Neural path integrator

Cells in several areas of the hippocampal formation show place specific firing patterns, and are thought to form a distributed representation of an animal’s current location in an environment. Experimental results suggest that this representation is continually updated even in complete darkness, indicating the presence of a path integration mechanism in the rat. Adopting the Neural Engineering Framework (NEF) presented by Eliasmith and Anderson (2003) we derive a novel attractor network model of path integration, using heterogeneous spiking neurons. The network we derive incorporates representation and updating of position into a single layer of neurons, eliminating the need for a large external control population, and without making use of multiplicative synapses. An efficient and biologically plausible control mechanism results directly from applying the principles of the NEF. We simulate the network for a variety of inputs, analyze its performance, and give three testable predictions of our model.

Instructions

1. Run makeData.m in Matlab to generate data files needed.
2. Modify line 12 of path_integrator.py file, pointing it to the folder containing data files generated in the previous step.
3. Load path_integrator.py in Nengo and run.
4. In the simulation window (open by right-clicking "Path-Integrator" -> "run Interactive Plot"), right-click "PI"->"function" to open the function representation view. Then click the play button at the right bottom. Right-click "control"->"control" to the adjust input along two axis.

Note: an error may occur at the line 20 of makeData.m in old versions of MATLAB or Octave. Please try to substitute the tildes in line 20 with other variable names (e.g. "var1", "var2").