Hyperactivity with Disrupted Attention by Activation of an Astrocyte Synaptogenic Cue.

Hyperactivity and disturbances of attention are common behavioral disorders whose underlying cellular and neural circuit causes are not understood. We report the discovery that striatal astrocytes drive such phenotypes through a hitherto unknown synaptic mechanism. We found that striatal medium spiny neurons (MSNs) triggered astrocyte signaling via γ-aminobutyric acid B (GABAB) receptors. Selective chemogenetic activation of this pathway in striatal astrocytes in vivo resulted in acute behavioral hyperactivity and disrupted attention. Such responses also resulted in upregulation of the synaptogenic cue thrombospondin-1 (TSP1) in astrocytes, increased excitatory synapses, enhanced corticostriatal synaptic transmission, and increased MSN action potential firing in vivo. All of these changes were reversed by blocking TSP1 effects. Our data identify a form of bidirectional neuron-astrocyte communication and demonstrate that acute reactivation of a single latent astrocyte synaptogenic cue alters striatal circuits controlling behavior, revealing astrocytes and the TSP1 pathway as therapeutic targets in hyperactivity, attention deficit, and related psychiatric disorders.

Pop-out search instigates beta-gated feature selectivity enhancement across V4 layers.

Visual search is a workhorse for investigating how attention interacts with processing of sensory information. Attentional selection has been linked to altered cortical sensory responses and feature preferences (i.e., tuning). However, attentional modulation of feature selectivity during search is largely unexplored. Here we map the spatiotemporal profile of feature selectivity during singleton search. Monkeys performed a search where a pop-out feature determined the target of attention. We recorded laminar neural responses from visual area V4. We first identified "feature columns" which showed preference for individual colors. In the unattended condition, feature columns were significantly more selective in superficial relative to middle and deep layers. Attending a stimulus increased selectivity in all layers but not equally. Feature selectivity increased most in the deep layers, leading to higher selectivity in extragranular layers as compared to the middle layer. This attention-induced enhancement was rhythmically gated in phase with the beta-band local field potential. Beta power dominated both extragranular laminar compartments, but current source density analysis pointed to an origin in superficial layers, specifically. While beta-band power was present regardless of attentional state, feature selectivity was only gated by beta in the attended condition. Neither the beta oscillation nor its gating of feature selectivity varied with microsaccade production. Importantly, beta modulation of neural activity predicted response times, suggesting a direct link between attentional gating and behavioral output. Together, these findings suggest beta-range synaptic activation in V4's superficial layers rhythmically gates attentional enhancement of feature tuning in a way that affects the speed of attentional selection.

Linking minimal and detailed models of CA1 microcircuits reveals how theta rhythms emerge and their frequencies controlled.

The wide variety of cell types and their biophysical complexities pose a challenge in our ability to understand oscillatory activities produced by cellular-based computational network models. This challenge stems from their high-dimensional and multiparametric natures. To overcome this, we implement a solution by linking minimal and detailed models of CA1 microcircuits that generate intrahippocampal (3-12 Hz) theta rhythms. We leverage insights from minimal models to guide explorations of more detailed models and obtain a cellular perspective of theta generation. Our findings distinguish the pyramidal cells as the theta rhythm initiators and reveal that their activity is regularized by the inhibitory cell populations, supporting a proposed hypothesis of an "inhibition-based tuning" mechanism. We find a strong correlation between input current to the pyramidal cells and the resulting local field potential theta frequency, indicating that intrinsic pyramidal cell properties underpin network frequency characteristics. This work provides a cellular-based foundation from which in vivo theta activities can be explored.

Activation of parvalbumin-expressing neurons reconfigures neuronal ensembles in murine striatal microcircuits.

The striatum is the largest entrance to the basal ganglia. Diverse neuron classes make up striatal microcircuit activity, consisting in the sequential activation of neuronal ensembles. How different neuron classes participate in generating ensemble sequences is unknown. In control mus musculus brain slices in vitro, providing excitatory drive generates ensemble sequences. In Parkinsonian microcircuits captured by a highly recurrent ensemble, a cortical stimulus causes a transitory reconfiguration of neuronal groups alleviating Parkinsonism. Alternation between neuronal ensembles needs interconnectivity, in part due to interneurons, preferentially innervated by incoming afferents. One main class of interneuron expresses parvalbumin (PV+ neurons) and mediates feed-forward inhibition. However, its more global actions within the microcircuit are unknown. Using calcium imaging in ex vivo brain slices simultaneously recording dozens of neurons, we aimed to observe the actions of PV+ neurons within the striatal microcircuit. PV+ neurons in active microcircuits are 5%-11% of the active neurons even if, anatomically, they are <1% of the total neuronal population. In resting microcircuits, optogenetic activation of PV+ neurons turns on circuit activity by activating or disinhibiting, more neurons than those actually inhibited, showing that feed-forward inhibition is not their only function. Optostimulation of PV+ neurons in active microcircuits inhibits and activates different neuron sets, resulting in the reconfiguration of neuronal ensembles by changing their functional connections and ensemble membership, showing that neurons may belong to different ensembles at different situations. Our results show that PV+ neurons participate in the mechanisms that generate alternation of neuronal ensembles, therefore provoking ensemble sequences.

Anatomy and Physiology of Macaque Visual Cortical Areas V1, V2, and V5/MT: Bases for Biologically Realistic Models.

The cerebral cortex of primates encompasses multiple anatomically and physiologically distinct areas processing visual information. Areas V1, V2, and V5/MT are conserved across mammals and are central for visual behavior. To facilitate the generation of biologically accurate computational models of primate early visual processing, here we provide an overview of over 350 published studies of these three areas in the genus Macaca, whose visual system provides the closest model for human vision. The literature reports 14 anatomical connection types from the lateral geniculate nucleus of the thalamus to V1 having distinct layers of origin or termination, and 194 connection types between V1, V2, and V5, forming multiple parallel and interacting visual processing streams. Moreover, within V1, there are reports of 286 and 120 types of intrinsic excitatory and inhibitory connections, respectively. Physiologically, tuning of neuronal responses to 11 types of visual stimulus parameters has been consistently reported. Overall, the optimal spatial frequency (SF) of constituent neurons decreases with cortical hierarchy. Moreover, V5 neurons are distinct from neurons in other areas for their higher direction selectivity, higher contrast sensitivity, higher temporal frequency tuning, and wider SF bandwidth. We also discuss currently unavailable data that could be useful for biologically accurate models.

Dopamine Receptor Expression Among Local and Visual Cortex-Projecting Frontal Eye Field Neurons.

Dopaminergic modulation of prefrontal cortex plays an important role in numerous cognitive processes, including attention. The frontal eye field (FEF) is modulated by dopamine and has an established role in visual attention, yet the underlying circuitry upon which dopamine acts is not known. We compared the expression of D1 and D2 dopamine receptors (D1Rs and D2Rs) across different classes of FEF neurons, including those projecting to dorsal or ventral extrastriate cortex. First, we found that both D1Rs and D2Rs are more prevalent on pyramidal neurons than on several classes of interneurons and are particularly prevalent on putatively long-range projecting pyramidals. Second, higher proportions of pyramidal neurons express D1Rs than D2Rs. Third, overall a higher proportion of inhibitory neurons expresses D2Rs than D1Rs. Fourth, among inhibitory interneurons, a significantly higher proportion of parvalbumin+ neurons expresses D2Rs than D1Rs, and a significantly higher proportion of calbindin+ neurons expresses D1Rs than D2Rs. Finally, compared with D2Rs, virtually all of the neurons with identified projections to both dorsal and ventral extrastriate visual cortex expressed D1Rs. Our results demonstrate that dopamine tends to act directly on the output of the FEF and that dopaminergic modulation of top-down projections to visual cortex is achieved predominately via D1Rs.

Bayesian fusion and multimodal DCM for EEG and fMRI.

This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rests upon a dynamic causal model that generates both EEG and fMRI data from the same neuronal dynamics. We introduce the use of Bayesian fusion to provide informative (empirical) neuronal priors - derived from dynamic causal modelling (DCM) of EEG data - for subsequent DCM of fMRI data. To illustrate this procedure, we generated synthetic EEG and fMRI timeseries for a mismatch negativity (or auditory oddball) paradigm, using biologically plausible model parameters (i.e., posterior expectations from a DCM of empirical, open access, EEG data). Using model inversion, we found that Bayesian fusion provided a substantial improvement in marginal likelihood or model evidence, indicating a more efficient estimation of model parameters, in relation to inverting fMRI data alone. We quantified the benefits of multimodal fusion with the information gain pertaining to neuronal and haemodynamic parameters - as measured by the Kullback-Leibler divergence between their prior and posterior densities. Remarkably, this analysis suggested that EEG data can improve estimates of haemodynamic parameters; thereby furnishing proof-of-principle that Bayesian fusion of EEG and fMRI is necessary to resolve conditional dependencies between neuronal and haemodynamic estimators. These results suggest that Bayesian fusion may offer a useful approach that exploits the complementary temporal (EEG) and spatial (fMRI) precision of different data modalities. We envisage the procedure could be applied to any multimodal dataset that can be explained by a DCM with a common neuronal parameterisation.

Real-time cortical simulation on neuromorphic hardware.

Real-time simulation of a large-scale biologically representative spiking neural network is presented, through the use of a heterogeneous parallelization scheme and SpiNNaker neuromorphic hardware. A published cortical microcircuit model is used as a benchmark test case, representing ≈1 mm2 of early sensory cortex, containing 77 k neurons and 0.3 billion synapses. This is the first hard real-time simulation of this model, with 10 s of biological simulation time executed in 10 s wall-clock time. This surpasses best-published efforts on HPC neural simulators (3 × slowdown) and GPUs running optimized spiking neural network (SNN) libraries (2 × slowdown). Furthermore, the presented approach indicates that real-time processing can be maintained with increasing SNN size, breaking the communication barrier incurred by traditional computing machinery. Model results are compared to an established HPC simulator baseline to verify simulation correctness, comparing well across a range of statistical measures. Energy to solution and energy per synaptic event are also reported, demonstrating that the relatively low-tech SpiNNaker processors achieve a 10 × reduction in energy relative to modern HPC systems, and comparable energy consumption to modern GPUs. Finally, system robustness is demonstrated through multiple 12 h simulations of the cortical microcircuit, each simulating 12 h of biological time, and demonstrating the potential of neuromorphic hardware as a neuroscience research tool for studying complex spiking neural networks over extended time periods. This article is part of the theme issue 'Harmonizing energy-autonomous computing and intelligence'.

Cortical stimulation relieves parkinsonian pathological activity in vitro.

Previously, we have shown that chemical excitatory drives such as N-methyl-d-aspartate (NMDA) are capable of activating the striatal microcircuit exhibiting neuronal ensembles that alternate their activity producing temporal sequences. One aim of this work was to demonstrate whether similar activity could be evoked by delivering cortical stimulation. Dynamic calcium imaging allowed us to follow the activity of dozens of neurons with single-cell resolution in mus musculus brain slices. A train of electrical stimuli in the cortex evoked network activity similar to the one induced by bath application of NMDA. Previously, we have also shown that the dopamine-depleted striatal microcircuit increases its spontaneous activity generating dominant recurrent ensembles that interrupt the temporal sequences found in control microcircuits. This activity correlates with parkinsonian pathological activity. Several cortical stimulation protocols such as transcranial magnetic stimulation reduce motor signs of Parkinsonism. Here, we show that cortical stimulation in vitro temporarily eliminates the pathological activity from the dopamine-depleted striatal microcircuit by turning off some neurons that sustain this activity and recruiting new ones that allow transitions between network states, similar to the control circuit. When cortical stimulation is given in the presence of L-DOPA, parkinsonian activity is eliminated during the whole recording period. The present experimental evidence suggests that cortical stimulation such as that generated by transcranial magnetic stimulation, or otherwise, may allow reduce L-DOPA dosage.

Introducing double bouquet cells into a modular cortical associative memory model.

We present an electrophysiological model of double bouquet cells and integrate them into an established cortical columnar microcircuit model that has previously been used as a spiking attractor model for memory. Learning in that model relies on a Hebbian-Bayesian learning rule to condition recurrent connectivity between pyramidal cells. We here demonstrate that the inclusion of a biophysically plausible double bouquet cell model can solve earlier concerns about learning rules that simultaneously learn excitation and inhibition and might thus violate Dale's principle. We show that learning ability and resulting effective connectivity between functional columns of previous network models is preserved when pyramidal synapses onto double bouquet cells are plastic under the same Hebbian-Bayesian learning rule. The proposed architecture draws on experimental evidence on double bouquet cells and effectively solves the problem of duplexed learning of inhibition and excitation by replacing recurrent inhibition between pyramidal cells in functional columns of different stimulus selectivity with a plastic disynaptic pathway. We thus show that the resulting change to the microcircuit architecture improves the model's biological plausibility without otherwise impacting the model's spiking activity, basic operation, and learning abilities.

Emergence of cognitive priming and structure building from the hierarchical interaction of canonical microcircuit models.

The concept of connectionism states that higher cognitive functions emerge from the interaction of many simple elements. Accordingly, research on canonical microcircuits conceptualizes findings on fundamental neuroanatomical circuits as well as recurrent organizational principles of the cerebral cortex and examines the link between architectures and their associated functionality. In this study, we establish minimal canonical microcircuit models as elements of hierarchical processing networks. Based on a combination of descriptive time simulations and explanatory state-space mappings, we show that minimal canonical microcircuits effectively segregate feedforward and feedback information flows and that feedback information conditions basic processing operations in minimal canonical microcircuits. Further, we derive and examine two prototypical meta-circuits of cooperating minimal canonical microcircuits for the neurocognitive problems of priming and structure building. Through the application of these findings to a language network of syntax parsing, this study embodies neurocognitive research on hierarchical communication in light of canonical microcircuits, cell assembly theory, and predictive coding.

Cellular components and circuitry of the presubiculum and its functional role in the head direction system.

Orientation in space is a fundamental cognitive process relying on brain-wide neuronal circuits. Many neurons in the presubiculum in the parahippocampal region encode head direction and each head direction cell selectively discharges when the animal faces a specific direction. Here, we attempt to link the current knowledge of afferent and efferent connectivity of the presubiculum to the processing of the head direction signal. We describe the cytoarchitecture of the presubicular six-layered cortex and the morphological and electrophysiological intrinsic properties of principal neurons and interneurons. While the presubicular head direction signal depends on synaptic input from thalamus, the intra- and interlaminar information flow in the microcircuit of the presubiculum may contribute to refine directional tuning. The interaction of a specific interneuron type, the Martinotti cells, with the excitatory pyramidal cells may maintain the head direction signal in the presubiculum with attractor-like properties.

Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.

With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm2 patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail.

Parallel Inhibition of Dopamine Amacrine Cells and Intrinsically Photosensitive Retinal Ganglion Cells in a Non-Image-Forming Visual Circuit of the Mouse Retina.

An inner retinal microcircuit composed of dopamine (DA)-containing amacrine cells and melanopsin-containing, intrinsically photosensitive retinal ganglion cells (M1 ipRGCs) process information about the duration and intensity of light exposures, mediating light adaptation, circadian entrainment, pupillary reflexes, and other aspects of non-image-forming vision. The neural interaction is reciprocal: M1 ipRGCs excite DA amacrine cells, and these, in turn, feed inhibition back onto M1 ipRGCs. We found that the neuropeptide somatostatin [somatotropin release inhibiting factor (SRIF)] also inhibits the intrinsic light response of M1 ipRGCs and postulated that, to tune the bidirectional interaction of M1 ipRGCs and DA amacrine cells, SRIF amacrine cells would provide inhibitory modulation to both cell types. SRIF amacrine cells, DA amacrine cells, and M1 ipRGCs form numerous contacts. DA amacrine cells and M1 ipRGCs express the SRIF receptor subtypes sst(2A) and sst4 respectively. SRIF modulation of the microcircuit was investigated with targeted patch-clamp recordings of DA amacrine cells in TH-RFP mice and M1 ipRGCs in OPN4-EGFP mice. SRIF increases K(+) currents, decreases Ca(2+) currents, and inhibits spike activity in both cell types, actions reproduced by the selective sst(2A) agonist L-054,264 (N-[(1R)-2-[[[(1S*,3R*)-3-(aminomethyl)cyclohexyl]methyl]amino]-1-(1H-indol-3-ylmethyl)-2-oxoethyl]spiro[1H-indene-1,4'-piperidine]-1'-carboxamide) in DA amacrine cells and the selective sst4 agonist L-803,087 (N(2)-[4-(5,7-difluoro-2-phenyl-1H-indol-3-yl)-1-oxobutyl]-L-arginine methyl ester trifluoroacetate) in M1 ipRGCs. These parallel actions of SRIF may serve to counteract the disinhibition of M1 ipRGCs caused by SRIF inhibition of DA amacrine cells. This allows the actions of SRIF on DA amacrine cells to proceed with adjusting retinal DA levels without destabilizing light responses by M1 ipRGCs, which project to non-image-forming targets in the brain.

A structural and genotypic scaffold underlying temporal integration.

The accumulation and storage of information over time, temporal integration, is key to numerous behaviors. Many oculomotor tasks depend on integration of eye-velocity signals to eye-position commands, a transformation achieved by a hindbrain cell group termed the velocity-to-position neural integrator (VPNI). Although the VPNI's coding properties have been well characterized, its mechanism of function remains poorly understood because few links exist between neuronal activity, structure, and genotypic identity. To fill this gap, we used calcium imaging and single-cell electroporation during oculomotor behaviors to map VPNI neural activity in zebrafish onto a hindbrain scaffold consisting of alternating excitatory and inhibitory parasagittal stripes. Three distinct classes of VPNI cells were identified. One glutamatergic class was medially located along a stripe associated with the alx transcription factor; these cells had ipsilateral projections terminating near abducens motoneurons and collateralized extensively within the ipsilateral VPNI in a manner consistent with integration through recurrent excitation. A second glutamatergic class was more laterally located along a stripe associated with transcription factor dbx1b; these glutamatergic cells had contralateral projections collateralizing near abducens motoneurons, consistent with a role in disconjugate eye movements. A third class, immunohistochemically suggested to be GABAergic, was located primarily in the dbx1b stripe and also had contralateral projections terminating near abducens motoneurons; these cells collateralized extensively in the dendritic field of contralateral VPNI neurons, consistent with a role in coordinating activity between functionally opposing populations. This mapping between VPNI activity, structure, and genotype may provide a blueprint for understanding the mechanisms governing temporal integration.

A population of gap junction-coupled neurons drives recurrent network activity in a developing visual circuit.

In many regions of the vertebrate brain, microcircuits generate local recurrent activity that aids in the processing and encoding of incoming afferent inputs. Local recurrent activity can amplify, filter, and temporally and spatially parse out incoming input. Determining how these microcircuits function is of great interest because it provides glimpses into fundamental processes underlying brain computation. Within the Xenopus tadpole optic tectum, deep layer neurons display robust recurrent activity. Although the development and plasticity of this local recurrent activity has been well described, the underlying microcircuitry is not well understood. Here, using a whole brain preparation that allows for whole cell recording from neurons of the superficial tectal layers, we identified a physiologically distinct population of excitatory neurons that are gap junctionally coupled and through this coupling gate local recurrent network activity. Our findings provide a novel role for neuronal coupling among excitatory interneurons in the temporal processing of visual stimuli.

Pathophysiological signatures of functional connectomics in parkinsonian and dyskinetic striatal microcircuits.

A challenge in neuroscience is to integrate the cellular and system levels. For instance, we still do not know how a few dozen neurons organize their activity and relations in a microcircuit or module of histological scale. By using network theory and Ca(2+) imaging with single-neuron resolution we studied the way in which striatal microcircuits of dozens of cells orchestrate their activity. In addition, control and diseased striatal tissues were compared in rats. In the control tissue, functional connectomics revealed small-world, scale-free and hierarchical network properties. These properties were lost during pathological conditions in ways that could be quantitatively analyzed. Decorticated striatal circuits disclosed that corticostriatal interactions depend on privileged connections with a set of highly connected neurons or "hubs". In the 6-OHDA model of Parkinson's disease there was a decrease in hubs number; but the ones that remained were linked to dominant network states. l-DOPA induced dyskinesia provoked a loss in the hierarchical structure of the circuit. All these conditions conferred distinct temporal sequences to circuit activity. Temporal sequences appeared as particular signatures of disease process thus bringing the possibility of a future quantitative pathophysiology at a histological scale.

Rapid Bidirectional Reorganization of Cortical Microcircuits.

Mature neocortex adapts to altered sensory input by changing neural activity in cortical circuits. The underlying cellular mechanisms remain unclear. We used blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) to show reorganization in somatosensory cortex elicited by altered whisker sensory input. We found that there was rapid expansion followed by retraction of whisker cortical maps. The cellular basis for the reorganization in primary somatosensory cortex was investigated with paired electrophysiological recordings in the periphery of the expanded whisker representation. During map expansion, the chance of finding a monosynaptic connection between pairs of pyramidal neurons increased 3-fold. Despite the rapid increase in local excitatory connectivity, the average strength and synaptic dynamics did not change, which suggests that new excitatory connections rapidly acquire the properties of established excitatory connections. During map retraction, entire excitatory connections between pyramidal neurons were lost. In contrast, connectivity between pyramidal neurons and fast spiking interneurons was unchanged. Hence, the changes in local excitatory connectivity did not occur in all circuits involving pyramidal neurons. Our data show that pyramidal neurons are recruited to and eliminated from local excitatory networks over days. These findings suggest that the local excitatory connectome is dynamic in mature neocortex.

Dendritic Excitability and Gain Control in Recurrent Cortical Microcircuits.

Layer 5 thick tufted pyramidal cells (TTCs) in the neocortex are particularly electrically complex, owing to their highly excitable dendrites. The interplay between dendritic nonlinearities and recurrent cortical microcircuit activity in shaping network response is largely unknown. We simulated detailed conductance-based models of TTCs forming recurrent microcircuits that were interconnected as found experimentally; the network was embedded in a realistic background synaptic activity. TTCs microcircuits significantly amplified brief thalamocortical inputs; this cortical gain was mediated by back-propagation activated N-methyl-D-aspartate depolarizations and dendritic back-propagation-activated Ca(2+) spike firing, ignited by the coincidence of thalamic-activated somatic spike and local dendritic synaptic inputs, originating from the cortical microcircuit. Surprisingly, dendritic nonlinearities in TTCs microcircuits linearly multiplied thalamic inputs--amplifying them while maintaining input selectivity. Our findings indicate that dendritic nonlinearities are pivotal in controlling the gain and the computational functions of TTCs microcircuits, which serve as a dominant output source for the neocortex.

Emergence of feature-specific connectivity in cortical microcircuits in the absence of visual experience.

In primary visual cortex (V1), connectivity between layer 2/3 (L2/3) excitatory neurons undergoes extensive reorganization after the onset of visual experience whereby neurons with similar feature selectivity form functional microcircuits (Ko et al., 2011, 2013). It remains unknown whether visual experience is required for the developmental refinement of intracortical circuitry or whether this maturation is guided intrinsically. Here, we correlated the connectivity between V1 L2/3 neurons assayed by simultaneous whole-cell recordings in vitro to their response properties measured by two-photon calcium imaging in vivo in dark-reared mice. We found that neurons with similar responses to oriented gratings or natural movies became preferentially connected in the absence of visual experience. However, the relationship between connectivity and similarity of visual responses to natural movies was not as strong in dark-reared as in normally reared mice. Moreover, dark rearing prevented the normally occurring loss of connections between visually nonresponsive neurons after eye opening (Ko et al., 2013). Therefore, our data suggest that the absence of visual input does not prevent the emergence of functionally specific recurrent connectivity in cortical circuits; however, visual experience is required for complete microcircuit maturation.