Inhibition synchronizes sparsely connected cortical neurons within and between columns in realistic network models

Abstract

Abstract.Networks of compartmental model neurons were used to investigate the biophysical basis of the synchronization observed between sparsely-connected neurons in neocortex. A model of a single column in layer 5 consisted of 100 model neurons: 80 pyramidal and 20 inhibitory. The pyramidal cells had conductances that caused intrinsic repetitive bursting at different frequencies when driven with the same input. When connected randomly with a connection density of lo%, a single model column displayed synchronous oscillatory action potentials in response to stationary, uncorrelated Poisson spike-train inputs. Synchrony required a high ratio of inhibitory to excitatory synaptic strength; the optimal ratio was 4 : 1, within the range observed in cortex. The synchrony was insensitive to variation in amplitudes of postsynaptic potentials and synaptic delay times, even when the mean synaptic delay times were varied over the range 1 to 7 ms. Synchrony was found to be sensitive to the strength of reciprocal inhibition between the inhibitory neurons in one column: Too weak or too strong reciprocal inhibition degraded intra-columnar synchrony. The only parameter that affected the oscillation frequency of the network was the strength of the external driving input which could shift the frequency between 35 to 60 Hz. The same results were obtained using a model column of 1000 neurons with a connection density of 5%, except that the oscillation became more regular.Synchronization between cortical columns was studied in a model consisting of two columns with 100 model neurons each. When connections were made with a density of 3% between the pyramidal cells of each column there was no inter-columnar synchrony and in some cases the columns oscillated 180" out of phase with each other. Only when connections from the pyramidal cells in each column to the inhibitory cells in the other column were added was synchrony between the columns observed. This synchrony was established within one or two cycles of the oscillation and there was on average less than 1 ms phase difference between the two columns. Unlike the intra-columnar synchronization, the inter-columnar synchronization was found to be sensitive to the synaptic delay: A mean delay of greater than 5 ms virtually abolished synchronization between columns.

From affective blindsight to emotional consciousness

“…Likewise, spontaneous facial reactions occur faster for unseen than seen facial or bodily expressions in blindsight patients (Tamietto et al, 2009), and peripheral arousal, as indexed by pupil dilation, is enhanced for unaware stimuli in both, patients with affective blindsight or patients with neglect (Tamietto et al, 2015). This enhanced influence of nonconscious emotions is in line with evidence that cortical activity may exert an inhibitory modulation over subcortical areas (Bush & Sejnowski, 1996). Absence or reduction of such inhibition during nonconscious perception of emotional stimuli could also explain the apparently paradoxical finding that subcortical activity can be enhanced during nonconscious compared to conscious perception of emotional stimuli in healthy subjects (Anderson, Christoff, Panitz, De Rosa, & Gabrieli, 2003;Williams, Morris, McGlone, Abbott, & Mattingley, 2004).…”

Dynamical Mean Field approximation of a canonical cortical model for studying inter-population synchrony

“…However, the maximum frequency of membrane potential generated at inhibitory neurons cluster (population 3) emerge as the main responsible of synchronization ratio between population 1 and 3, and population 2 and 3. If we take into account that the inhibitory population is considerably small than the other populations, these results demonstrate the active role of inhibition on synchronization process and are in agreement with previous results who propose that inhibition play an important role in neural synchrony [23,24] and consequently in a wide variety of cognitive process (reviewed in 20) as well as in neurological dysfunctions and disorders as epilepsy [25] and Parkinson's disease [26]. A limitation of our model is the absence of delays in the mathematical formulation since signaling between populations is not instantaneous.…”

Derivation and Analysis of Basic Computational Operations of Thalamocortical Circuits

“…There is strong evidence for ascending influences (e.g., basal forebrain) on inhibitory neurons (Freund and Meskenaite, 1992;Gulyas et al, 1996;Blasco-Ibanez et al, 1998;Gulyas et al, 1999) modulating their response properties, in turn affecting the probability of response of excitatory cells during the peaks and troughs of such "clocked" inhibitory cycles. Evidence of intrinsic rhythmic currents in thalamic and cortical cells (Kim et al, 1995;Bush and Sejnowski, 1996;Destexhe et al, 1999;Zhu and Connors, 1999) is compatible with extrinsic ascending influences, acting either independently or in concert with them. Three modes of activity have typically been reported for thalamic neurons: tonic, rhythmic, and arrhythmic bursting.…”