Kinematic networks distributed model for representing and regularizing motor redundancy
Biological Cybernetics
Neural representation of space using sinusoidal arrays
Neural Computation
The population vector, an unbiased estimator for non-uniformly distributed neural maps
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Statistically efficient estimation using population coding
Neural Computation
The effect of correlated variability on the accuracy of a population code
Neural Computation
Where does the population vector of motor cortical cells point during reaching movements?
Proceedings of the 1998 conference on Advances in neural information processing systems II
Parameter extraction from population codes: A critical assessment
Neural Computation
Learning population codes by minimizing description length
Neural Computation
Learning in linear neural networks: a survey
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
Many neurons of the central nervous system are broadly tuned to some sensory or motor variables. This property allows one to assign to each neuron a preferred attribute (PA). The width of tuning curves and the distribution of PAs in a population of neurons tuned to a given variable define the collective behavior of the population. In this article, we study the relationship of the nature of the tuning curves, the distribution of PAs, and computational properties of linear neuronal populations. We show that noise-resistant distributed linear algebraic processing and learning can be implemented by a population of cosine tuned neurons assuming a nonuniform but regular distribution of PAs. We extend these resuits analytically to the noncosine tuning and uniform distribution case and show with a numerical simulation that the results remain valid for a nonuniform regular distribution of PAs for broad noncosine tuning curves. These observations provide a theoretical basis for modeling general nonlinear sensorimotor transformations as sets of local linearized representations.