Information processing in a midbrain visual pathway

Visual information is processed in brain via the intricate interactions between neurons. We investigated a midbrain visual pathway: optic tectum and its isthmic nucleus) that is motion sensitive and is thought as part of attentional system. We determined the physiological properties of individual neurons as well as their synaptic connections with intracellular recordings. We reproduced the center-surround receptive field structure of tectal neurons in a dynamical recurrent feedback loop. We reveal in a computational model that the anti-topographic inhibitory feedback could mediate competitive stimulus selection in a complex visual scene. We also investigated the dynamics of the competitive selection in a rate model. The isthmotectal feedback loop gates the information transfer from tectum to thalamic rotundus. We discussed the role of a localized feedback projection in contributing to the gating mechanisms with both experimental and numerical approaches. We further discussed the dynamics of the isthmotectal system by considering the propagation delays between different components. We conclude that the isthmotectal system is involved in attention-like competitive stimulus selection and control the information coding in the motion sensitive SGC-I neurons by modulating the retino-tectal synaptic transmission

Segregation of cortical head direction cell assemblies on alternating theta cycles

High-level cortical systems for spatial navigation, including entorhinal grid cells, critically depend on input from the head direction system. We examined spiking rhythms and modes of synchrony between neurons participating in head direction networks for evidence of internal processing, independent of direct sensory drive, which may be important for grid cell function. We found that head direction networks of rats were segregated into at least two populations of neurons firing on alternate theta cycles (theta cycle skipping) with fixed synchronous or anti-synchronous relationships. Pairs of anti-synchronous theta cycle skipping neurons exhibited larger differences in head direction tuning, with a minimum difference of 40 degrees of head direction. Septal inactivation preserved the head direction signal, but eliminated theta cycle skipping of head direction cells and grid cell spatial periodicity. We propose that internal mechanisms underlying cycle skipping in head direction networks may be critical for downstream spatial computation by grid cells.We kindly thank S. Gillet, J. Hinman, E. Newman and L. Ewell for their invaluable consultations and comments on previous versions of this manuscript, as well as M. Connerney, S. Eriksson, C. Libby and T. Ware for technical assistance and behavioral training. This work was supported by grants from the National Institute of Mental Health (R01 MH60013 and MH61492) and the Office of Naval Research Multidisciplinary University Research Initiative (N00014-10-1-0936). (R01 MH60013 - National Institute of Mental Health; MH61492 - National Institute of Mental Health; N00014-10-1-0936 - Office of Naval Research Multidisciplinary University Research Initiative)Accepted manuscrip

The Mechanisms And Roles Of Feedback Loops For Visual Processing

Signal flow in the brain is not unidirectional; feedback represents a key element in neural signal processing. To address the question on how do neural feedback loops work in terms of synapses, microcircuitry, and systems dynamics, we developed a chick midbrain slice preparation to study and characterize one important feedback loop within the avian visual system: isthmotectal feedbackloop. The isthmotectal feedback loop consists of the optic tectum: OT) and three nucleus isthmi: Imc, Ipc and SLu. The tectal layer 10 neurons project to ipsilateral Imc, Ipc and SLu in a topographic way. In turn Ipc and SLu send back topographical: local) cholinergic terminals to the OT, whereas Imc sends non-topographical: global) GABAergic projections to the OT, and also to the Ipc and the SLu. We first study the cellular properties of Ipc neurons and found that almost all Ipc cells exhibited spontaneous activity characterized with a barrage of EPSPs and occasional spikes. Further experiments reveal the involvement of GABA in mediating the spontaneous synaptic inputs to the Ipc neurons. Next we investigate the mechanisms of oscillatory bursting in Ipc, which is observed in vivo, by building a model network based on the in vitro experimental results. Our simulation results conclude that strong feedforward excitation and spike-rate adaptation can generate oscillatory bursting in Ipc neuron in response to a constant input. Then we consider the effect of distributed synaptic delays measured within the isthmotectal feedback loop and elucidate that distributed delays can stabilize the system and lead to an increased range of parameters for which the system converges to a stable fixed point. Next we explore the functional features of GABAergic projection from Imc to Ipc and find that Imc has a regulatory role on actions of Ipc neurons in that stimulating Imc can evoke action potentials in Ipc neurons while it also can suppress the firing in Ipc neurons which is generated by somatic current injection. The mechanism of regulatory action is further studied by a two-compartment neuron model. Last, we lay out several open questions in this area which may worth further investigation

Intrinsic adaptation in autonomous recurrent neural networks

A massively recurrent neural network responds on one side to input stimuli and is autonomously active, on the other side, in the absence of sensory inputs. Stimuli and information processing depends crucially on the qualia of the autonomous-state dynamics of the ongoing neural activity. This default neural activity may be dynamically structured in time and space, showing regular, synchronized, bursting or chaotic activity patterns. We study the influence of non-synaptic plasticity on the default dynamical state of recurrent neural networks. The non-synaptic adaption considered acts on intrinsic neural parameters, such as the threshold and the gain, and is driven by the optimization of the information entropy. We observe, in the presence of the intrinsic adaptation processes, three distinct and globally attracting dynamical regimes, a regular synchronized, an overall chaotic and an intermittent bursting regime. The intermittent bursting regime is characterized by intervals of regular flows, which are quite insensitive to external stimuli, interseeded by chaotic bursts which respond sensitively to input signals. We discuss these finding in the context of self-organized information processing and critical brain dynamics.Comment: 24 pages, 8 figure

Mechanisms explaining transitions between tonic and phasic firing in neuronal populations as predicted by a low dimensional firing rate model

Several firing patterns experimentally observed in neural populations have been successfully correlated to animal behavior. Population bursting, hereby regarded as a period of high firing rate followed by a period of quiescence, is typically observed in groups of neurons during behavior. Biophysical membrane-potential models of single cell bursting involve at least three equations. Extending such models to study the collective behavior of neural populations involves thousands of equations and can be very expensive computationally. For this reason, low dimensional population models that capture biophysical aspects of networks are needed. \noindent The present paper uses a firing-rate model to study mechanisms that trigger and stop transitions between tonic and phasic population firing. These mechanisms are captured through a two-dimensional system, which can potentially be extended to include interactions between different areas of the nervous system with a small number of equations. The typical behavior of midbrain dopaminergic neurons in the rodent is used as an example to illustrate and interpret our results. \noindent The model presented here can be used as a building block to study interactions between networks of neurons. This theoretical approach may help contextualize and understand the factors involved in regulating burst firing in populations and how it may modulate distinct aspects of behavior.Comment: 25 pages (including references and appendices); 12 figures uploaded as separate file

A különböző gátlósejttípusok hozzájárulása a hippokampális éleshullámok kialakulásához. = Contribution by distinct types of GABAergic interneuron to hippocampal sharp wave/ripple oscillations.

A hippokampusz neuronhálózatában spontán keletkeznek az éleshullámok, amelyek kulcsszerepet játszanak a memóriafolyamatokban. Egy in vitro modellt használva feltérképeztük az egyes idegsejttípusok bemeneti és kimeneti tulajdonságait az éleshullámok alatt. Az találtuk, hogy a legaktívabb gátlósejtek parvalbumint tartalmaztak, míg a piramissejtek többsége nem tüzelt. Meghatároztuk, hogy az éleshullámok alatti tüzelési aktivitás korrelált a serkentő szinaptikus bemenettel. Farmakológiai kísérletekkel kiderítettük, hogy a parvalbumin tartalmú gátlósejtek nagyfrekvenciás kisüléséből eredő periszomatikus gátló áramok generálják a lokális mezőpotenciálban mérhető éleshullámokat. Hasonlóan, ezek a gátlósejtek felelősek a gamma oszcillációk létrehozásáért is a hippokampális agyszeletekben. Ezen túlmenően megállapítottuk, hogy a kolinerg receptorok aktivációja, amely növeli a serkenthetőséget, de csökkenti a szinaptikus kommunikáció hatékonyságát, képes a hippokampusz alapműködését, az éleshullám-aktivitást átkapcsolni gamma oszcillációvá. Az eredményeink azt mutatják, hogy az éber állatra jellemző hálózati aktivitásokat, az éleshullámokat és a gamma oszcillációt ugyan az a hippokampális neuronhálózat generálja, amely a piramissejtek és a parvalbumin tartamú gátlósejtekből áll. | Sharp wave/ripple oscillations (SPW-Rs), that play a crucial role in memory formation, are spontaneously emerging synchronous network events in the hippocampal circuitry. Using an in vitro model, we uncovered that the input-output properties of distinct types of neurons during SPW-Rs. We found that the most active GABAergic cells were parvalbumin containing interneurons, while the vast majority of pyramidal cells was silent. Our analysis revealed that in all cell types the firing during SPW-Rs was driven by excitatory synaptic input. Pharmacological manipulations uncovered that perisomatic inhibitory currents predominantly originated from the high frequency discharge of parvalbumin containing interneurons generate the majority of the field potential that is seen as a sharp wave. Similarly, these GABAergic cells were found to generate the gamma oscillations in hippocampal slices as well. In addition, we elucidated that by cholinergic receptor activation, which increases the excitability, but reduces the efficiency of synaptic communication, the default mode of the hippocampal operation, the SPW-R state can be readily switched to gamma oscillation. Our results propose that the behaviorally relevant network activities, SPW-Rs and gamma oscillations are generated by the same neuronal circuitries in the hippocampus, comprised of pyramidal cells and parvalbumin containing interneurons

Probing the dynamics of identified neurons with a data-driven modeling approach

In controlling animal behavior the nervous system has to perform within the operational limits set by the requirements of each specific behavior. The implications for the corresponding range of suitable network, single neuron, and ion channel properties have remained elusive. In this article we approach the question of how well-constrained properties of neuronal systems may be on the neuronal level. We used large data sets of the activity of isolated invertebrate identified cells and built an accurate conductance-based model for this cell type using customized automated parameter estimation techniques. By direct inspection of the data we found that the variability of the neurons is larger when they are isolated from the circuit than when in the intact system. Furthermore, the responses of the neurons to perturbations appear to be more consistent than their autonomous behavior under stationary conditions. In the developed model, the constraints on different parameters that enforce appropriate model dynamics vary widely from some very tightly controlled parameters to others that are almost arbitrary. The model also allows predictions for the effect of blocking selected ionic currents and to prove that the origin of irregular dynamics in the neuron model is proper chaoticity and that this chaoticity is typical in an appropriate sense. Our results indicate that data driven models are useful tools for the in-depth analysis of neuronal dynamics. The better consistency of responses to perturbations, in the real neurons as well as in the model, suggests a paradigm shift away from measuring autonomous dynamics alone towards protocols of controlled perturbations. Our predictions for the impact of channel blockers on the neuronal dynamics and the proof of chaoticity underscore the wide scope of our approach

Visual attention deficits in schizophrenia can arise from inhibitory dysfunction in thalamus or cortex

Schizophrenia is associated with diverse cognitive deficits, including disorders of attention-related oculomotor behavior. At the structural level, schizophrenia is associated with abnormal inhibitory control in the circuit linking cortex and thalamus. We developed a spiking neural network model that demonstrates how dysfunctional inhibition can degrade attentive gaze control. Our model revealed that perturbations of two functionally distinct classes of cortical inhibitory neurons, or of the inhibitory thalamic reticular nucleus, disrupted processing vital for sustained attention to a stimulus, leading to distractibility. Because perturbation at each circuit node led to comparable but qualitatively distinct disruptions in attentive tracking or fixation, our findings support the search for new eye movement metrics that may index distinct underlying neural defects. Moreover, because the cortico-thalamic circuit is a common motif across sensory, association, and motor systems, the model and extensions can be broadly applied to study normal function and the neural bases of other cognitive deficits in schizophrenia.R01 MH057414 - NIMH NIH HHS; R01 MH101209 - NIMH NIH HHS; R01 NS024760 - NINDS NIH HHSPublished versio