Biological neurons possess elaborate dendrites that perform elaborate computations. They are however ignored in the widely used point neuron models. Here, we present a simple addition to the commonly used leaky integrate-and-fire model that introduces the concept of a dendrite. All synapses on the dendrite have a mutual relationship. The result is a form of short term plasticity in which synapse strengths are influenced by recent activity in other synapses. This improves the ability of the neuron to recognize temporal sequences
International audienceNonlinear dendritic integration is thought to increase the computational abili...
We have previously shown using biophysically detailed compartmental models that nonlinear interactio...
Dendrites are not static structures, new synaptic connec-tions are established and old ones disappea...
Synaptic plasticity is thought to be the principal neuronal mechanism underlying learning. Models of...
Neurons are the computational building blocks of our brains. They form complicated net- works that p...
The discovery of binary dendritic events such as local NMDA spikes in dendritic subbranches led to t...
One of the key questions in neuroscience is how our brain self-organises to efficiently process info...
SummaryMemories are believed to be stored in distributed neuronal assemblies through activity-induce...
Memories are believed to be stored in distributed neuronal assemblies through activity-induced chang...
Dendrites are not static structures, new synaptic connections are established and old ones disappear...
Computational analyses of dendritic computations often assume stationary inputs to neurons, ignoring...
A major challenge in neuroscience is to reverse engineer the brain and understand its information pr...
In this paper, we discuss the nonlinear computational power provided by dendrites in biological and ...
Neurons are spatially extended structures that receive and process inputs on their dendrites. It is ...
It has been conjectured that nonlinear processing in dendritic branches endows individual neurons wi...
International audienceNonlinear dendritic integration is thought to increase the computational abili...
We have previously shown using biophysically detailed compartmental models that nonlinear interactio...
Dendrites are not static structures, new synaptic connec-tions are established and old ones disappea...
Synaptic plasticity is thought to be the principal neuronal mechanism underlying learning. Models of...
Neurons are the computational building blocks of our brains. They form complicated net- works that p...
The discovery of binary dendritic events such as local NMDA spikes in dendritic subbranches led to t...
One of the key questions in neuroscience is how our brain self-organises to efficiently process info...
SummaryMemories are believed to be stored in distributed neuronal assemblies through activity-induce...
Memories are believed to be stored in distributed neuronal assemblies through activity-induced chang...
Dendrites are not static structures, new synaptic connections are established and old ones disappear...
Computational analyses of dendritic computations often assume stationary inputs to neurons, ignoring...
A major challenge in neuroscience is to reverse engineer the brain and understand its information pr...
In this paper, we discuss the nonlinear computational power provided by dendrites in biological and ...
Neurons are spatially extended structures that receive and process inputs on their dendrites. It is ...
It has been conjectured that nonlinear processing in dendritic branches endows individual neurons wi...
International audienceNonlinear dendritic integration is thought to increase the computational abili...
We have previously shown using biophysically detailed compartmental models that nonlinear interactio...
Dendrites are not static structures, new synaptic connec-tions are established and old ones disappea...