of spiking neurons are confronted with. The solution to this problem not only could contribute to understanding the fundamental mechanisms of computation used in the biological brain, but could also lead to efficient hardware implementations of a wide range of applications ranging from autonomous sensory-motor systems to brain-machine interfaces. Here we demonstrate real-time classification of complex patterns of mean firing rates, using a VLSI network of spiking neurons and dynamic synapses which implement a robust spike-driven plasticity mechanism. The learning rule implemented is a supervised one: a teacher signal provides the output neuron with an extra input spike-train during training, in parallel to the spike-trains that represent the input pattern. The teacher signal simply indicates if the neuron should respond to the input pattern with a high rate or with a low one. The learning mechanism modifies the synaptic weights only as long as the current generated by all the stimulated plastic synapses does not match the output desired by the teacher, as in the perceptron learning rule. We describe the implementation of this learning mechanism and present experimental data that demonstrate how the VLSI neural network can learn to classify patterns of neural activities, also in the case in which they are highly correlated. Abstract-Real-time classification of patterns of spike trains is a difficult computational problem that both natural and artificial networks of spiking neurons are confronted with. The solution to this problem not only could contribute to understanding the fundamental mechanisms of computation used in the biological brain, but could also lead to efficient hardware implementations of a wide range of applications ranging from autonomous sensory-motor systems to brain-machine interfaces. Here we demonstrate real-time classification of complex patterns of mean firing rates, using a VLSI network of spiking neurons and dynamic synapses which implement a robust spike-driven plasticity mechanism. The learning rule implemented is a supervised one: a teacher signal provides the output neuron with an extra input spike-train during training, in parallel to the spike-trains that represent the input pattern. The teacher signal simply indicates if the neuron should respond to the input pattern with a high rate or with a low one. The learning mechanism modifies the synaptic weights only as long as the current generated by all the stimulated plastic synapses does not match the output desired by the teacher, as in the perceptron learning rule. We describe the implementation of this learning mechanism and present experimental data that demonstrate how the VLSI neural network can learn to classify patterns of neural activities, also in the case in which they are highly correlated.

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“…In computational neuroscience research various customized computing systems for the simulation of neuronal networks are under development, such as Facets/ BrainScales/ HBP [1], Neuro-grid [2], dedicated aVLSI computing chips [3], or the SpiNNaker spiking network computing system [4]. All such hardware resembles brain style information processing, which in contrast to traditional general purpose computers offers various advantages, e.g.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Real-Time Interface Board for Closed-Loop Robotic Tasks on the SpiNNaker Neural Computing System

Denk

Llobet-Blandino

Galluppi

et al. 2013

Artificial Neural Networks and Machine Learning – ICANN 2013

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Abstract. Various custom hardware solutions for simulation of neural circuitry have recently been developed, each focusing on particular aspects such as low power operation, high computation speed, or biologically detailed simulations. The SpiNNaker computing system has been developed to simulate large spiking neural circuits in real-time in a network of parallel operating microcontrollers, interconnected by a high-speed asynchronous interface. A potential application area is autonomous mobile robotics, which would tremendously benefit from on-board simulations of networks of tens of thousands of spiking neurons in real-time. Currently, the SpiNNaker hardware circuit boards provide a single Ethernet interface for booting, debug, and input and output of data, which results in a severe bottleneck for sensory perception and motor control signals. This paper describes a small and flexible real-time I/O-hardware interface to connect external devices such as robotic sensors and actuators directly to the fast asynchronous internal communication infrastructure of the SpiNNaker neural computing system. We evaluate performance in terms of package throughput and present a simple application demonstration of a closed loop mobile robot interpreting visual data to approach the most salient stimulus.

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An aVLSI recurrent network of spiking neurons with reconfigurable and plastic synapses

Cited by 36 publications

References 14 publications

Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics

Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics

Real-Time Classification of Complex Patterns Using Spike-Based Learning in Neuromorphic VLSI

Real-Time Interface Board for Closed-Loop Robotic Tasks on the SpiNNaker Neural Computing System

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