Function Of Cortical Microcircuits Research Articles

Spatial organization of neuron-astrocyte interactions in the somatosensory cortex.

Microcircuits in the neocortex are functionally organized along layers and columns, which are the fundamental modules of cortical information processing. While the function of cortical microcircuits has focused on neuronal elements, much less is known about the functional organization of astrocytes and their bidirectional interaction with neurons. Here, we show that Cannabinoid type 1 receptor (CB1R)-mediated astrocyte activation by neuron-released endocannabinoids elevate astrocyte Ca2+ levels, stimulate ATP/adenosine release as gliotransmitters, and transiently depress synaptic transmission in layer 5 pyramidal neurons at relatively distant synapses (˃20μm) from the stimulated neuron. This astrocyte-mediated heteroneuronal synaptic depression occurred between pyramidal neurons within a cortical column and was absent in neurons belonging to adjacent cortical columns. Moreover, this form of heteroneuronal synaptic depression occurs between neurons located in particular layers, following a specific connectivity pattern that depends on a layer-specific neuron-to-astrocyte signaling. These results unravel the existence of astrocyte-mediated nonsynaptic communication between cortical neurons and that this communication is column- and layer-specific, which adds further complexity to the intercellular signaling processes in the neocortex.

Inhibitory Interneurons Decorrelate Excitatory Cells to Drive Sparse Code Formation in a Spiking Model of V1

Sparse coding models of natural scenes can account for several physiological properties of primary visual cortex (V1), including the shapes of simple cell receptive fields (RFs) and the highly kurtotic firing rates of V1 neurons. Current spiking network models of pattern learning and sparse coding require direct inhibitory connections between the excitatory simple cells, in conflict with the physiological distinction between excitatory (glutamatergic) and inhibitory (GABAergic) neurons (Dale's Law). At the same time, the computational role of inhibitory neurons in cortical microcircuit function has yet to be fully explained. Here we show that adding a separate population of inhibitory neurons to a spiking model of V1 provides conformance to Dale's Law, proposes a computational role for at least one class of interneurons, and accounts for certain observed physiological properties in V1. When trained on natural images, this excitatory-inhibitory spiking circuit learns a sparse code with Gabor-like RFs as found in V1 using only local synaptic plasticity rules. The inhibitory neurons enable sparse code formation by suppressing predictable spikes, which actively decorrelates the excitatory population. The model predicts that only a small number of inhibitory cells is required relative to excitatory cells and that excitatory and inhibitory input should be correlated, in agreement with experimental findings in visual cortex. We also introduce a novel local learning rule that measures stimulus-dependent correlations between neurons to support "explaining away" mechanisms in neural coding.

Inhibitory interneurons enable sparse code formation in a spiking circuit model of V1

Sparse coding accounts for several physiological properties of primary visual cortex (V1), including the shapes of simple cell receptive fields and the highly kurtotic firing rates of V1 neurons [1]. Current spiking network models of pattern learning [2] and sparse coding [3] require direct inhibitory connections between the excitatory simple cells, in violation of Dale's Law which states that neurons can either excite or inhibit but not both. At the same time, the computational role of inhibitory neurons in cortical microcircuit function has yet to be fully explained. Here we show that adding a separate population of inhibitory neurons to a recently proposed model of V1 [3] not only brings spiking network models of sparse coding in line with Dale’s Law, but it also predicts excitatory-to-inhibitory neuron ratios and explains how inhibitory neurons may function computationally. When trained on natural images, this excitatory-inhibitory spiking circuit learns Gabor-like receptive fields as found in V1 using spiking neurons and synaptically local plasticity rules. The inhibitory cells enable sparse code formation using a novel learning rule by collaboratively discovering and suppressing correlations within the excitatory population (Figure ​(Figure1).1). The model predicts that only a small number of inhibitory cells is required relative to excitatory cells, matching physiological ratios observed in primary visual cortex. Figure 1 A. Circuit diagram of our spiking network with separate excitatory (E) and inhibitory (I) neural populations (top) compared to current single population models (bottom). This network was simulated with different numbers of excitatory and inhibitory cells. ...

Matching synaptic type with postsynaptic firing class shapes the encoding of either stimulus rate or rate change

The synergy between synaptic and postsynaptic firing dynamics in shaping neuronal encoding has not been explored. We show that by matching short-term synaptic dynamics with postsynaptic firing class, either stimulus rate or a rate change are encoded. The result may be relevant to understand the function of cortical microcircuits. Short-term synaptic dynamics and firing dynamics of neurons can each be classified into two types. In short-term plasticity, type 1 synapses show release-dependent depression and constant rate of recovery. They appear to encode a stimulus rate change. Type 2 synapses show release-independent depression and faster recovery at higher stimulus frequencies. They can follow the stimulus rate [1]. Postsynaptic firing characteristics conform either to class 1 or 2 on the basis of phase-reset curves (PRCs) [2]. Class 1 neurons can fire at arbitrarily low frequencies and do not exhibit spike frequency adaptation. Class 2 neurons exhibit subthreshold oscillations and spike frequency adaption. We investigate if different combinations of synaptic and postsynaptic dynamics can shape neuronal encoding. Using NEURON, we simulated biophysically realistic cells with class 1 or 2 firing dynamics [3] that receive 2000 unsynchronized type 1 or 2 synaptic inputs [1]. When class 1 neurons are matched with type 2 synaptic inputs the average synaptic stimulus rate is encoded in the firing rate (Fig. 1A,1B). When class 2 neurons and type 1 inputs are matched, the rate change but not its magnitude is encoded (Fig. 1C ,1D). For unmatched cases, rate and rate change are encoded to different extents (Fig. 1E-1H), suggesting both synaptic and postsynaptic dynamics shape encoding. Figure 1 Spiking response when a class 1 neuron is matched with type 2 synaptic inputs (A,B) upon altering stimulus rate (below). Class 2 neuron matched with type 1 synaptic inputs (C,D). Class 1 neuron receiving type 1 synaptic inputs (unmatched; E,F). Class ... Spiny stellate cells in the layer IV microcircuit preferentially receive inputs from type 1 synapses while star pyramidal neurons receive type 2 inputs [4]. Even though the PRC for these cell types have not been characterized yet, our results pinpoint the possibility that in layer IV rate changes are encoded in the network of spiny stellate and stimulus rate in that of star pyramidal cells.