Accession Number : AD1029686


Title :   A Neural Information Field Approach to Computational Cognition


Descriptive Note : Technical Report,01 Sep 2013,31 May 2017


Corporate Author : UNIVERSITY OF WATERLOO WATERLOO United States


Personal Author(s) : Eliasmith,Chris ; Pinotsis,Dimitris


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1029686.pdf


Report Date : 18 Nov 2016


Pagination or Media Count : 11


Abstract : The two main research objectives for this project are to understand how local cortical circuits embody states underlying cognitive functions, and to build large-scale models that are able to simulate a variety of tasks and levels of detail. Over the last three years, we have: demonstrated the first large-scale, function neural simulation to use highly detailed single neuron models, allowing the simulated testing of drug effects on cognitive performance; demonstrated a scalable neural model of motor planning; developed a new perceptual decision making model; demonstrated adaptive motor control in a large-scale cognitive simulation with spiking neurons (Spaun); demonstrated simple instruction following in Spaun; shown the first human-scale concept representations in spiking networks; demonstrated how to learn those representations; optimized cognitive computations for the Nengo simulation environment; demonstrated transfer learning to replicate performance of children learning to count in a SPA model; proposed a new SPA model of cognitive load using the N-back task; developed anew model of the effects of distraction in working memory; shown a hippocampal model able to perform context sensitive sequence encoding and retrieval; proposed what is currently the best model of neural activity during context-based working memory retrieval in monkeys; developed new software infrastructure for large spiking neural models; developed specialized hardware implementations of the N-back task; and optimized large model simulations for CPUs.


Descriptors :   computational neuroscience , brain , scale models , neural engineering , artificial neural networks , DECISION MAKING , COMPUTER SIMULATIONS


Subject Categories : Anatomy and Physiology


Distribution Statement : APPROVED FOR PUBLIC RELEASE