The real-time biologically realistic neuromorphic attention group will attempt to generate working models of attention. There are several strands of activity involved. In one thread, we will focus on robotic attention, hopefully moving to some demo using the iCub robot. A second thread will examine the biological models in the basal ganglia as proposed by Kevin Gurney. In the second thread we will attempt to generated various modified Izhikevich-model neurons at a low level, and at a higher level to create realistic spiking subcircuits that generate Bayesian-like response to inputs. l The systems will use at least 2 chips: the SpiNNaker spiking neural chip and the Selective Attention (SAC) chip.

Implementing neural networks using the SpiNNaker system uses the PyNN package which we will provide on USB sticks, along with a tutorial for those unfamiliar with the system in the first session.

No previous experience is required, however, those interested in the low-level hardware interfacing parts of the projects should have at least basic hardware familiarity and in particular an understanding of general AER techniques and signalling.

Topics of Interest

There are basically 3 main topics of interest we (the SpiNNaker group) have considered; others please feel free to add more!

- Is a Bayesian model of attention an effective as well as biologically realistic one, and if so, what is the simplest spiking neural

network we can build to generate Bayesian-like behaviour?

- Can we create spiking network based attentional models to control behaviour in a robot engaged in real-time, real-world activity?

- What level of detail is necessary or desirable in the dynamic model of the neuron to capture the necessary attentional behaviour?

Projects

Please, suggest more projects here (by editing this page)

A simplified model of the basal ganglia

The basal ganglia appear to play a central role in coordinating and selecting actions. We will attempt to model this subsystem using a very simplified base unit (minicolumn-like) to represent the effects of an individual bayesian predictor. The model will use Izhikevich neurons to model most of the underlying dynamics (including burst neurons)

Controlled pick-and place

We will attempt to use an integrated retinal sensor/robotic arm to control pick-and-place of various randomly assorted components. The key task will be to recognise the object of interest, pick it out from the relevant array of objects, and place it in another location with specified orientation. This deceptively simple-sounding task should actually be quite challenging and requires a fair amount of attention-guided control.

Gaze direction with the iCub robot

We will try to get the iCub to direct its gaze towards a selected object out of a series of targets based upon saliency. This system will use the SAC chip as a pre-processing front-end in order to extract the salient object. Obviously this fairly ambitious task requires the integration of multiple hardware systems which we expect will furnish the main challenges.

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Running models on spiNNaker using PyNN tutorial

Spiking neural network models can be implemented on the spinnaker without any knowledge about the hardware by using PyNN, a description language for spiking neural networks written in python.

For more information about PyNN visit  http://www.neuralensemble.org/trac/PyNN/, talk to me (francesco.galluppi@…) or follow the PyNN tutorial on the FACETS/BrainScaleS workgroup http://capocaccia.ethz.ch/capo/wiki/2011/bsshw11.

As a basic introduction you can download the synfire_example.py on this page http://capocaccia.ethz.ch/capo/attachment/wiki/2011/rta11/synfire_example.py.

You can also take a look at last year's wiki page which contains more examples on how to use PyNN and SpiNNaker http://capocaccia.ethz.ch/capo/wiki/2010/spinn10

See also