Stability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity

Abstract

Spike-timing dependent plasticity (STDP) is a widespread plasticity mechanism in the nervous system. The simplest description of STDP only takes into account pairs of pre- and postsynaptic spikes, with potentiation of the synapse when a presynaptic spike precedes a postsynaptic spike and depression otherwise. In light of experiments that explored a variety of spike patterns, the pair-based STDP model has been augmented to account for multiple pre- and postsynaptic spike interactions. As a result, a number of different “multi-spike” STDP models have been proposed based on different experimental observations. The behavior of these models at the population level is crucial for understanding mechanisms of learning and memory. The challenging balance between the stability of a population of synapses and their competitive modification is well studied for pair-based models, but it has not yet been fully analyzed for multi-spike models. Here, we address this issue through numerical simulations of an integrate-and-fire model neuron with excitatory synapses subject to STDP described by three different proposed multi-spike models. We also analytically calculate average synaptic changes and fluctuations about these averages. Our results indicate that the different multi-spike models behave quite differently at the population level. Although each model can produce synaptic competition in certain parameter regions, none of them induces synaptic competition with its originally fitted parameters. The dichotomy between synaptic stability and Hebbian competition, which is well characterized for pair-based STDP models, persists in multi-spike models. However, anti-Hebbian competition can coexist with synaptic stability in some models. We propose that the collective behavior of synaptic plasticity models at the population level should be used as an additional guideline in applying phenomenological models based on observations of single synapses.

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

“…[227,257,258] These competitive interactions have been linked to Hebbian learning in an elegant theory by Miller and MacKay. [227,257,258] These competitive interactions have been linked to Hebbian learning in an elegant theory by Miller and MacKay.…”

The Role of Neuromodulators in Cortical Plasticity. A Computational Perspective

“…These low correlations ensure the learning window is equally sampled so that the asymmetry of potentiation and depression results in depression of these weak synaptic weights. This effect was formally derived in several studies (e.g., Kempter et al, 1999; Song et al, 2000; Gilson et al, 2009; Babadi and Abbott, 2016). For the remaining learning rules, synaptic weights are either all potentiated or all depressed (Figure 1E).…”

“…This allowed us to explore a wide range of different possibilities within a frequently explored and well described framework. Some of the behaviors shown in this perspective article can also be explored analytically, using established techniques of plasticity in feedforward network (Kempter et al, 1999; Song et al, 2000; van Rossum et al, 2000; Kempter et al, 2001; Rubin et al, 2001; Câteau and Fukai, 2003; Izhikevich and Desai, 2003; Zhu et al, 2006; Burkitt et al, 2007; Gilson et al, 2009, 2010; Gjorgjieva et al, 2011; Ocker et al, 2015; Babadi and Abbott, 2016). …”

Parameter Exploration to Improve Performance of Memristor-Based Neuromorphic Architectures

“…Consequently, with the latest proposals to use the memristive nano-devices as synapses, we implement an efficient and well-studied unsupervised learning rule known as spike timing dependent plasticity (STDP) [19], [20]. In this study, building upon our previous work [21], we show that the memristive synapses are adapted to unsupervised STDP learning in SNNs.…”

“…Another example is to evaluate the impact of learning parameter (α) and initial conductance of synapse which are studied in [17]. The advantages and disadvantages of using different types of STDP learning methods are studied comprehensively in [20], [61].…”