TY  - JOUR
A1  - Bernáez Timón, Laura
A1  - Ekelmans, Pierre
A1  - Konrad, Sara
A1  - Nold, Andreas
A1  - Tchumatchenko, Tatjana
T1  - Synaptic plasticity controls the emergence of population-wide invariant representations in balanced network models
T2  - Physical review research
N2  - The intensity and the features of sensory stimuli are encoded in the activity of neurons in the cortex. In the visual and piriform cortices, the stimulus intensity rescales the activity of the population without changing its selectivity for the stimulus features. The cortical representation of the stimulus is therefore intensity invariant. This emergence of network-invariant representations appears robust to local changes in synaptic strength induced by synaptic plasticity, even though (i) synaptic plasticity can potentiate or depress connections between neurons in a feature-dependent manner, and (ii) in networks with balanced excitation and inhibition, synaptic plasticity determines the nonlinear network behavior. In this study we investigate the consistency of invariant representations with a variety of synaptic states in balanced networks. By using mean-field models and spiking network simulations, we show how the synaptic state controls the emergence of intensity-invariant or intensity-dependent selectivity. In particular, we demonstrate that an effective power-law synaptic transformation at the population level is necessary for invariance. In a range of firing rates, purely depressing short-term synapses fulfills this condition, and in this case, the network is contrast-invariant. Instead, facilitating short-term plasticity generally narrows the network selectivity. We found that facilitating and depressing short-term plasticity can be combined to approximate a power-law that leads to contrast invariance. These results explain how the physiology of individual synapses is linked to the emergence of invariant representations of sensory stimuli at the network level.
KW  - Biological neural networks
KW  - Neuroscience, neural computation & artificial intelligence
KW  - Neuronal network models
KW  - Physics of Living Systems
Y1  - 2022
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/75136
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-751363
SN  - 2643-1564
N1  - Funding: Max Planck Society, University of Bonn Medical Center, University of Mainz Medical Center, and the German Research Foundation ;  CRC 1080.
N1  - Funding: CMMS
VL  - 4
IS  - 1, art. 013162
SP  - 1
EP  - 14
PB  - APS
CY  - College Park, MD
ER  -