Solving the problem of negative synaptic weights in cortical models Export

@unpublished{citeulike:1568722, abstract = {In cortical neural networks, connections from a given neuron are either inhibitory or excitatory, but not both. This constraint is often ignored by theoreticians who build models of these systems. There is currently no general solution to the problem of converting such unrealistic network models into biologically plausible models that respect this constraint. We demonstrate a constructive transformation of models that solves this problem for both feedforward and dynamic recurrent networks. The resulting models give a close approximation to the original network functions and temporal dynamics of the system, and are biologically plausible. More precisely, we identify a general form for the solution to this problem. As a result, we also describe how the precise solution for a given cortical network can be determined empirically.}, author = {Parisien, Christopher and Anderson, Charles H. and Eliasmith, Chris}, citeulike-article-id = {1568722}, citeulike-linkout-0 = {http://www.arts.uwaterloo.ca/~celiasmi/Papers/parisien.eliasmith.2007.negative%20weights.penult.neurcomp.pdf}, journal = {Neural Computation}, keywords = {cogsci, neuro}, posted-at = {2007-08-16 11:11:53}, priority = {2}, title = {Solving the problem of negative synaptic weights in cortical models}, url = {http://www.arts.uwaterloo.ca/~celiasmi/Papers/parisien.eliasmith.2007.negative%20weights.penult.neurcomp.pdf}, year = {2007} }

TY - INPR ID - citeulike:1568722 L3 - citeulike-article-id:1568722 N2 - In cortical neural networks, connections from a given neuron are either inhibitory or excitatory, but not both. This constraint is often ignored by theoreticians who build models of these systems. There is currently no general solution to the problem of converting such unrealistic network models into biologically plausible models that respect this constraint. We demonstrate a constructive transformation of models that solves this problem for both feedforward and dynamic recurrent networks. The resulting models give a close approximation to the original network functions and temporal dynamics of the system, and are biologically plausible. More precisely, we identify a general form for the solution to this problem. As a result, we also describe how the precise solution for a given cortical network can be determined empirically. JF - Neural Computation TI - Solving the problem of negative synaptic weights in cortical models KW - cogsci KW - neuro AU - Parisien, Christopher AU - Anderson, Charles AU - Eliasmith, Chris PY - 2007/// UR - http://www.arts.uwaterloo.ca/~celiasmi/Papers/parisien.eliasmith.2007.negative%20weights.penult.neurcomp.pdf ER -