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Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice
Published on: June 29, 2018
Theta phase precession and phase selectivity: a cognitive device description of neural coding
Osbert C Zalay1, Berj L Bardakjian
1Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada. oz.zalay@utoronto.ca
Journal of Neural Engineering
|May 14, 2009
Summary
The cognitive rhythm generator (CRG) models neural coding transformations. Nonlinear modal mixing in CRG networks explains how network connectivity shapes neural dynamics and phase precession.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Systems Neuroscience
Background:
- Neural information processing relies on phase and rate codes.
- Biophysical mechanisms at cellular and network levels transform neuronal signals.
- Mathematical models are needed to represent neural coding transformations.
Purpose of the Study:
- To introduce the cognitive rhythm generator (CRG) as a model for neural coding transformations.
- To investigate coding functionality related to neuronal phase preference and theta precession using CRG networks.
- To explore the role of nonlinear modal mixing in shaping neural dynamics and network behavior.
Main Methods:
- Developing and utilizing a cognitive rhythm generator (CRG) model.
- Parsing incoming signals through neuronal modes for proportional, integrative, and derivative transformations.
- Mixing mode outputs via static nonlinearities to encode spatio-temporal phase relationships.
- Modulating a ring device (limit cycle) for output dynamics.
- Creating small coupled CRG networks to study phase preference and theta precession.
- Implementing nonlinear system identification to validate the model and explain response characteristics.
- Inserting experimentally derived principal dynamic modes from hippocampal neurons into the CRG.
Main Results:
- Phase selectivity in CRG networks was dependent on mode shape and polarity.
- Phase precession emerged as a result of modal mixing, where shifting mode contributions altered phase preference.
- CRG networks with disynaptic feedforward inhibition and excitation showed frequency-dependent inhibitory-to-excitatory and excitatory-to-inhibitory transitions.
- The model successfully cloned the dynamic response of a hippocampal neuron when its principal dynamic modes were inserted.
- Nonlinear modal mixing was identified as a key mechanism by which network connectivity shapes system dynamics.
Conclusions:
- Nonlinear modal mixing is a crucial aspect of neural coding, reflecting the influence of network connectivity on system dynamics.
- CRG networks with disynaptic feedforward inhibition exhibit frequency-selective properties relevant to interpreting rate codes.
- Circuits with disynaptic feedforward inhibition are potential candidates for processing upstream rate codes to influence downstream processes like phase precession.
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