Linking dynamics of the inhibitory network to the input structure

Networks of inhibitory interneurons are found in many distinct classes of biological systems. Inhibitory interneurons govern the dynamics of principal cells and are likely to be critically involved in the coding of information. In this theoretical study, we describe the dynamics of a generic inhibitory network in terms of low-dimensional, simplified rate models. We study the relationship between the structure of external input applied to the network and the patterns of activity arising in response to that stimulation. We found that even a minimal inhibitory network can generate a great diversity of spatio-temporal patterning including complex bursting regimes with non-trivial ratios of burst firing. Despite the complexity of these dynamics, the network's response patterns can be predicted from the rankings of the magnitudes of external inputs to the inhibitory neurons. This type of invariant dynamics is robust to noise and stable in densely connected networks with strong inhibitory coupling. Our study predicts that the response dynamics generated by an inhibitory network may provide critical insights about the temporal structure of the sensory input it receives.

Propagation of spontaneous slow-wave activity across columns and layers of the adult rat barrel cortex in vivo

During slow-wave sleep, neocortical networks exhibit self-organized activity switching between periods of concurrent spiking (up-states) and periods of network silence (down-states), a phenomenon also occurring under the effects of different anesthetics and in in vitro brain slice preparations. Although this type of ongoing activity has been implicated into important functions such as memory consolidation and learning, the manner in which it propagates across different cortical modules (i.e., columns and layers) has not been fully characterized. In the present study, we investigated this issue by measuring spontaneous activity at large scale in the adult rat barrel cortex under urethane anesthesia by means of voltage-sensitive dye imaging and 128-channel probe recordings. Up to 74 neurons located in all layers of up to four functionally identified barrel-related columns were recorded simultaneously. The spontaneous activity propagated isotropically across the cortical surface with a median speed of ~35 µm/ms. A concomitant radial spread of activation was present from deep to superficial cortical layers. Thus, spontaneous activity occurred rather globally in the barrel cortex, with ≥50 % of the up-states presenting spikes in ≥3 columns and layers. Temporally precise spike sequences, which occurred repeatedly (although sporadically) within the up-states, were typically led by putative excitatory neurons in the infragranular cortical layers. In summary, our data provide for the first time an overall view of the spontaneous slow-wave activity within the barrel cortex circuit, characterizing its propagation across columns and layers at high spatio-temporal resolution.

Complex spike patterns in olfactory bulb neuronal networks

BACKGROUND

T-pattern analysis is a procedure developed for detecting non-randomly recurring hierarchical and multiordinal real-time sequential patterns (T-patterns).

NEW METHOD

We have inquired whether such patterns of action potentials (spikes) can be extracted from extracellular activity sampled simultaneously from many neurons across the mitral cell layer of the olfactory bulb (OB). Spikes were sampled from urethane-anaesthetized rats over a 6h recording session, or a period lasting as long as permitted by the physiological condition of the animal. Breathing was recorded to mark peak inhalation and exhalation.

RESULTS

Complex T-patterns of up to ∼20 elements were identified with functional connections often spanning the full extent of the array. A considerable proportion of these sequences incorporated breathing.

COMPARISON WITH EXISTING METHODS

In contrast to sequence detection by synfire, the incidence of sequences detected in our real data is very much greater than in the same data when randomized either by shuffling, or an alternative procedure preserving the interval structure of each spike train, and so more conservative. Further, when recordings were terminated before completion of the full recording session, the relative pattern detection in real and randomized data was a strong indicator of physiological condition-in recordings leading up to the preparation becoming physiologically unstable, the number of patterns detected in real data approached that in the randomized data.

CONCLUSIONS

We conclude that such sequences are an important physiological property of the neural system studied, and suggest that they may form a basis for encoding sensory information.