Synaptic Dynamics in Analog VLSI

By Chiara Bartolozzi, Giacomo Indiveri. Neural Computation, 2007

361 citations recorded by the source corpus.

Synapses are crucial elements for computation and information transfer in both real and artificial neural systems. Recent experimental findings and theoretical models of pulse-based neural networks suggest that synaptic dynamics can play a crucial role for learning neural codes and encoding spatiotemporal spike patterns. Within the context of hardware implementations of pulse-based neural networks, several analog VLSI circuits modeling synaptic functionality have been proposed. We present an overview of previously proposed circuits and describe a novel analog VLSI synaptic circuit suitable for integration in large VLSI spike-based neural systems. The circuit proposed is based on a computational model that fits the real postsynaptic currents with exponentials. We present experimental data showing how the circuit exhibits realistic dynamics and show how it can be connected to additional modules for implementing a wide range of synaptic properties.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/5874ea03e1a19d82cca2ccdc8b8b4ea248640824

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses (2015, methodology): “…(Mahowald and Douglas, 1991; Farquhar and Hasler, 2005; Hynna and Boahen, 2007; van Schaik et al., 2010), to models of synaptic dynamics (Liu, 2003; Bartolozzi and Indiveri, 2007a; Xu et al., 2007), to auditory/visual sensory systems (Sarpeshkar et al., 1996; van Schaik and Meddis, 1999; Zaghloul…”
Event-driven contrastive divergence for spiking neuromorphic systems (2013, background): “The implementation of synapses producing such PSPs on a large scale is very difficult to realize in hardware, when compared to first-order linear filters that result in “alpha”shaped PSPs (Destexhe et al., 1998; Bartolozzi and Indiveri, 2007).”
Neuromorphic Silicon Neuron Circuits (2011, methodology): “In this case, rather than using the reset voltage V pw as an analog bias, the designers used it as a digital signal externally controlled, and exploited its good matching properties. models (Destexhe and Huguenard, 2000): The membrane voltage creates an energy barrier which a gating particle (a…”
A Scalable Multicore Architecture With Heterogeneous Memory Structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs) (2017, methodology): “plemented using sub-threshold Differential Pair Integrator (DPI) log-domain filters, of the type proposed in [29], and described in [17].”
Memory and Information Processing in Neuromorphic Systems (2015, background): “rated by two independent sets of low-power log-domain pulse integrator filters [140] that can reproduce synaptic dynamics with time constants that can range from fractions of microseconds to hundreds of milliseconds.”
A Survey of Neuromorphic Computing and Neural Networks in Hardware (2017, neutral): “of conductance-based synapse models [246]–[251].”
Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems (2014, background): “…can be integrated to form multi-chip spiking recurrent and WinnerTake-All neural networks, which in turn have been proposed as neural models for explaining pattern recognition [24], [25], working memory [9], [26], decision making [27], [28] and state-dependent computation [29], [30] in the brain.”
Artificial neural networks in hardware: A survey of two decades of progress (2010, neutral): “Bartolozzi and Indiveri in [64] provide a comparative analysis of various hardware implementations for the spiking synaptic models.”
Integration of nanoscale memristor synapses in neuromorphic computing architectures (2013, background): “CMOS circuits to control the DPI Vw bias voltage, or to the Ith bias current [62, 69, 70].”
Artificial Intelligence in Healthcare: Review and Prediction Case Studies (2020, neutral): “Researchers have tried to implement AI algorithms using analog integrated circuits [15,16], spintronics [17], and memristors [18–20].”