Abstract
When exposed to rewarding stimuli, only some animals develop persistent craving. Others are resilient and do not. How the activity of neural populations relates to the development of persistent craving behavior is not fully understood. Previous computational studies suggest that synchrony helps a network embed certain patterns of activity, although the role of synchrony in reward-dependent learning has been less studied. Increased synchrony has been reported as a marker for both susceptibility and resilience to developing persistent craving. Here we use computational simulations to study the effect of reward salience on the ability of synchronous input to embed a new pattern of activity into a neural population. Our main finding is that weak stimulus-reward correlations can facilitate the short-term repetition of a pattern of neural activity, while blocking long-term embedding of that pattern. Interestingly, synchrony did not have this dual effect on all patterns, which suggests that synchrony is more effective at embedding some patterns of activity than others. Our results demonstrate that synchrony can have opposing effects in networks sensitive to the correlation structure of their inputs, in this case the correlation between stimulus and reward. This work contributes to an understanding of the interplay between synchrony and reward-dependent plasticity.
Highlights
Synchrony refers to a coordinated pattern of network activity
This paper discussed the ability of a computational model of neural population dynamics with activity-dependent plasticity to maintain preset patterns of activity in the face of different stimulus-reward patterns
We found that a tonic stimulus, modeling chronic exposure, was most effective in destabilizing the network
Summary
Synchrony refers to a coordinated pattern of network activity. Synchrony occurs between (i) action potentials, (ii) local field potentials, or (iii) action potentials and local field potentials. The latter two types of synchrony are frequently called coherence. Neural networks with strong recurrent connections can demonstrate synchronous activity that persists over seconds to minutes (Tetzlaff et al, 2012). Changing synaptic strengths allows that activity to persist over longer time scales (Holtmaat and Svoboda, 2009). Synchrony between action potentials helps localize sounds (Joris et al, 1998), signal the direction of motion (Meister et al, 1995; Meister and Berry, 1999), and discriminate among odors (Stopfer et al, 1997; Tetzlaff et al, 2012)
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