PMID- 31158265
OWN - NLM
STAT- MEDLINE
DCOM- 20191202
LR  - 20200309
IS  - 1553-7358 (Electronic)
IS  - 1553-734X (Print)
IS  - 1553-734X (Linking)
VI  - 15
IP  - 6
DP  - 2019 Jun
TI  - Emergent decision-making behaviour and rhythm generation in a computational model 
      of the ventromedial nucleus of the hypothalamus.
PG  - e1007092
LID - 10.1371/journal.pcbi.1007092 [doi]
LID - e1007092
AB  - The ventromedial nucleus of the hypothalamus (VMN) has an important role in 
      diverse behaviours. The common involvement in these of sex steroids, 
      nutritionally-related signals, and emotional inputs from other brain areas, 
      suggests that, at any given time, its output is in one of a discrete number of 
      possible states corresponding to discrete motivational drives. Here we explored 
      how networks of VMN neurons might generate such a decision-making architecture. 
      We began with minimalist assumptions about the intrinsic properties of VMN 
      neurons inferred from electrophysiological recordings of these neurons in rats in 
      vivo, using an integrate-and-fire based model modified to simulate 
      activity-dependent post-spike changes in neuronal excitability. We used a genetic 
      algorithm based method to fit model parameters to the statistical features of 
      spike patterning in each cell. The spike patterns in both recorded cells and 
      model cells were assessed by analysis of interspike interval distributions and of 
      the index of dispersion of firing rate over different binwidths. Simpler 
      patterned cells could be closely matched by single neuron models incorporating a 
      hyperpolarising afterpotential and either a slow afterhyperpolarisation or a 
      depolarising afterpotential, but many others could not. We then constructed 
      network models with the challenge of explaining the more complex patterns. We 
      assumed that neurons of a given type (with heterogeneity introduced by 
      independently random patterns of external input) were mutually interconnected at 
      random by excitatory synaptic connections (with a variable delay and a random 
      chance of failure). Simple network models of one or two cell types were able to 
      explain the more complex patterns. We then explored the information processing 
      features of such networks that might be relevant for a decision-making network. 
      We concluded that rhythm generation (in the slow theta range) and bistability 
      arise as emergent properties of networks of heterogeneous VMN neurons.
FAU - MacGregor, Duncan J
AU  - MacGregor DJ
AUID- ORCID: 0000-0002-3046-6640
AD  - Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, United 
      Kingdom.
FAU - Leng, Gareth
AU  - Leng G
AUID- ORCID: 0000-0002-2388-8466
AD  - Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, United 
      Kingdom.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20190603
PL  - United States
TA  - PLoS Comput Biol
JT  - PLoS computational biology
JID - 101238922
SB  - IM
MH  - Algorithms
MH  - Animals
MH  - Computational Biology
MH  - Decision Making/*physiology
MH  - Male
MH  - *Models, Neurological
MH  - Neurons/cytology/physiology
MH  - Rats
MH  - *Ventromedial Hypothalamic Nucleus/cytology/physiology
PMC - PMC6564049
COIS- The authors have declared that no competing interests exist.
EDAT- 2019/06/04 06:00
MHDA- 2019/12/04 06:00
PMCR- 2019/06/03
CRDT- 2019/06/04 06:00
PHST- 2018/06/28 00:00 [received]
PHST- 2019/05/13 00:00 [accepted]
PHST- 2019/06/13 00:00 [revised]
PHST- 2019/06/04 06:00 [pubmed]
PHST- 2019/12/04 06:00 [medline]
PHST- 2019/06/04 06:00 [entrez]
PHST- 2019/06/03 00:00 [pmc-release]
AID - PCOMPBIOL-D-18-01124 [pii]
AID - 10.1371/journal.pcbi.1007092 [doi]
PST - epublish
SO  - PLoS Comput Biol. 2019 Jun 3;15(6):e1007092. doi: 10.1371/journal.pcbi.1007092. 
      eCollection 2019 Jun.