John Conklin
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
John Conklin has a total of 1 co-authors. Here are the top co-authors by number of shared publications:
- Chris Eliasmith
Centre for Theoretical Neuroscience, University of Waterloo, Waterloo, Canada
1 shared publication
Latest Publications
- 1 March 2005
- Published by Springer Nature in Journal of Computational Neuroscience
- Vol. 18 (2) , 183-203
A Controlled Attractor Network Model of Path Integration in the Rat
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.
- 1 March 2005
- Published by Springer Nature in Journal of Computational Neuroscience
- Vol. 18 (2) , 183-203
A Controlled Attractor Network Model of Path Integration in the Rat
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.