Networks of (spiking) neurons

Standard neural network theory describes the neuron as an input-output unit with a nonlinear transfer function. Real biological neurons are much more complicated than that. They have  intrinsic dynamics which transform the input into a sequence of electrical pulses, the so-called action potentials or spikes which are transmitted along the axon to other neurons. The exact timing of spikes can, in principle, convey a lot of information which is lost if only the temporally averaged mean firing rate is considered. During recent years, an increasing amount of experimental evidence has accumulated which shows that temporal information indeed plays a role in several biological neural systems.

In order to get a better understanding of temporal aspects of information processing, we study the dynamics of recurrent neural networks, either in rate-models or in large populations of spiking neuron models. We work on a theory for populations equations with adaptive spiking neurons as well as on applications to memory formation and information recall in large networks.

Recent papers from the LCN

C. Gastaldi, E. De Falco, R.Q. Quiroga, and W. Gerstner (2021)
When shared concept cells support associations: theory of overlapping memory engrams 
BioRxiv doi:

S. Muscinelli, W. Gerstner, and T. Schwalger (2019)
How single neuron properties shape chaotic dynamics and signal transmission in random neural networks
PLOS Comput. Biol. 15:e1007122, doi:10.1371/journal.pcbi.1007122

A. Seeholzer, M. Deger, and W. Gerstner (2019)
Stability of working memory in continuous attractor networks under the control of short-term plasticity
PLOS Comput. Biol. 15:e1006928.
doi: 10.1371/journal.pcbi.1006928

T. Schwalger, M. Deger and W. Gerstner (2017)
Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.
PLoS Comput Biol 13(4): e1005507

C. Pozzorini, S. Mensi, O. Hagens, R. Naud, C. Koch, and W. Gerstner (2015)
Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models 
PLOS Comput. Biol. 11:e1004275

F. Zenke and E.J. Agnes and W. Gerstner (2015)
Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks
Nature Comm. 6: 6922

G. Hennequin, T.P. Vogels and W. Gerstner (2014)
Optimal Control of Transient Dynamics in Balanced Networks Supports Generation of Complex Movements
NEURON 82: 1394-1406

R. Naud and W. Gerstner (2012)
Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram
PLOS Comput. 8:e100271

W. Gerstner, R. Naud (2009)
How good are Neuron Models?
Science,  326:  379-380

R. Naud, N. Marcille, C. Clopath and W. Gerstner (2008)
Firing patterns in the adaptive exponential integrate-and-fire model
Biological Cybernetics,  99:335-347

L. Badel, S. Lefort, R. Brette, C. C. H. Petersen, W. Gerstner, M. J. E. Richardson (2008)
Dynamic I-V curves are reliable predictors of naturalistic pyramidal-neuron voltage traces
J. Neurophysilogy 99: 656-666

R. Jolivet and T. J. Lewis and W. Gerstner (2004)
Generalized Integrate-and-Fire Models of Neuronal Activity Approximate Spike Trains of a Detailed Model to a High Degree of Accuracy
J. Neurophysiology 92: 959-976

W. Gerstner (2000)
Population Dynamics of Spiking Neurons: Fast Transients, Asynchronous States, and Locking
Neural Computation 12:43-89.