An introduction to splines for use in computer graphics & geometric modeling
An introduction to splines for use in computer graphics & geometric modeling
1994 Special Issue: Dynamics of compartmental model neurons
Neural Networks - Special issue: models of neurodynamics and behavior
Consistent recovery of stimuli encoded with a neural ensemble
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Reconstruction of sensory stimuli encoded with integrate-and-fire neurons with random thresholds
EURASIP Journal on Advances in Signal Processing - Special issue on statistical signal processing in neuroscience
Generalized sampling: a variational approach .I. Theory
IEEE Transactions on Signal Processing
On Real-Time AER 2-D Convolutions Hardware for Neuromorphic Spike-Based Cortical Processing
IEEE Transactions on Neural Networks
Population encoding with Hodgkin-Huxley neurons
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
Functional identification of spike-processing neural circuits
Neural Computation
| Hi-index | 0.00 |
We consider the problem of reconstructing finite energy stimuli encoded with a population of spiking leaky integrate-and-fire neurons. The reconstructed signal satisfies a consistency condition: when passed through the same neuron, it triggers the same spike train as the original stimulus. The recovered stimulus has to also minimize a quadratic smoothness optimality criterion. We formulate the reconstruction as a spline interpolation problem for scalar as well as vector valued stimuli and show that the recovery has a unique solution. We provide explicit reconstruction algorithms for stimuli encoded with single as well as a population of integrate-and-fire neurons. We demonstrate how our reconstruction algorithms can be applied to stimuli encoded with ON-OFF neural circuits with feedback. Finally, we extend the formalism to multi-input multi-output neural circuits and demonstrate that vector-valued finite energy signals can be efficiently encoded by a neural population provided that its size is beyond a threshold value. Examples are given that demonstrate the potential applications of our methodology to systems neuroscience and neuromorphic engineering.