Synaptic depression and slow oscillatory activity in a biophysical network model of the cerebral cortex

Abstract

Short-term synaptic depression (STD) is a form of synaptic plasticity that has a large impact on network computations. Experimental results suggest that STD is modulated by cortical activity, decreasing with activity in the network and increasing during silent states. Here, we explored different activity-modulation protocols in a biophysical network model for which the model displayed less STD when the network was active than when it was silent, in agreement with experimental results. Furthermore, we studied how trains of synaptic potentials had lesser decay during periods of activity (UP states) than during silent periods (DOWN states), providing new experimental predictions. We next tackled the inverse question of what is the impact of modifying STD parameters on the emergent activity of the network, a question difficult to answer experimentally. We found that synaptic depression of cortical connections had a critical role to determine the regime of rhythmic cortical activity. While low STD resulted in an emergent rhythmic activity with short UP states and long DOWN states, increasing STD resulted in longer and more frequent UP states interleaved with short silent periods. A still higher synaptic depression set the network into a non-oscillatory firing regime where DOWN states no longer occurred. The speed of propagation of UP states along the network was not found to be modulated by STD during the oscillatory regime; it remained relatively stable over a range of values of STD. Overall, we found that the mutual interactions between synaptic depression and ongoing network activity are critical to determine the mechanisms that modulate cortical emergent patterns.

Bifurcation Analysis on Phase-Amplitude Cross-Frequency Coupling in Neural Networks with Dynamic Synapses

“…Synaptic depression triggers a large fluctuation in sustained periods between the up and down states (Mejias et al, 2010), and the level of the synaptic depression changes the property of the sustained activities of these two different states (Benita et al, 2012). Moreover, the synaptic depression contributes to the destabilization of network activity, the generation of an oscillatory state, and the spontaneous state transitions among multiple patterns in an associative memory network (Katori et al, 2013).…”

Modulation of cortical slow oscillatory rhythm by GABABreceptors: an experimental and computational study

“…Such currents would be Ca 2+ and Na + -dependent K + currents (Compte et al, 2003;Sanchez-Vives et al, 2010) or AMPc-dependent potassium currents (Cunningham et al, 2006). Hyperpolarizing currents can also interact with other mechanisms such as synaptic depression, modulating the emerging patterns (Benita et al, 2012). Adaptation has also been considered in the dynamics of Up/Down states as a necessary mechanism, but in a more ample sense, such that it could include either hyperpolarizing ionic currents but also synaptic inhibition (Mattia and Sanchez-Vives, 2012).…”

“…However, a modeling framework where a simpler activitydependent adaptation is responsible for the Up-to-Down state transitions produces, in the absence of GABAB receptors, oscillations as regular as those observed in the slices. Our model considers firing rate adaptation and slow inhibition by GABAB as the two biophysical elements determining the SWO; another plausible mechanism is short-term synaptic dynamics (Timofeev et al, 2000;Melamed et al, 2008;Benita et al, 2012); however, we did not need it to explain the slice behavior.…”

“…In order to understand spontaneous SWO, several studies have proposed potential mechanisms responsible for their generation (transition from Down to Up states), maintenance, and termination (transition from Up to Down states). In terms of finalization, four main different mechanisms have been proposed that mediate the transition from Up to Down states: firing rate adaptation (Compte et al, 2003;Sanchez-Vives et al, 2010), short-term synaptic dynamics (Timofeev et al, 2000;Melamed et al, 2008;Benita et al, 2012), ATP-dependent homeostatic mechanisms mediated by ATP-modulated potassium (KATP) channels (Cunningham et al, 2006), and pre-and post-synaptic GABAB (gamma-aminobutyric acid B) receptor activation (Parga and Abbott, 2007;Mann et al, 2009;Wang et al, 2010;Craig et al, 2013) (Sanchez-Vives et al, in review). It is plausible that more than one of these mechanisms interact and contribute to the termination of the Up-to-Down state transition; however, this is not yet well understood due to a paucity of experimental and modeling work addressing this issue.…”

Emerging phenomena in neural networks with dynamic synapses and their computational implications

“…However, a very recent study has focused on the interplay between synaptic depression and these inhibitory currents and concludes that synaptic depression is relevant for maintaining the up state (Benita et al, 2012). The reason for that counterintuitive behavior is that synaptic depression decreases the firing rate in the up state which also decreases the effect of the hyper-polarizing potassium currents and, as a consequence, the prolongation of the up state.…”