Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
The upward bias in measures of information derived from limited data samples
Neural Computation
Spikes: exploring the neural code
Spikes: exploring the neural code
Estimation of entropy and mutual information
Neural Computation
Computation in a single Neuron: Hodgkin and Huxley revisited
Neural Computation
Neural Computation
How Close Are We to Understanding V1?
Neural Computation
System identification of Drosophila olfactory sensory neurons
Journal of Computational Neuroscience
Affective information processing and representations
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
Journal of Computational Neuroscience
Learning quadratic receptive fields from neural responses to natural stimuli
Neural Computation
Identifying functional bases for multidimensional neural computations
Neural Computation
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We propose a method that allows for a rigorous statistical analysis of neural responses to natural stimuli that are nongaussian and exhibit strong correlations. We have in mind a model in which neurons are selective for a small number of stimulus dimensions out of a high-dimensional stimulus space, but within this subspace the responses can be arbitrarily nonlinear. Existing analysis methods are based on correlation functions between stimuli and responses, but these methods are guaranteed to work only in the case of gaussian stimulus ensembles. As an alternative to correlation functions, we maximize the mutual information between the neural responses and projections of the stimulus onto low-dimensional subspaces. The procedure can be done iteratively by increasing the dimensionality of this subspace. Those dimensions that allow the recovery of all of the information between spikes and the full unprojected stimuli describe the relevant subspace. If the dimensionality of the relevant subspace indeed is small, it becomes feasible to map the neuron's input-output function even under fully natural stimulus conditions. These ideas are illustrated in simulations on model visual and auditory neurons responding to natural scenes and sounds, respectively.