# A theoretical basis for efficient computations with noisy spiking neurons

@article{Jonke2014ATB, title={A theoretical basis for efficient computations with noisy spiking neurons}, author={Zeno Jonke and Stefan Habenschuss and Wolfgang Maass}, journal={ArXiv}, year={2014}, volume={abs/1412.5862} }

Network of neurons in the brain apply - unlike processors in our current generation of computer hardware - an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However it turned out to be surprisingly difficult to design networks of spiking neurons that are able…

## 7 Citations

### On the Algorithmic Power of Spiking Neural Networks

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A dual view of the SNN dynamics is seen, under which SNN can be viewed as a new natural primal-dual algorithm for the l1 minimization problem, which is of independent interest and may potentially find interesting interpretation in neuroscience.

### To Spike or Not to Spike: That Is the Question

- Computer Science, BiologyProc. IEEE
- 2015

Recent progress in understanding how complex computations can be carried out with such stochastically spiking neurons is discussed, and the viability of such a merger is examined.

### Brain-Inspired Models and Systems for Distributed Computation

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The purpose of this thesis is to investigate key characteristics of biological nervous systems and suggest ways of incorporating these principles in artificial systems and demonstrate how a non-trivial network topology, featuring predominantly local connectivity, can be achieved by taking into account these principles.

### Integrating Temporal Information to Spatial Information in a Neural Circuit

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This paper designs three networks that solve three problems -- "first consecutive spikes counting", "total spikes counting" and "$k-spikes temporal to spatial encoding" -- to model how brains extract temporal information into spatial information from different neural codings.

### Positive Neural Networks in Discrete Time Implement Monotone-Regular Behaviors

- Mathematics, Computer ScienceNeural Computation
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It is shown that in discrete time and in the absence of noise, the class of positive neural networks captures the so-called monotone-regular behaviors, which are based on regular languages.

### Spiking analog VLSI neuron assemblies as constraint satisfaction problem solvers

- Computer Science2016 IEEE International Symposium on Circuits and Systems (ISCAS)
- 2016

This work shows how stochasticity can be achieved by implementing deterministic models of integrate and fire neurons using subthreshold analog circuits that are affected by thermal noise and presents an efficient implementation of spike-based CSP solvers using a reconfigurable neural network VLSI device, and the device's intrinsic noise as a source of randomness.

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