Abstract
The frequency-intensity receptive fields (RF) of neurons in primary auditory cortex (AI) are heterogeneous. Some neurons have V-shaped RFs, whereas others have enclosed ovoid RFs. Moreover, there is a wide range of temporal response profiles ranging from phasic to tonic firing. The mechanisms underlying this diversity of receptive field properties are yet unknown. Here we study the characteristics of thalamocortical (TC) and intracortical connectivity that give rise to the individual cell responses. Using a mouse auditory TC slice preparation, we found that the amplitude of synaptic responses in AI varies non-monotonically with the intensity of the stimulation in the medial geniculate nucleus (MGv). We constructed a network model of MGv and AI that was simulated using either rate model cells or in vitro neurons through an iterative procedure that used the recorded neural responses to reconstruct network activity. We compared the receptive fields and firing profiles obtained with networks configured to have either cotuned excitatory and inhibitory inputs or relatively broad, lateral inhibitory inputs. Each of these networks yielded distinct response properties consistent with those documented in vivo with natural stimuli. The cotuned network produced V-shaped RFs, phasic-tonic firing profiles, and predominantly monotonic rate-level functions. The lateral inhibitory network produced enclosed RFs with narrow frequency tuning, a variety of firing profiles, and robust non-monotonic rate-level functions. We conclude that both types of circuits must be present to account for the wide variety of responses observed in vivo.
Original language | English (US) |
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Pages (from-to) | 9151-9163 |
Number of pages | 13 |
Journal | Journal of Neuroscience |
Volume | 28 |
Issue number | 37 |
DOIs | |
State | Published - Sep 10 2008 |
Keywords
- Computational model
- Frequency tuning
- Intensity tuning
- Lateral inhibition
- Network dynamics
- Primary auditory cortex
- Thalamocortical transformation
ASJC Scopus subject areas
- General Neuroscience