Long-range Inhibitory Connections Research Articles

Dynamics of multiple interacting excitatory and inhibitory populations with delays.

A network consisting of excitatory and inhibitory (EI) neurons is a canonical model for understanding local cortical network activity. In this study, we extended the local circuit model and investigated how its dynamical landscape can be enriched when it interacts with another excitatory (E) population with long transmission delays. Through analysis of a rate model and numerical simulations of a corresponding network of spiking neurons, we studied the transition from stationary to oscillatory states by analyzing the Hopf bifurcation structure in terms of two network parameters: (1) transmission delay between the EI subnetwork and the E population and (2) inhibitory couplings that induced oscillatory activity in the EI subnetwork. We found that the critical coupling strength can strongly modulate as a function of transmission delay, and consequently the stationary state can be interwoven intricately with the oscillatory state. Such a dynamical landscape gave rise to an isolated stationary state surrounded by multiple oscillatory states that generated different frequency modes, and cross-frequency coupling developed naturally at the bifurcation points. We identified the network motifs with short- and long-range inhibitory connections that underlie the emergence of oscillatory states with multiple frequencies. Thus, we provided a mechanistic explanation of how the transmission delay to and from the additional E population altered the dynamical landscape. In summary, our results demonstrated the potential role of long-range connections in shaping the network activity of local cortical circuits.

Visual Information Processing in the Ventral Division of the Mouse Lateral Geniculate Nucleus of the Thalamus.

Even though the lateral geniculate nucleus of the thalamus (LGN) is associated with form vision, that is not its sole role. Only the dorsal portion of LGN (dLGN) projects to V1. The ventral division (vLGN) connects subcortically, sending inhibitory projections to sensorimotor structures, including the superior colliculus (SC) and regions associated with certain behavioral states, such as fear (Monavarfeshani et al., 2017; Salay et al., 2018). We combined computational, physiological, and anatomical approaches to explore visual processing in vLGN of mice of both sexes, making comparisons to dLGN and SC for perspective. Compatible with past, qualitative descriptions, the receptive fields we quantified in vLGN were larger than those in dLGN, and most cells preferred bright versus dark stimuli (Harrington, 1997). Dendritic arbors spanned the length and/or width of vLGN and were often asymmetric, positioned to collect input from large but discrete territories. By contrast, arbors in dLGN are compact (Krahe et al., 2011). Consistent with spatially coarse receptive fields in vLGN, visually evoked changes in spike timing were less precise than for dLGN and SC. Notably, however, the membrane currents and spikes of some cells in vLGN displayed gamma oscillations whose phase and strength varied with stimulus pattern, as for SC (Stitt et al., 2013). Thus, vLGN can engage its targets using oscillation-based and conventional rate codes. Finally, dark shadows activate SC and drive escape responses, whereas vLGN prefers bright stimuli. Thus, one function of long-range inhibitory projections from vLGN might be to enable movement by releasing motor targets, such as SC, from suppression.SIGNIFICANCE STATEMENT Only the dorsal lateral geniculate nucleus (dLGN) connects to cortex to serve form vision; the ventral division (vLGN) projects subcortically to sensorimotor nuclei, including the superior colliculus (SC), via long-range inhibitory connections. Here, we asked how vLGN processes visual information, making comparisons with dLGN and SC for perspective. Cells in vLGN versus dLGN had wider dendritic arbors, larger receptive fields, and fired with lower temporal precision, consistent with a modulatory role. Like SC, but not dLGN, visual stimuli entrained oscillations in vLGN, perhaps reflecting shared strategies for visuomotor processing. Finally, most neurons in vLGN preferred bright shapes, whereas dark stimuli activate SC and drive escape behaviors, suggesting that vLGN enables rapid movement by releasing target motor structures from inhibition.

Unsupervised Learning Facilitates Neural Coordination Across the Functional Clusters of the C. elegans Connectome.

Modeling of complex adaptive systems has revealed a still poorly understood benefit of unsupervised learning: when neural networks are enabled to form an associative memory of a large set of their own attractor configurations, they begin to reorganize their connectivity in a direction that minimizes the coordination constraints posed by the initial network architecture. This self-optimization process has been replicated in various neural network formalisms, but it is still unclear whether it can be applied to biologically more realistic network topologies and scaled up to larger networks. Here we continue our efforts to respond to these challenges by demonstrating the process on the connectome of the widely studied nematode worm C. elegans. We extend our previous work by considering the contributions made by hierarchical partitions of the connectome that form functional clusters, and we explore possible beneficial effects of inter-cluster inhibitory connections. We conclude that the self-optimization process can be applied to neural network topologies characterized by greater biological realism, and that long-range inhibitory connections can facilitate the generalization capacity of the process.

The interaction between integration and segmentation neurons for motion perception

The main challenge for understanding motion processing in the visual system is that local motion signals do not accurately represent the direction of the whole object. This is known as the The small receptive fields of individual neurons can only measure motion components orthogonal to their preferred direction. Neuro-physiological studies show that neurons in brain area MT (V5) have a major role in dealing with this problem [1]. We develop a model to overcome the aperture problem through excitatory and inhibitory interconnections between MT neurons. The model contains two levels of rate-based neuron. The first level is a model of V1 with two sets of neurons: complex neurons and hyper-complex end-stopped neurons. End-stopped neurons respond to the end-points of the stimulus , thereby representing an unambiguous estimation of the direction of motion [2]. The response of V1 neurons is transmitted to the second level, the model of MT neurons. MT neurons are categorized as integration and segmentation neurons. The role of integration neurons is to integrate local motion signals to solve the aperture problem; the role of segmentation neurons is to detect discontinuities in the input stimulus to inhibit integration neurons [3]. Excitatory recurrent connections between integration neurons propagate end-stopped information from the object terminators. As each spatial location contains neurons selective to different directions, inhibitory connections are added between the neurons such that there will be only one neuron active at each spatial location. Long-range inhibitory connections are also added between more distant MT neurons selective to other directions. Segmentation neurons send inhibitory connections to integration neurons to prevent the propagation of motion signals outside the border of the input stimulus. Inhibitory connections between segmentation neurons are defined based on center-surround interactions; this suppresses activity when the motion signals in the center and surround are in the same directions; ensuring that their activity will not prevent the propagation of motion signals when there is no discontinuity. The results of simulations show that the activity of V1 neurons along the edges is much stronger than the activity of the neurons at the end-points. Activation of end-stopped neurons in V1 suppresses ambiguous motion signals along the edges of the stimulus. Therefore, unambiguous information at the end-points wins the competition over the neurons with ambiguous motion signals. The final output of the model shows that MT neurons achieve an accurate estimation of motion using the information provided by the end-stopped neurons. We developed a model of MT neurons for motion perception based on the available neuro-physiological data. These neurons receive their input from neurons in V1. The results show the successful function of these neurons in the perception of motion of a single moving bar. In this model, we also investigated the role of end-stopped neurons in overcoming the aperture problem. The results suggest that MT neurons are not able to solve the aperture problem without the vital role played by end-stopped cells.

Reconciling models of surround modulation and V1 feature map development

The cerebral cortex of mammals is organized as a set of topographic maps, forming sensory and motor areas such as those in the visual, auditory, and somatosensory systems. Understanding how these maps develop and whether they have any functional significance is critical for understanding cortical processing. The prototypical example of topographic feature maps is the map of orientation preference in primary visual cortex (V1). Models of V1 orientation map development have been very successful in reproducing the features of biological maps. The majority of these models are based on a principle of Mexican-hat connectivity i.e. short-range excitatory and long-range inhibitory connections between neurons (e.g. [1]). However, experimental data is in striking disagreement with this principle. There is a consensus that long-range connections between V1 neurons are excitatory [2]. Moreover, models with long-range excitatory connections are able to account for a wide range of experimental data from adult V1, such as surround modulation (e.g. [3]). Models of orientation map development are thus based on a connectivity which is precisely opposite to that suggested by a mounting body of experimental and computational evidence. It is not yet clear if the circuits used in surround modulation models are consistent with the development of orientation maps. It is also important to consider how the topographic organization of orientation preference may affect surround modulation. Since cortical circuitry is intimately tied to topographic organization, it is likely that surround modulation properties differ depending on the position of a cell within the orientation map. In order to address the above issues, we have developed the first model that is consistent with current models of surround modulation, yet also reproduces the features of successful developmental models of topographic map formation. The model consists of sheets of firing-rate-based units that represent the retina, LGN, excitatory, and inhibitory neurons in V1. An activity-driven Hebbian learning mechanism results in the adjustment of afferent (retina to V1) and long-range lateral connection weights (within V1), leading to the development of orientation selectivity organized smoothly in a realistic orientation map.

Long-range inhibition within the zebra finch song nucleus RA can coordinate the firing of multiple projection neurons.

The zebra finch forebrain song control nucleus RA (robust nucleus of the archistriatum) generates a phasic and temporally precise neural signal that drives vocal and respiratory motoneurons during singing. RA's output during singing predicts individual notes, even though afferent drive to RA from the song nucleus HVc is more tonic, and predicts song syllables, independent of the particular notes that comprise the syllable. Therefore RA's intrinsic circuitry transforms neural activity from HVc into a highly precise premotor output. To understand how RA's intrinsic circuitry effects this transformation, we characterized RA interneurons and projection neurons using intracellular recordings in brain slices. RA interneurons fired fast action potentials with steep current-frequency relationships and had small somata with thin aspinous processes that extended throughout large portions of the nucleus; the similarity of their fine processes to those labeled with a glutamic acid decarboxylase (GAD) antibody strongly suggests that these interneurons are GABAergic. Electrical stimulation revealed that RA interneurons receive excitatory inputs from RA's afferents, the lateral magnocellular nucleus of the anterior neostriatum (LMAN) and HVc, and from local axon collaterals of RA projection neurons. To map the functional connections that RA interneurons make onto RA projection neurons, we focally uncaged glutamate, revealing long-range inhibitory connections in RA. Thus these interneurons provide fast feed-forward and feedback inhibition to RA projection neurons and could help create the phasic pattern of bursts and pauses that characterizes RA output during singing. Furthermore, selectively activating the inhibitory network phase locks the firing of otherwise unconnected pairs of projection neurons, suggesting that local inhibition could coordinate RA output during singing.

Organization of long-range inhibitory connections with rat visual cortex

We have studied the laminar organization of local long-range inhibitory connections within rat primary visual cortex (area 17) by combining retrograde tracing of nerve cell bodies with glutamic acid decarboxylase immunocytochemistry. While most inhibitory connections are confined to within 0.4 mm of the injection site, a subset of neurons at the layer 5/6 border provide long-range (> 1 mm) inhibitory connections within area 17. However, other cell layers that contain similar local long-range horizontal connections, that is, lower layer 2/3, upper layer 5, and lower layer 6 (Burkhalter and Charles, 1990), show a much more restricted distribution of inhibitory connections. This suggests that cells at the layer 5/6 border play a role in the direct inhibition of neurons at a distant point of the topographic map. Similar double labeling studies reveal long-range inhibitory connections between visual areas. Following injections of fluorescent tracers into area 17, in horizontal sections inhibitory connections can be identified that are up to 8 mm long, linking the extrastriate subdivisions 18a and 18b with striate cortex. Conversely, injections of fluorescent tracers into the cytoarchitectonic subdivision 18a reveal local long-range inhibitory connections within 18a, long-range inhibitory connections between 18a and the cytoarchitectonic subdivision 18b, and inhibitory forward connections from area 17 to 18a. These results suggest that the communication between different cortical areas can be influenced by direct inhibitory connections.