Affiliation: Boston University
- Implications of neuronal diversity on population codingMaoz Shamir
Center for BioDynamics, Boston University, Boston, MA 02215, U S A
Neural Comput 18:1951-86. 2006..We show that simple online learning can generate readout weights with the appropriate dependence on the neuronal diversity, thereby yielding efficient readout...
- The scaling of winner-takes-all accuracy with population sizeMaoz Shamir
Hearing Research Center and Center for BioDynamics, Boston University, Boston, MA 02215, USA
Neural Comput 18:2719-29. 2006..More precisely, we show that while the accuracy of a linear readout scales linearly with the population size, the accuracy of the WTA readout scales logarithmically with the number of cells in the population...
- Temporal coding of time-varying stimuliMaoz Shamir
Hearing Research Center, Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
Neural Comput 19:3239-61. 2007..We further show that finite temporal resolution is sufficient for obtaining most of the information from the cell's response. This finite timescale is related to the response properties of the cell...
- Cortical discrimination of complex natural stimuli: can single neurons match behavior?Le Wang
Hearing Research Center, Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, USA
J Neurosci 27:582-9. 2007..Finally, we compare neural performance with behavioral performance. We find a diverse range of performance levels in field L, with neural performance matching behavioral accuracy only for the best neurons using a spike-timing-based code...
- Representation of time-varying stimuli by a network exhibiting oscillations on a faster time scaleMaoz Shamir
Center for BioDynamics, Boston University, Boston, MA, USA
PLoS Comput Biol 5:e1000370. 2009....
- Nonlinear population codesMaoz Shamir
Racah Institute of Physics and Center for Neural Computation, Hebrew University of Jerusalem, Jerusalem 91904, Israel
Neural Comput 16:1105-36. 2004..We show that this nonlinear population vector scheme yields accurate estimates of stimulus parameters, with an efficiency that grows linearly with the population size. This code can be implemented using biologically plausible neurons...