Populations of neurons represent sensory, motor and cognitive variables via patterns of activity distributed across the population. The size of the population used to encode a variable is typically much greater than the dimension of the variable itself, and thus the corresponding neural population activity occupies lower-dimensional subsets of the full set of possible activity states. Given population activity data with such lower-dimensional structure, a fundamental question asks how close the low-dimensional data lies to a linear subspace. The linearity or non-linearity of the low-dimensional structure reflects important computational features of the encoding, such as robustness and generalizability. Moreover, identifying such linear structure underlies common data analysis methods such as Principal Component Analysis. Here we show that for data drawn from many common population codes the resulting point clouds and manifolds are exceedingly nonlinear, with the dimension of the best-fitting linear subspace growing at least exponentially with the true dimension of the data. Consequently, linear methods like Principal Component Analysis fail dramatically at identifying the true underlying structure, even in the limit of arbitrarily many data points and no noise.
We build a computational rate model for a biological neural network found in mammals that is thought to be important in the localisation of the sound in the vertical plane. We find the response of neurons in the brain stem that participate in the localisation neural circuit to pure tones, broad band noise and notched noise and compare them to experimentally obtained response of these neurons. Our model is able to reproduce the sensitivity of these neurons in the brain stem to spectral properties of sounds that are important in localisation. This is the first rate based population model that elucidates all the response properties of the neurons in the vertical localisation pathway to our knowledge.
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