Modeling Synaptic Plasticity within Networks of Highly Accelerated I&F Neurons
- 1 May 2007
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 02714302,p. 3367-3370
- https://doi.org/10.1109/iscas.2007.378289
Abstract
When studying the different aspects of synaptic plasticity, the timescales involved range from milliseconds to hours, thus covering at least seven orders of magnitude. To make this temporal dynamic range accessible to the experimentalist, we have developed a highly accelerated analog VLSI model of leaky integrate and fire neurons. It incorporates fast and slow synaptic facilitation and depression mechanisms in its conductance based synapses. By using a 180 nm process 10 5 synapses fit on a 25 mm 2 die. A single chip can model the temporal evolution of the synaptic weights in networks of up to 384 neurons with an acceleration factor of 10 5 while recording the neural action potentials with a temporal resolution better than 30 μ s biological time. This reduces the time needed for a 10 minute experiment to merely 6 ms, paving the way for complex parameter searches to reproduce biological findings. Due to a digital communication structure larger networks can be built from multiple chips while retaining an acceleration factor of a least 10 4 .Keywords
This publication has 9 references indexed in Scilit:
- Implementing Synaptic Plasticity in a VLSI Spiking Neural Network ModelPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- A VLSI Array of Low-Power Spiking Neurons and Bistable Synapses With Spike-Timing Dependent PlasticityIEEE Transactions on Neural Networks, 2006
- The high-conductance state of neocortical neurons in vivoNature Reviews Neuroscience, 2003
- States of High Conductance in a Large-Scale Model of the Visual CortexJournal of Computational Neuroscience, 2002
- Correlation based learning from spike timing dependent plasticityNeurocomputing, 2001
- Competitive Hebbian learning through spike-timing-dependent synaptic plasticityNature Neuroscience, 2000
- Differential signaling via the same axon of neocortical pyramidal neuronsProceedings of the National Academy of Sciences, 1998
- The neural code between neocortical pyramidal neurons depends on neurotransmitter release probabilityProceedings of the National Academy of Sciences, 1997
- Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalismJournal of Computational Neuroscience, 1994