Papers by Johannes Schemmel
IEEE Transactions on Biomedical Circuits and Systems, 2017
2020 IEEE International Symposium on Circuits and Systems (ISCAS), 2020
We present first experimental results on the novel BrainScaleS-2 neuromorphic architecture based ... more We present first experimental results on the novel BrainScaleS-2 neuromorphic architecture based on an analog neuro-synaptic core and augmented by embedded microprocessors for complex plasticity and experiment control. The high acceleration factor of 1000 compared to biological dynamics enables the execution of computationally expensive tasks, by allowing the fast emulation of long-duration experiments or rapid iteration over many consecutive trials. The flexibility of our architecture is demonstrated in a suite of five distinct experiments, which emphasize different aspects of the BrainScaleS-2 system.
Neuromorphic hardware in the loop: Training a deep spiking network on the BrainScaleS wafer-scale system
2017 International Joint Conference on Neural Networks (IJCNN)
Frontiers in Neuroscience
Frontiers in Neuroscience
A Mixed-Signal Structured AdEx Neuron for Accelerated Neuromorphic Cores
IEEE Transactions on Biomedical Circuits and Systems
Here, we describe a multicompartment neuron circuit based on the adaptive-exponential I&F... more Here, we describe a multicompartment neuron circuit based on the adaptive-exponential I&F (AdEx) model, developed for the second-generation BrainScaleS hardware. Based on an existing modular leaky integrate-and-fire (LIF) architecture designed in 65-nm CMOS, the circuit features exponential spike generation, neuronal adaptation, intercompartmental connections as well as a conductance-based reset. The design reproduces a diverse set of firing patterns observed in cortical pyramidal neurons. Further, it enables the emulation of sodium and calcium spikes, as well as N-methyl-D-aspartate plateau potentials known from apical and thin dendrites. We characterize the AdEx circuit extensions and exemplify how the interplay between passive and nonlinear active signal processing enhances the computational capabilities of single (but structured) on-chip neurons.
Scientific reports, Jan 13, 2018
Spiking networks that perform probabilistic inference have been proposed both as models of cortic... more Spiking networks that perform probabilistic inference have been proposed both as models of cortical computation and as candidates for solving problems in machine learning. However, the evidence for spike-based computation being in any way superior to non-spiking alternatives remains scarce. We propose that short-term synaptic plasticity can provide spiking networks with distinct computational advantages compared to their classical counterparts. When learning from high-dimensional, diverse datasets, deep attractors in the energy landscape often cause mixing problems to the sampling process. Classical algorithms solve this problem by employing various tempering techniques, which are both computationally demanding and require global state updates. We demonstrate how similar results can be achieved in spiking networks endowed with local short-term synaptic plasticity. Additionally, we discuss how these networks can even outperform tempering-based approaches when the training data is imb...
Physical review. E, 2016
The highly variable dynamics of neocortical circuits observed in vivo have been hypothesized to r... more The highly variable dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference but stand in apparent contrast to the deterministic response of neurons measured in vitro. Based on a propagation of the membrane autocorrelation across spike bursts, we provide an analytical derivation of the neural activation function that holds for a large parameter space, including the high-conductance state. On this basis, we show how an ensemble of leaky integrate-and-fire neurons with conductance-based synapses embedded in a spiking environment can attain the correct firing statistics for sampling from a well-defined target distribution. For recurrent networks, we examine convergence toward stationarity in computer simulations and demonstrate sample-based Bayesian inference in a mixed graphical model. This points to a new computational role of high-conductance states and establishes a rigorous link between deterministic neuron m...
A highly tunable 65-nm CMOS LIF neuron for a large scale neuromorphic system
2016 46th European Solid-State Device Research Conference (ESSDERC), 2016
Modeling Synaptic Plasticity within Networks of Highly Accelerated I&F Neurons
2007 Ieee International Symposium on Circuits and Systems, May 27, 2007
When studying the different aspects of synaptic plasticity, the timescales involved range from mi... more 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
The 2006 Ieee International Joint Conference on Neural Network Proceedings, 2006
Probabilistic Inference in Discrete Spaces with Networks of LIF Neurons
ABSTRACT The means by which cortical neural networks are able to efficiently solve inference prob... more ABSTRACT The means by which cortical neural networks are able to efficiently solve inference problems remains on open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell level. In this article, we describe a complete theoretical framework for building networks of leaky integrate-and-fire neurons that can sample from arbitrary probability distributions over binary random variables. We test our framework for a model inference task based on a psychophysical phenomenon (the Knill-Kersten optical illusion) and further assess its performance when applied to randomly generated distributions. As the local computations performed by the network strongly depend on the interaction between neurons, we compare several types of couplings mediated by either single synapses or interneuron chains. Due to its robustness to substrate imperfections such as parameter noise and background noise correlations, our model is particularly interesting for implementation on novel, neuro-inspired computing architectures, which can thereby serve as a fast, low-power substrate for solving real-world inference problems.
Proceedings Third NASA/DoD Workshop on Evolvable Hardware. EH-2001, 2000
High-conductance states in a neuromorphic hardware system
Proceedings of the 2009 International Joint Conference on Neural Networks, Jun 14, 2009
Proceedings. 2004 NASA/DoD Conference on Evolvable Hardware, 2004., 2004
First NASA/ESA Conference on Adaptive Hardware and Systems (AHS'06), 2006
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Papers by Johannes Schemmel