4.7 Article

Dynamical Response Properties of Neocortical Neuron Ensembles: Multiplicative versus Additive Noise

Journal

JOURNAL OF NEUROSCIENCE
Volume 29, Issue 4, Pages 1006-1010

Publisher

SOC NEUROSCIENCE
DOI: 10.1523/JNEUROSCI.3424-08.2009

Keywords

transfer function; linear response; population rate; multiplicative noise; pyramidal cell; ensemble

Categories

Funding

  1. German Federal Ministry of Education and Research [01GQ0420]
  2. European Union [15879]
  3. Research Council of Norway (eVITA program)

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To understand the mechanisms of fast information processing in the brain, it is necessary to determine how rapidly populations of neurons can respond to incoming stimuli in a noisy environment. Recently, it has been shown experimentally that an ensemble of neocortical neurons can track a time-varying input current in the presence of additive correlated noise very fast, up to frequencies of several hundred hertz. Modulations in the firing rate of presynaptic neuron populations affect, however, not only the mean but also the variance of the synaptic input to postsynaptic cells. It has been argued that such modulations of the noise intensity (multiplicative modulation) can be tracked much faster than modulations of the mean input current (additive modulation). Here, we compare the response characteristics of an ensemble of neocortical neurons for both modulation schemes. We injected sinusoidally modulated noisy currents (additive and multiplicative modulation) into layer V pyramidal neurons of the rat somatosensory cortex and measured the trial and ensemble-averaged spike responses for a wide range of stimulus frequencies. For both modulation paradigms, we observed low-pass behavior. The cutoff frequencies were markedly high, considerably higher than the average firing rates. We demonstrate that modulations in the variance can be tracked significantly faster than modulations in the mean input. Extremely fast stimuli (up to 1 kHz) can be reliably tracked, provided the stimulus amplitudes are sufficiently high.

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