Volume 16 Supplement 1

24th Annual Computational Neuroscience Meeting: CNS*2015

Open Access

Surround suppression and normalization in a model of coupled balanced cortical networks with short-term synaptic plasticity

BMC Neuroscience201516(Suppl 1):P249


Published: 18 December 2015


The input currents to neurons in the cortex are under many circumstances highly variable with a mean intensity considerably below the firing threshold. As a consequence, the spiking activity of cortical neurons is strongly fluctuating such that the coefficient of variation of the inter-spike interval distribution of individual neurons is approximately equal to 1, implying almost Poisson-like spiking. In addition to this characteristic activity, the connectivity of excitatory and inhibitory neurons in cortex is sparse and irregular. It has been shown in models that super-threshold excitatory and strong inhibitory input currents, which nearly cancel for individual neurons, can lead to this irregular spiking activity [1]. However, balanced networks of excitatory and inhibitory neurons are characterized by a strictly linear relation between stimulus strength and network firing rate, making it hard to perform more complex computational tasks like the generation of receptive fields, multiple stable activity states or normalization, which for example has been measured in visual cortex (eg. [2, 3]).

Synapses displaying activity dependent short-term plasticity (STP) have been previously reported to give rise to a non-linear network response with potentially multiple stable states for a given stimulus [4].

In this study, we analyze analytically and numerically the computational properties of two interconnected balanced networks, receiving independent stationary stimuli. This situation can be viewed as a simple instantiation of two interconnected cortical columns. For an illustration of the network topology, see Figure 1A. We demonstrate that these stimuli are normalized by the system and that increasing the stimulus to one network, suppresses the activity of the neighboring network (see Figure 1B). Thereby, normalization and suppression are linear in stimulus strength when STP is disabled and becomes non-linear with activity dependent synapses.
Figure 1

A. Schematic illustration of the network topology. E1 and I1 represent excitatory and inhibitory neuronal populations of network 1, receiving input from an external population X1. Same for network 2. Coupling of neuronal populations is indicated by the arrows, synaptic strengths by the Jyz. B. Schematic illustration of the resulting activity in the network for constant stimulus X1 and varying stimulus X2. In this model we observe the activity of network 1 being suppressed when activity in network 2 increases. Colors of lines represent the population corresponding to panel A.

Authors’ Affiliations

Theory of Neural Dynamics, Max-Planck Institute for Brain Research


  1. van Vreeswijk C, Sompolinsky H: Chaotic Balanced State in a Model of Cortical Circuits. Neural Comput. 1998, 10 (6): 1321-1371.PubMedView ArticleGoogle Scholar
  2. Carandini M, Heeger DJ, Movshon JA: Linearity and normalization in simple cells of the macaque primary visual cortex. J Neurosci. 1997, 17 (21): 8621-8644.PubMedGoogle Scholar
  3. Levitt JB, Kiper DC, Movshon JA: Receptive fields and functional architecture of macaque V2. J Neurophysiol. 1994, 71 (6): 2517-2542.PubMedGoogle Scholar
  4. Mongillo G, Hansel D, van Vreeswijk C: Bistability and spatiotemporal irregularity in neuronal networks with nonlinear synaptic transmission. Phys Rev Lett. 2012, 108 (15): 158101-PubMedView ArticleGoogle Scholar


© Konrad and Tchumatchenko 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.