Untitled - Laboratory of Neurophysics and Physiology

eview key theoretical results and provide numerous examples of massively parallel neural encoding
circuits and stimulus decoding algorithms with the Time Encoding Machines Toolbox (http:
//www.bionet.ee.columbia.edu/code/ted.html).
Part II: Channel Identification Machines
Parameter estimation is at the core of functional identification of neural circuits. How are estimates
of model parameters affected by the stimuli employed in neurophysiology What is a suitable metric
to assess the faithfulness of identified parameters and the goodness of model performance These
are key open questions that are of relevance to both theoretical and experimental neuroscientists. We
will discuss these questions using a class of algorithms called Channel Identification Machines (CIMs)
[7,8,9] and give an overview of the functional identification of massively parallel neural circuit models
of sensory systems arising in olfaction, audition and vision. These circuits are built with temporal,
spectro-temporal and non-separable spatio-temporal receptive fields, and biophysical spiking neuron
models. The tutorial will demonstrate how CIMs achieve the efficient identification of neural circuit
models and will provide numerous examples of functional identification using the Channel Identification
Machines Toolbox (http://www.bionet.ee.columbia.edu/code/cim.html).
References:
[1] Dimitrov et al. (2011), Information theory in neuroscience, Journal of Computational Neuroscience,
30(1):1–5
[2] Lazar (2013), Neural signal sampling and time encoding machines, In: Encyclopedia of Computational
Neuroscience, Jaeger & Jung (eds.), Springer
[3] Lazar (2010), Population encoding with Hodgkin-Huxley neurons, IEEE Transactions on Information
Theory, 56(2):821–837
[4] Lazar & Pnevmatikakis (2011), Video time encoding machines, IEEE Transactions on Neural
Networks 22(3):461–473
[5] Lazar et al. (2010), Encoding natural scenes with neural circuits with random thresholds, Vision
Research 50(22):2200–2212 (Special Issue on Mathematical Models of Visual Coding)
[6] Lazar & Zhou (2012), Massively parallel neural encoding and decoding of visual stimuli, Neural
Networks 32:303–312
[7] Lazar & Slutskiy (2010), Identifying dendritic processing, Advances in Neural Information Processing
Systems 23:1261–1269
[8] Kim & Lazar (2011), Recovery of stimuli encoded with a Hodgkin-Huxley neuron using conditional
PRCs, In: Phase Response Curves in Neuroscience, Schultheiss, Prinz & Butera (eds.),
Springer
[9] Lazar & Slutskiy (2012), Channel Identication Machines, Computational Intelligence and Neuroscience
2012:1–20
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