Focus Issue on Spike-Based Plasticity

The role of pre-synaptic DC bias is investigated in three-terminal organic neuromorphic architectures based on electrolyte-gated organic transistors—EGOTs. By means of pre-synaptic offset it is possible to finely control the number of discrete conductance states in short-term plasticity experiments, to obtain, at will, both depressive and facilitating response in the same neuromorphic device and to set the ratio between two subsequent pulses in paired-pulse experiments. The charge dynamics leading to these important features are discussed in relationship with macroscopic device figures of merit such as conductivity and transconductance, establishing a novel key enabling parameter in devising the operation of neuromorphic organic electronics.

Most efforts on spike-based learning on neuromorphic hardware focus on synaptic plasticity and do not yet exploit the potential of altering the spike-generating dynamics themselves. Biological neurons show distinct mechanisms of spike generation, which affect single-neuron and network computations. Such a variety of spiking mechanisms can only be mimicked on chips with more advanced, nonlinear single-neuron dynamics than the commonly implemented leaky integrate-and-fire neurons. Here, we demonstrate that neurons on the BrainScaleS-2 chip configured for exponential leaky integrate-and-fire dynamics can be tuned to undergo a qualitative switch in spike generation via a modulation of the reset voltage. This switch is accompanied by altered synchronization properties of neurons in a network and thereby captures a main characteristic of the unfolding of the saddle-node loop bifurcation—a qualitative transition that was recently demonstrated in biological neurons. Using this switch, cell-intrinsic properties alone provide a means to control whether small networks of all-to-all coupled neurons on the chip exhibit synchronized firing or splayed-out spiking patterns. We use an example from a central pattern generating circuit in the fruitfly to show that such dynamics can be induced and controlled on the chip. Our study thereby demonstrates the potential of neuromorphic chips with relatively complex and tunable single-neuron dynamics such as the BrainScaleS-2 chip, to generate computationally distinct single unit dynamics. We conclude with a discussion of the utility of versatile spike-generating mechanisms on neuromorphic chips.

Spike-timing-dependent plasticity (STDP) is an unsupervised learning mechanism for spiking neural networks that has received significant attention from the neuromorphic hardware community. However, scaling such local learning techniques to deeper networks and large-scale tasks has remained elusive. In this work, we investigate a Deep-STDP framework where a rate-based convolutional network, that can be deployed in a neuromorphic setting, is trained in tandem with pseudo-labels generated by the STDP clustering process on the network outputs. We achieve 24.56% higher accuracy and 3.5 × faster convergence speed at iso-accuracy on a 10-class subset of the Tiny ImageNet dataset in contrast to a k-means clustering approach.

Understanding how biological neural networks carry out learning using spike-based local plasticity mechanisms can lead to the development of real-time, energy-efficient, and adaptive neuromorphic processing systems. A large number of spike-based learning models have recently been proposed following different approaches. However, it is difficult to assess if these models can be easily implemented in neuromorphic hardware, and to compare their features and ease of implementation. To this end, in this survey, we provide an overview of representative brain-inspired synaptic plasticity models and mixed-signal complementary metal–oxide–semiconductor neuromorphic circuits within a unified framework. We review historical, experimental, and theoretical approaches to modeling synaptic plasticity, and we identify computational primitives that can support low-latency and low-power hardware implementations of spike-based learning rules. We provide a common definition of a locality principle based on pre- and postsynaptic neural signals, which we propose as an important requirement for physical implementations of synaptic plasticity circuits. Based on this principle, we compare the properties of these models within the same framework, and describe a set of mixed-signal electronic circuits that can be used to implement their computing principles, and to build efficient on-chip and online learning in neuromorphic processing systems.