#### Filter Results:

#### Publication Year

1996

2017

#### Publication Type

#### Co-author

#### Publication Venue

#### Brain Region

#### Cell Type

#### Key Phrases

#### Method

#### Organism

Learn More

- Wolfgang Maass, Thomas Natschläger, Henry Markram
- Neural Computation
- 2002

A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real time. We propose a new computational model for real-time computing on time-varying input that provides an alternative to paradigms based… (More)

- Nils Bertschinger, Thomas Natschläger
- Neural Computation
- 2004

Depending on the connectivity, recurrent networks of simple computational units can show very different types of dynamics, ranging from totally ordered to chaotic. We analyze how the type of dynamics (ordered or chaotic) exhibited by randomly connected networks of threshold gates driven by a time-varying input signal depends on the parameters describing the… (More)

- Romain Brette, Michelle Rudolph-Lilith, +19 authors Alain Destexhe
- Journal of Computational Neuroscience
- 2007

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different… (More)

- T Natschläger, B Ruf
- Network
- 1998

Spiking neurons, receiving temporally encoded inputs, can compute radial basis functions (RBFs) by storing the relevant information in their delays. In this paper we show how these delays can be learned using exclusively locally available information (basically the time difference between the pre- and postsynaptic spikes). Our approach gives rise to a… (More)

- Wolfgang Maass, Thomas Natschläger, Henry Markram
- NIPS
- 2002

A key challenge for neural modeling is to explain how a continuous stream of multi-modal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neu-rons in real-time. We propose a new computational model that does not require a task-dependent construction of neural circuits. Instead it is based… (More)

- Thomas Natschläger, Wolfgang Maass
- Neural Computation
- 2001

Experimental data have shown that synapses are heterogeneous: different synapses respond with different sequences of amplitudes of postsynaptic responses to the same spike train. Neither the role of synaptic dynamics itself nor the role of the heterogeneity of synaptic dynamics for computations in neural circuits is well understood. We present in this… (More)

A theoretical model for analogue computation in networks of spiking neurons with temporal coding is introduced and tested through simulations in GENESIS. It turns out that the use of multiple synapses yields very noise robust mechanisms for analogue computations via the timing of single spikes in networks of detailed compartmental neuron models. In this… (More)

- Dejan Pecevski, Thomas Natschläger, Klaus Schuch
- Front. Neuroinform.
- 2009

The Parallel Circuit SIMulator (PCSIM) is a software package for simulation of neural circuits. It is primarily designed for distributed simulation of large scale networks of spiking point neurons. Although its computational core is written in C++, PCSIM's primary interface is implemented in the Python programming language, which is a powerful programming… (More)

In this paper we analyze the relationship between the computational capabilities of randomly connected networks of threshold gates in the time-series domain and their dynamical properties. In particular we propose a complexity measure which we find to assume its highest values near the edge of chaos, i.e. the transition from ordered to chaotic dynamics.… (More)

- Thomas Natschläger, Wolfgang Maass
- NIPS
- 2000

Experimental data have shown that synapses are heterogeneous: different synapses respond with different sequences of amplitudes of postsynaptic responses to the same spike train. Neither the role of synaptic dynamics itself nor the role of the heterogeneity of synaptic dynamics for computations in neural circuits is well understood. We present in this… (More)