- Oral presentation
- Open Access
Stimulus-dependent suppression of intrinsic variability in recurrent neural networks
© Rajan et al; licensee BioMed Central Ltd. 2010
- Published: 20 July 2010
- Neural Response
- Recurrent Neural Network
- Chaotic State
- Stochastic Input
- Chaotic Pattern
Trial-to-trial variability is an essential feature of neural responses and is likely to arise from a complex interaction between stimulus-evoked activity and ongoing spontaneous neural activity in the central nervous system. Response variability is often treated as random noise generated either by an external source like another brain area, or by stochastic processes within the circuit. A considerable amount of variability can also arise from the same circuitry and intrinsic network dynamics that generate responses to a stimulus. Indeed ongoing neural activity in the central nervous system is comparable in magnitude and complexity to activity evoked by sensory stimuli [1, 2].
How can we distinguish between external and internal sources of neuronal variability? We ask whether internal and external sources of variability depend on stimulus features in different ways, giving them distinct experimental signatures and functional interpretations. How are stimulus-evoked responses faithfully extracted from complex background activity to identify real features of the external world?
We also show that the nonlinear interaction between the relatively slow intrinsic fluctuations and external stimulus results in a non-monotonic frequency dependence of this suppression. Consequently, measures of trial-to-trial variability of neural responses can be more sensitive to the amplitude and frequency of the stimulus, compared to the mean responses that are typically the focus of electrophysiological studies.
- Please find detailed methodology as well as relevant references in the supplement to this abstract.Google Scholar
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