Do Neocortical Pyramidal Neurons Display Stochastic Resonance?

• Published in: Journal of computational neuroscience Vol. 11; no. 1; pp. 19 - 42
• Main Authors: Rudolph, Michael, Destexhe, Alain
• Format: Journal Article
• Language: English
• Published: United States Springer Nature B.V 01.07.2001; Springer Verlag
• Subjects: Action Potentials; Action Potentials - physiology; Animals; Biological Clocks; Biological Clocks - physiology; Cats; Cell Membrane; Cell Membrane - physiology; Excitatory Postsynaptic Potentials; Excitatory Postsynaptic Potentials - physiology; Ion Channels; Ion Channels - physiology; Life Sciences; Models, Neurological; Neocortex; Neocortex - cytology; Neocortex - physiology; Nerve Net; Nerve Net - cytology; Nerve Net - physiology; Neural Conduction; Neural Conduction - physiology; Neural Inhibition; Neural Inhibition - physiology; Neurons and Cognition; Pyramidal Cells; Pyramidal Cells - cytology; Pyramidal Cells - physiology; Synapses; Synapses - physiology; Synaptic Transmission; Synaptic Transmission - physiology; computational models; noise; cerebral cortex; neocortex; synaptic background activity
• ISSN: 0929-5313; 1573-6873
• DOI: 10.1023/A:1011200713411

Neocortical pyramidal neurons in vivo are subject to an intense synaptic background activity that has a significant impact on various electrophysiological properties and dendritic integration. Using detailed biophysical models of a morphologically reconstructed neocortical pyramidal neuron, in which synaptic background activity was simulated according to recent measurements in cat parietal cortex in vivo, we show that the responsiveness of the cell to additional periodic subthreshold stimuli can be significantly enhanced through mechanisms similar to stochastic resonance. We compare several paradigms leading to stochastic resonance-like behavior, such as varying the strength or the correlation in the background activity. A new type of resonance-like behavior was obtained when the correlation was varied, in which case the responsiveness is sensitive to the statistics rather than the strength of the noise. We suggest that this type of resonance may be relevant to information processing in the cerebral cortex.