Tutorials & E-Library

SPADE is a method to detect repeated spatio-temporal activity patterns in parallel spike train data that occur in excess to chance expectation. In this tutorial, we will use SPADE to detect the simplest type of such patterns, synchronous events that are found across a subset of the neurons considered (i.e., patterns that do not exhibit a temporal extent). We will demonstrate the method on stochastic data in which we control the patterns statistics.

Gaussian-process factor analysis (GPFA) is a dimensionality reduction method for neural trajectory visualization of parallel spike trains. GPFA applies factor analysis (FA) to time-binned spike count data to reduce the dimensionality and at the same time smoothes the resulting low-dimensional trajectories by fitting a Gaussian process (GP) model to them.

The Unitary Events (UE) analysis [1] tool allows us to reliably detect correlated spiking activity that is not explained by the firing rates of the neurons alone. It was designed to detect coordinated spiking activity that occurs significantly more often than predicted by the firing rates of the neurons. The method allows one to analyze correlations not only between pairs of neurons but also between multiple neurons, by considering the various spike patterns across the neurons. In addition, the method allows one to extract the dynamics of correlation between the neurons by perform-ing the analysis in a time-resolved manner. This enables us to relate the occurrence of spike synchrony to behavior.

Covers statistics module with an introduction to Neo input-output data types like SpikeTrain, AnalogSignal, etc.

In this tutorial, the neuron with dendritic action potentials from the NESTML active dendrite tutorial is combined with a spike-timing dependent synaptic plasticity model. The dendritic action potential current acts as the “third factor” in the learning rule (in addition to pre- and postsynaptic spike timings) and is used to gate the weight update: changes in the weight can only occur during the postsynaptic neuron’s dendritic action potential.

In this tutorial, a dopamine-modulated STDP model is created in NESTML, and we characterize the model before using it in a network (reinforcement) learning task.

Pavlov and Thompson (1902) first described classical conditioning: a phenomenon in which a biologically potent stimulus–the Unconditional Stimulus (UC)—is initially paired with a neutral stimulus—the Conditional Stimulus (CS). After many trials, learning is observed when the previously neutral stimuli start to elicit a response similar to that which was previously only elicited by the biologically potent stimulus. Pavlov and Thompson performed many experiments with dogs, observing their response (by monitoring salivation) to the appearance of a person who has been feeding them and the actual food appearing (UC). He demonstrated that the dogs started to salivate in the presence of the person who has been feeding them (or any other CS), rather than just when the food appears, because the CS had previously been associated with food.

In this tutorial, we will learn to formulate triplet rule (which considers sets of three spikes, i.e., two presynaptic and one postsynaptic spikes or two postsynaptic and one presynaptic spikes) for Spike Timing-Dependent Plasticity (STDP) learning model using NESTML and simulate it with NEST simulator.

In this tutorial, we will plot the “window function”, relating the weight change of a synapse to the relative timing of a single pair of pre- and postsynaptic spikes. This type of synaptic plasticity is commonly known as spike-timing depdendent plasticity (STDP).

The aim of this exercise is to obtain familiarity with NESTML by completing a partial model of the Izhikevich neuron.

In this tutorial, we formulate a NESTML model for an inhomogeneous Poisson process, and simulate it in NEST Simulator. The rate of the model is piecewise constant and is defined by an array containing desired rates (in units of 1/s) and an array of equal length containing the corresponding times (in units of ms). Please see the documentation for the NEST built-in inhomogeneous_poisson_generator for more details.

In this tutorial, we will formulate the Ornstein-Uhlenbeck (O-U) noise process in NESTML and simulate it in NEST Simulator.

In this tutorial we will go step by step through adding two different types of SFA mechanism to a neuron model, threshold adaptation and an adaptation current [1], and then evaluate how the new models behave in simulation.