TY - GEN

T1 - Statistically efficient estimation using cortical lateral connection

AU - Pouget, Alexandre

AU - Zhang, Kechen

PY - 1997/1/1

Y1 - 1997/1/1

N2 - Coarse codes are widely used throughout the brain to encode sensory and motor variables. Methods designed to interpret these codes, such as population vector analysis, are either inefficient, i.e., the variance of the estimate is much larger than the smallest possible variance, or biologically implausible, like maximum likelihood. Moreover, these methods attempt to compute a scalar or vector estimate of the encoded variable. Neurons are faced with a similar estimation problem. They must read out the responses of the presynaptic neurons, but, by contrast, they typically encode the variable with a further population code rather than as a scalar. We show how a non-linear recurrent network can be used to perform these estimation in an optimal way while keeping the estimate in a coarse code format. This work suggests that lateral connections in the cortex may be involved in cleaning up uncorrelated noise among neurons representing similar variables.

AB - Coarse codes are widely used throughout the brain to encode sensory and motor variables. Methods designed to interpret these codes, such as population vector analysis, are either inefficient, i.e., the variance of the estimate is much larger than the smallest possible variance, or biologically implausible, like maximum likelihood. Moreover, these methods attempt to compute a scalar or vector estimate of the encoded variable. Neurons are faced with a similar estimation problem. They must read out the responses of the presynaptic neurons, but, by contrast, they typically encode the variable with a further population code rather than as a scalar. We show how a non-linear recurrent network can be used to perform these estimation in an optimal way while keeping the estimate in a coarse code format. This work suggests that lateral connections in the cortex may be involved in cleaning up uncorrelated noise among neurons representing similar variables.

UR - http://www.scopus.com/inward/record.url?scp=0041775660&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0041775660&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0041775660

SN - 0262100657

SN - 9780262100656

T3 - Advances in Neural Information Processing Systems

SP - 97

EP - 103

BT - Advances in Neural Information Processing Systems 9 - Proceedings of the 1996 Conference, NIPS 1996

PB - Neural information processing systems foundation

T2 - 10th Annual Conference on Neural Information Processing Systems, NIPS 1996

Y2 - 2 December 1996 through 5 December 1996

ER -