Abstract
Spectro-temporal receptive fields (STRFs) have been widely used as linear approximations to the signal transform from sound spectrograms to neural responses along the auditory pathway. Their dependence on statistical attributes of the stimuli, such as sound intensity, is usually explained by nonlinear mechanisms and models. Here, we apply an efficient coding principle which has been successfully used to understand receptive fields in early stages of visual processing, in order to provide a computational understanding of the STRFs. According to this principle, STRFs result from an optimal tradeoff between maximizing the sensory information the brain receives, and minimizing the cost of the neural activities required to represent and transmit this information. Both terms depend on the statistical properties of the sensory inputs and the noise that corrupts them. The STRFs should therefore depend on the input power spectrum and the signal-to-noise ratio, which is assumed to increase with input intensity. We analytically derive the optimal STRFs when signal and noise are approximated as Gaussians. Under the constraint that they should be spectro-temporally local, the STRFs are predicted to adapt from being band-pass to low-pass filters as the input intensity reduces, or the input correlation becomes longer range in sound frequency or time. These predictions qualitatively match physiological observations. Our prediction as to how the STRFs should be determined by the input power spectrum could readily be tested, since this spectrum depends on the stimulus ensemble. The potentials and limitations of the efficient coding principle are discussed.
Highlights
In response to acoustic input signals, neurons in the auditory pathway are typically selective to sound frequency f and have particular response latencies
At least ignoring cases with f v4 kHz, in which neuronal responses often phase lock to the sound waves, a spectro-temporal receptive field (STRF) is often used to describe the tuning properties of a neuron [1,2,3,4]. This is a two-dimensional function STRF (f,t) that reports the sensitivity of the neuron at response latency t to acoustic inputs of frequency f for a given stimulus ensemble
We propose that the STRFs and their dependence on the input ensemble can be understood by an efficient coding principle, according to which the responses of the encoding neurons report the maximum amount of information about the sensory input, subject to limits on the neural cost in representing and transmitting information
Summary
In response to acoustic input signals, neurons in the auditory pathway are typically selective to sound frequency f and have particular response latencies. At least ignoring cases with f v4 kHz, in which neuronal responses often phase lock to the sound waves, a spectro-temporal receptive field (STRF) is often used to describe the tuning properties of a neuron [1,2,3,4]. This is a two-dimensional function STRF (f ,t) that reports the sensitivity of the neuron at response latency t to acoustic inputs of frequency f for a given stimulus ensemble (i.e., given input statistics). In studies in which the temporal dimension is omitted, the STRF is called the spectral receptive field (SRF)
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