PMID- 31335294
OWN - NLM
STAT- MEDLINE
DCOM- 20200915
LR  - 20200915
IS  - 1530-888X (Electronic)
IS  - 0899-7667 (Linking)
VI  - 31
IP  - 9
DP  - 2019 Sep
TI  - Sensitivity to Stimulus Irregularity Is Inherent in Neural Networks.
PG  - 1789-1824
LID - 10.1162/neco_a_01215 [doi]
AB  - Behavior is controlled by complex neural networks in which neurons process 
      thousands of inputs. However, even short spike trains evoked in a single cortical 
      neuron were demonstrated to be sufficient to influence behavior in vivo. 
      Specifically, irregular sequences of interspike intervals (ISIs) had a more 
      reliable influence on behavior despite their resemblance to stochastic activity. 
      Similarly, irregular tactile stimulation led to higher rates of behavioral 
      responses. In this study, we identify the mechanisms enabling this sensitivity to 
      stimulus irregularity (SSI) on the neuronal and network levels using simulated 
      spiking neural networks. Matching in vivo experiments, we find that irregular 
      stimulation elicits more detectable network events (bursts) than regular 
      stimulation. Dissecting the stimuli, we identify short ISIs-occurring more 
      frequently in irregular stimulations-as the main drivers of SSI rather than 
      complex irregularity per se. In addition, we find that short-term plasticity 
      modulates SSI. We subsequently eliminate the different mechanisms in turn to 
      assess their role in generating SSI. Removing inhibitory interneurons, we find 
      that SSI is retained, suggesting that SSI is not dependent on inhibition. 
      Removing recurrency, we find that SSI is retained due to the ability of 
      individual neurons to integrate activity over short timescales ("cell memory"). 
      Removing single-neuron dynamics, we find that SSI is retained based on the 
      short-term retention of activity within the recurrent network structure ("network 
      memory"). Finally, using a further simplified probabilistic model, we find that 
      local network structure is not required for SSI. Hence, SSI is identified as a 
      general property that we hypothesize to be ubiquitous in neural networks with 
      different structures and biophysical properties. Irregular sequences contain 
      shorter ISIs, which are the main drivers underlying SSI. The experimentally 
      observed SSI should thus generalize to other systems, suggesting a functional 
      role for irregular activity in cortex.
FAU - van Gils, Teun
AU  - van Gils T
AD  - Department of Neuroinformatics and Department of Neurophysiology, Donders 
      Institute for Brain, Cognition, and Behaviour, 6525 AJ Nijmegen, Gelderland, The 
      Netherlands edu@teunvg.nl.
FAU - Tiesinga, Paul H E
AU  - Tiesinga PHE
AD  - Department of Neuroinformatics, Donders Institute for Brain, Cognition, and 
      Behaviour, 6525 AJ Nijmegen, Gelderland, The Netherlands 
      p.tiesinga@science.ru.nl.
FAU - Englitz, Bernhard
AU  - Englitz B
AD  - Department of Neurophysiology, Donders Institute for Brain, Cognition, and 
      Behaviour, 6525 AJ Nijmegen, Gelderland, The Netherlands B.Englitz@science.ru.nl.
FAU - Martens, Marijn B
AU  - Martens MB
AD  - Department of Neuroinformatics, Donders Institute for Brain, Cognition, and 
      Behaviour, 6525 AJ Nijmegen, Gelderland, The Netherlands 
      martens.marijn@gmail.com.
LA  - eng
PT  - Letter
PT  - Research Support, Non-U.S. Gov't
DEP - 20190723
PL  - United States
TA  - Neural Comput
JT  - Neural computation
JID - 9426182
SB  - IM
MH  - Action Potentials/physiology
MH  - Animals
MH  - Nerve Net/cytology/*physiology
MH  - *Neural Networks, Computer
MH  - Neurons/*physiology
MH  - Somatosensory Cortex/cytology/*physiology
MH  - Synapses/physiology
EDAT- 2019/07/25 06:00
MHDA- 2020/09/17 06:00
CRDT- 2019/07/24 06:00
PHST- 2019/07/25 06:00 [pubmed]
PHST- 2020/09/17 06:00 [medline]
PHST- 2019/07/24 06:00 [entrez]
AID - 10.1162/neco_a_01215 [doi]
PST - ppublish
SO  - Neural Comput. 2019 Sep;31(9):1789-1824. doi: 10.1162/neco_a_01215. Epub 2019 Jul 
      23.