Milad Lankarany

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Time-varying excitatory and inhibitory synaptic inputs govern activity of neurons and process information in the brain. The importance of trial-to-trial fluctuations of synaptic inputs has recently been investigated in neuroscience. Such fluctuations are ignored in the most conventional techniques because they are removed when trials are averaged during(More)
Neuron transforms information via a complex interaction between its previous states, its intrinsic properties, and the synaptic input it receives from other neurons. Inferring synaptic input of a neuron only from its membrane potential (output) that contains both sub-threshold and action potentials can effectively elucidate the information processing(More)
In this paper we present a novel method for identification of linear time invariant, non-minimum phase (NMP), FIR systems when only output data are available and the order of system exceeds four. We generally model a non-minimum phase FIR system as an MA model of known order. To estimate the model parameters, we exploit the 1-D diagonal slice of the third(More)
Advanced statistical methods have enabled trial-by-trial inference of the underlying excitatory and inhibitory synaptic conductances (SCs) of membrane-potential recordings. Simultaneous inference of both excitatory and inhibitory SCs sheds light on the neural circuits underlying the neural activity and advances our understanding of neural information(More)
Interaction of the excitatory and inhibitory synaptic inputs constructs the shape of the receptive fields and can elucidate the synaptic mechanism underlying the functional activities of neurons. Estimating trial-to-trial excitatory and inhibitory synaptic conductance from noisy observation of membrane potential or input current can reveal drivers of(More)
A novel iterative adaptive filtering approach is proposed to remove the Transcranial Magnetic Stimulation (TMS) induced artifact from multi-channel recordings of neural responses to sensory stimuli. For each specific channel, the average of all trials is considered as the input to the adaptive filter whose coefficients are calculated by minimizing the mean(More)