Back

Dynamics of burst synchronization induced by excitatory inputs on midbrain dopamine neurons

chen, m.

2023-12-28 neuroscience
10.1101/2023.12.28.573502 bioRxiv
Show abstract

Dopamine (DA) signals play critical roles in reward-related behavior, decision making, and learning. Yet the mainstream notion that DA signals are encoded by the temporal dynamics of individual DA cell activity is increasingly contested with data supporting that DA signals prefer to be encoded by the spatial organization of DA neuron populations. However, how distributed and parallel excitatory afferent inputs simultaneously induce burst synchronization (BS) is unclear. Our previous work implies that the burst could presumably transition from an integrator to a resonator if the excitatory inputs increase further. Here the responses of networked DA neurons to different intensity of excitatory inputs are investigated. It is found that as NMDA conductance increases, the network will transition from resting state to burst asynchronization (BA) state and then to BS state, showing a bounded BA and BS region in the NMDA conductance space. Furthermore, it is found that as muscarinic receptors modulated Ca2+ dependent cationic (CAN) conductance increases, both boundaries between resting and BA, and between BA and BS gradually decrease. Phase plane analysis on DA reduced model unveils that the burst transition to a resonator underpins the changes in the network dynamics. Slow-fast dissection analysis on DA full model uncovers that the underlying mechanism of the roles and synergy of NMDA and muscarinic receptors in inducing the burst transition emerge from the enlargement of nonlinear positive feedback relationship between more Ca2+ influx provided by additional NMDA current and more ICAN modulated by added muscarinic receptors. Moreover, the lag in DA volume transmission has no effect on excitatory inputs-elicited resonator BS except for requiring more excitatory inputs. These findings shed new lights on understanding the collective behavior of DA cells population regulated by the distributed excitatory inputs, and might provide a new perspective for understanding the abnormal DA release in pathological states. Author summaryThe importance of DA signals is beyond doubt, so their encoding mechanism has very important biological significance and draws widespread attention. Yet the mainstream notion that DA cells individual provide a uniform, broadly distributed signal is increasingly contested with data supporting both homogeneity across dopamine cell activity and diversity in DA signals in target regions. Our article proposes that diverse distributed and parallel excitatory inputs can not only regulate the temporal dynamics of individual DA cell activity, but also simultaneously and synergistically regulate the network dynamics of DA cell populations by changing the local dynamics of DA cells, namely the burst transition from integrators to resonators. According to our perspective, many data that are difficult to interpret by the notion of the DA neuron individual coding can be well explained, such as burst asynchronization coding DA ramping signals, the scale of burst synchronization coding the amplitude of phase DA release, inhibitory DA autoreceptors facilitating resonator burst synchronization by postinhibitory rebound, etc. This study aims to elucidate the working mechanism of the DA system in physiological states such as positive reinforcement, and then to provide a new research perspective and foundation for understanding the abnormal DA release in pathological states.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Chaos, Solitons & Fractals
32 papers in training set
Top 0.1%
12.4%
2
Cognitive Neurodynamics
15 papers in training set
Top 0.1%
12.4%
3
PLOS Computational Biology
1633 papers in training set
Top 4%
8.4%
4
Neural Networks
32 papers in training set
Top 0.1%
6.8%
5
Neuroscience
88 papers in training set
Top 0.1%
6.4%
6
Frontiers in Computational Neuroscience
53 papers in training set
Top 0.6%
4.2%
50% of probability mass above
7
Frontiers in Systems Neuroscience
19 papers in training set
Top 0.1%
3.6%
8
Frontiers in Neural Circuits
36 papers in training set
Top 0.1%
2.4%
9
Scientific Reports
3102 papers in training set
Top 50%
2.1%
10
Neuroscience Bulletin
11 papers in training set
Top 0.2%
2.1%
11
PLOS ONE
4510 papers in training set
Top 48%
2.1%
12
Brain Research
35 papers in training set
Top 0.6%
1.9%
13
eneuro
389 papers in training set
Top 5%
1.7%
14
International Journal of Molecular Sciences
453 papers in training set
Top 8%
1.7%
15
eLife
5422 papers in training set
Top 42%
1.7%
16
iScience
1063 papers in training set
Top 16%
1.7%
17
Neuroscience Research
14 papers in training set
Top 0.1%
1.5%
18
Frontiers in Neuroscience
223 papers in training set
Top 5%
1.3%
19
Neurocomputing
13 papers in training set
Top 0.3%
1.2%
20
Progress in Neurobiology
41 papers in training set
Top 1%
1.2%
21
Brain Sciences
52 papers in training set
Top 1%
1.2%
22
Network Neuroscience
116 papers in training set
Top 0.9%
0.9%
23
Journal of Computational Neuroscience
23 papers in training set
Top 0.3%
0.9%
24
Journal of Theoretical Biology
144 papers in training set
Top 1%
0.9%
25
Neuroscience Letters
28 papers in training set
Top 1%
0.8%
26
Physical Review E
95 papers in training set
Top 1%
0.7%
27
Frontiers in Aging Neuroscience
67 papers in training set
Top 3%
0.7%
28
Computers in Biology and Medicine
120 papers in training set
Top 5%
0.7%
29
Schizophrenia Research
29 papers in training set
Top 0.6%
0.7%
30
Neural Computation
36 papers in training set
Top 0.9%
0.6%