My current research is on the auditory system.

I investigate responses of auditory cortical neurons in the brain to study computations underlying the processing of natural sound stimuli throughout the auditory pathway.

Using state-of-the-art NeuroPixel probe electrodes, I record the activity of a large number of auditory neurons simultaneously in anaesthetised ferrets in an in vivo setting. The large dataset, thus created, allows for the computational modelling of the auditory cortex.

The sound stimuli I use are isolated natural sounds, such as speech, animal calls, and environmental sounds, as well as sound mixtures of the same sounds. There are a couple of reasons for using natural sounds - one, they evoke strong response in large number of cortical neurons, two, cortex is optimized to represent the statistics of natural sounds.

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Research questions I am interested in:When neurons fire in the auditory cortex, what do they represent? How much of the past stimulus does the neural population represent, and how much of the future can it predict? What mechanisms are responsible for integration of past stimulus history and representation of the future stimulus?

I decode from a very small window of neural activity (~10 ms), the spectrogram of stimulus past and future relative to that window. I then build models for neural activity that would account for the encoding of past and future sounds inputs by neural population. Furthermore, I attempt to improve the explanatory power of these encoding models to predict neural activity in general. I employ various signal processing and machine learning techniques (e.g. linear regression, GLM and neural networks) for the encoding and decoding models.

I am mainly interested in biologically based modelling, developing models containing parameters that explicitly express biological characteristics of neurons such as membrane time constant, firing rate adaptation, gain adaption and recurrency.

We have recently published a model that accounts for the dependence of auditory neurons to recent stimulus history. This model applies the principles of dynamical systems in an artificial neural network to capture temporal receptive fields in auditory cortex by a short-span of time delays and exponentially decaying membrane time constant of neurons. To know more about it: Rahman M, Willmore BDB, King AJ, Harper NS (2019) A dynamic network model of temporal receptive fields in primary auditory cortex. PLoS Comput Biol 15(5): e1006618. https://doi.org/10.1371/journal.pcbi.1006618

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