Reverse-Engineering network topology from spiketrains with FPGA and neuromorphic hardware
This time, we will make use the ROLLS Neuromorphic Processor as our event-source and compute both the spatial and temporal correlation of events. The Idea is to set up the Neuromorphic Processors Long-Term-Plastic Synapses in a predetermined fashion that is unknown to the participants, and then to come up with methods to reconstruct the Networks topology from the observed correlated spiking activity.
Besides its obvious use in machine vision, having a spatio-temporal correlator that can handle large numbers of event-sources (Here the ROLLS Somata) has another use that may be of particular interest to neurobiologists. Analysing connectivity in a sample of neural tissue for reconstructing network topology is a challenging problem. The state of the art approach here relies on optical methods like viral transfection-studies and calcium imaging. Although providing great amounts of information, a human operator is needed for classifying synaptic connections off-line. However, recent advances in microelectrode-technologies provide extracellular recordings in high spacial resolution. Like the DVS, some of these Sensor-Arrays encode recorded spikes following the AER protocol which makes them ideal inputs for neuromorphic systems. Lets develop an architecture that is capable of inferring causal relationships between observed events and thus provides information on the topology of the Biological neural network.
We will build on last years theoretical foundation and both try to extend and apply it to the toy-problem at hand.Login to become a member send
|Mon, 02.05.2016||16:00 - 17:00||Disco|