HIERARCHICAL NEURAL COMPUTATION IN THE MAMMALIAN VISUAL SYSTEM

Our visual system can efficiently extract behaviorally relevant information from ambiguous and noisy luminance patterns. Although we know much about the anatomy and physiology of the visual system, it remains obscure how the computation performed by individual visual neurons is constructed from the neural circuits. In this thesis, I designed novel statistical modeling approaches to study hierarchical neural computation, using electrophysiological recordings from several stages of the mammalian visual system. In Chapter 2, I describe a two-stage nonlinear model that characterized both synaptic current and spike response of retinal ganglion cells with unprecedented accuracy. I found that excitatory synaptic currents to ganglion cells are well described by excitatory inputs multiplied by divisive suppression, and that spike responses can be explained with the addition of a second stage of spiking nonlinearity and refractoriness. The structure of the model was inspired by known elements of the retinal circuit, and implies that presynaptic inhibition from amacrine cells is an important mechanism underlying ganglion cell computation. In Chapter 3, I describe a hierarchical stimulus-processing model of MT neurons in the context of a naturalistic optic flow stimulus. The model incorporates relevant nonlinear properties of upstream V1 processing and explained MT neuron responses to complex motion stimuli. MT neuron responses are shown to be best predicted from distinct excitatory and suppressive components. The direction-selective suppression can impart selectivity of MT neurons to complex velocity fields, and contribute to improved estimation of the three-dimensional velocity of moving objects. In Chapter 4, I present an extended model of MT neurons that includes both the stimulus-processing component and network activity reflected in local field potentials (LFPs). A significant fraction of the trial-to-trial variability of MT neuron responses is predictable from the LFPs in both passive fixation and a motion discrimination task. Moreover, the choice-related variability of MT neuron responses can be explained by their phase preferences in low-frequency band LFPs. These results suggest an important role of network activity in cortical function. Together, these results demonstrated that it is possible to infer the nature of neural computation from physiological recordings using statistical modeling approaches

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

POPULATION CODING IN LAMINAR CORTICAL CIRCUITS

One of the fundamental questions in neuroscience is to understand how encoding of sensory inputs is distributed across neuronal networks in cerebral cortex to influence sensory processing and behavioral performance. The fact that the structure of neuronal networks is organized according to cortical layers raises the possibility that sensory information could be processed differently in distinct layers. The goal of my thesis research is to understand how laminar circuits encode information in their population activity, how the properties of the population code adapt to changes in visual input, and how population coding influences behavioral performance. To this end, we performed a series of novel experiments to investigate how sensory information in the primary visual cortex (V1) emerges across laminar cortical circuits. First, it is commonly known that the amount of information encoded by cortical circuits depends critically on whether or not nearby neurons exhibit correlations. We examined correlated variability in V1 circuits from a laminar-specific perspective and observed that cells in the input layer, which have only local projections, encode incoming stimuli optimally by exhibiting low correlated variability. In contrast, output layers, which send projections to other cortical and subcortical areas, encode information suboptimally by exhibiting large correlations. These results argue that neuronal populations in different cortical layers play different roles in network computations. Secondly, a fundamental feature of cortical neurons is their ability to adapt to changes in incoming stimuli. Understanding how adaptation emerges across cortical layers to influence information processing is vital for understanding efficient sensory coding. We examined the effects of adaptation, on the time-scale of a visual fixation, on network synchronization across laminar circuits. Specific to the superficial layers, we observed an increase in gamma-band (30-80 Hz) synchronization after adaptation that was correlated with an improvement in neuronal orientation discrimination performance. Thus, synchronization enhances sensory coding to optimize network processing across laminar circuits. Finally, we tested the hypothesis that individual neurons and local populations synchronize their activity in real-time to communicate information about incoming stimuli, and that the degree of synchronization influences behavioral performance. These analyses assessed for the first time the relationship between changes in laminar cortical networks involved in stimulus processing and behavioral performance

TACTILE PROCESSING DEPENDS ON MOTOR INTENTIONS

Tactile processing serves upcoming actions, which depend on behavioral goals. It remains unclear how neural signals related to movements interact with sensory signals in primary somatosensory (S1) cortex. We developed a cross-modal attention task in which head-fixed mice flexibly switched between responding to tactile stimuli in the presence of visual distractors, or to visual stimuli in the presence of tactile distractors, using licking movements to the left or right side in different blocks of trials. Spiking of S1 neurons during the task showed both tactile and licking-related motor responses. S1 neurons encoded tactile stimuli, licking, and direction of licking in response to tactile but not visual stimuli. Optogenetic stimulation of tongue premotor cortex recapitulated motor signals in S1. Bidirectional optogenetic manipulations revealed that performance depended on sensory-motor activity in S1 during attention to touch but not vision. Our results show that sensory and motor signals interact in S1 to promote specific actions

Stimulus and task-dependent gamma activity in monkey V1

The single unit doctrine proposes that each one of our percepts and sensations is represented by the activity of specialized high-level cells in the brain. A common criticism applied to this proposal is the one referred to as the "combinatorial problem". We are constantly confronted with unlimited combinations of elements and features, and yet we face no problem in recognizing patterns and objects present in visual scenes. Are there enough neurons in the brain to singly code for each one of our percepts? Or is it the case that perceptions are represented by the distributed activity of different neuronal ensembles? We lack a general theory capable of explaining how distributed information can be efficiently integrated into single percepts. The working hypothesis here is that distributed neuronal ensembles signal relations present in the stimulus by selectively synchronizing their spiking responses. Synchronization is generally associated with oscillatory activity in the brain. Gamma oscillations in particular have been linked to various integrative processes in the visual system. Studies in anesthetized animals have shown a conspicuous increase in power for the gamma frequency band (30 to 60 Hz) in response to visual stimuli. Recently, these observations have been extended to behavioral studies which addressed the role of gamma activity in cognitive processes demanding selective attention. The initial motivation for carrying out this work was to test if the binding-by-synchronization (BBS) hypothesis serves as a neuronal mechanism for perceptual grouping in the visual system. To this aim we used single and superimposed grating stimuli. Superimposed gratings (plaids) are bi-stable stimuli capable of eliciting different percepts depending on their physical characteristics. In this way, plaids can be perceived either as a single moving surface (pattern plaids), or as two segregated surfaces drifting in different directions (component plaids). While testing the BBS hypothesis, we performed various experiments which addressed the role of both stimulus and cortical architecture on the properties of gamma oscillations in the primary visual cortex (V1) of monkeys. Additionally, we investigated whether gamma activity could also be modulated by allocating attention in time. Finally, we report on gamma-phase shifts in area V1, and how they depend on the level of neuronal activation. ...Einleitung: Die visuelle Hirnforschung hat eine große Informationsmenge über die analytischen Fähigkeiten des Nervensystems zusammengetragen. Die Einführung von Einzelzellableitungen ermöglichte eine detaillierte Beschreibung der Eigenschaften rezeptiver Felder im Sehsystem. Konzentrische rezeptive Felder in der Netzhaut antworten optimal auf einen Luminanzkontrast in ihren On- und Off-Regionen. Antworteigenschaften entwickeln sich schrittweise entlang der Sehbahn, indem zunehmend komplexere Eigenschaften des visuellen Reizes extrahiert werden. Die Pionierarbeiten von David Hubel und Torsten Wiesel beschrieben zunächst Orientierung- und Richtungsselektivität von Neuronen in frühen visuellen Kortexarealen. Später fand man Einzelzellen im medialen Temporallappen, die auf komplexe Objekte wie Hände und Gesichter antworten. Die Hirnforschung ist daher lange davon ausgegangen, dass die Repräsentation komplexer Objekte eine natürliche Entfaltung von Konvergenz entlang der Sehbahn darstellt. Zellen, welche auf elementare Merkmale des Stimulus antworteten, bildeten so durch ihr Muster anatomischer Verbindungen schrittweise die spezialisierten Neurone in höheren visuellen Arealen. Diese Sichtweise zeigt allerdings Limitationen auf. Eine beständige Kritik, die an der "Einzelzelldoktrin" geübt wird, ist das sogenannte kombinatorische Problem. Obwohl wir ständig mit einer unbegrenzten Fülle an Kombinationen verschiedener Elemente und Merkmale konfrontiert sind, laufen wir selten Gefahr, Muster und Objekte in einer visuellen Szene nicht zu erkennen. Ist es überhaupt möglich, dass jedes unserer möglichen Perzepte durch die Antwort eines einzelnen hoch spezialisierten Neurons im Hirn kodiert wird? Falls nicht, welcher Mechanismus könnte einen relationalen Code darstellen, der es ermöglicht, die Aktivität verschiedener neuronaler Ensembles zu integrieren? Die Anforderungen an einen solchen Mechanismus treten besonders hervor, wenn man sich die verteilte Struktur der visuellen Verarbeitung verdeutlicht. Die Merkmalsextraktion entlang der Sehbahn führt unvermeidbar zu einer räumlich verstreuten Repräsentation eines visuellen Reizes. Zusätzlich kommen parallele Bahnen neuronaler Verarbeitung im Hirn häufig vor. Es fehlt eine universale Theorie darüber, wie die verteilte Information effizient in eine einzige Wahrnehmung integriert wird. Die Arbeitshypothese hier lautet, dass das Hirn die Zeitdomäne benutzt, um visuelle Informationen zu integrieren und zu verarbeiten. Konkret würden neuronale Ensemble die aus dem Stimulus hervorgehenden Beziehungen durch eine selektive Synchronisation ihrer Aktionspotenziale signalisieren. Synchronisation ist normalerweise mit oszillatorischer Hirnaktivität assoziiert. Besonders die Oszillationen im Gamma Frequenzband sind mit verschiedensten integrativen Prozessen im Sehsystem in Verbindung gebracht worden. Arbeiten an anästhesierten Tieren haben einen auffälligen Anstieg von Energie im Gamma Frequenzband (30-60 Hz) unter visueller Stimulation gezeigt. Kürzlich sind diese Beobachtungen auf Verhaltensstudien ausgeweitet worden, welche die Rolle von Gamma Aktivität bei der für kognitive Prozesse erforderlichen gerichteten Aufmerksamkeit untersuchen. Die ursprüngliche Motivation dieser Arbeit war es, die von Wolf Singer und Mitarbeitern formulierte "binding-bysynchronization (BBS)" Hypothese zu testen. Dies wurde durch die Ableitung neuronaler Antworten in V1 bei Darbietung eines Paars übereinander gelegter Balkengitter ("Plaid" Stimulus) angegangen. Physikalische Manipulationen der Luminanz in Unterregionen des Plaid-Stimulus können die Wahrnehmung zugunsten der Bewegung der Einzelkomponenten (zwei Objekte, die sich übereinander schieben) oder der Bewegung des Gesamtmusters (ein einziges sich in eine gemeinsame Richtung bewegendes Objekt) beeinflussen. Die gleichzeitige Ableitung von zwei Neuronen, die jeweils nur selektiv auf eines der beiden Balkengitter antworteten, ermöglichte es uns, zwei Vorhersagen der BBS Hypothese zu testen. Falls beide V1 Neurone auf dasselbe Balkengitter antworteten, sollten sie ihre Aktivität unabhängig davon, ob das Plaid in Einzelkomponenten oder als Gesamtmuster wahrgenommen würde, synchronisieren. Der Grund dafür wäre, dass beide Neurone auf dasselbe Objekt reagierten. Im zweiten Fall antworten beide Ableitstellen auf jeweils eine der beiden Balkengitterkomponenten. Hier sagt die BBS Hypothese voraus, dass beide ihre Aktivität nur bei Gesamtmusterbewegung synchronisieren würden, da sie nur in dieser Bedingung auf dasselbe Objekt antworten würden. ..

Sensor Fusion in the Perception of Self-Motion

This dissertation has been written at the Max Planck Institute for Biological Cybernetics (Max-Planck-Institut für Biologische Kybernetik) in Tübingen in the department of Prof. Dr. Heinrich H. Bülthoff. The work has universitary support by Prof. Dr. Günther Palm (University of Ulm, Abteilung Neuroinformatik). Main evaluators are Prof. Dr. Günther Palm, Prof. Dr. Wolfgang Becker (University of Ulm, Sektion Neurophysiologie) and Prof. Dr. Heinrich Bülthoff.amp;lt;bramp;gt;amp;lt;bramp;gt; The goal of this thesis was to investigate the integration of different sensory modalities in the perception of self-motion, by using psychophysical methods. Experiments with healthy human participants were to be designed for and performed in the Motion Lab, which is equipped with a simulator platform and projection screen. Results from psychophysical experiments should be used to refine models of the multisensory integration process, with an mphasis on Bayesian (maximum likelihood) integration mechanisms.amp;lt;bramp;gt;amp;lt;bramp;gt; To put the psychophysical experiments into the larger framework of research on multisensory integration in the brain, results of neuroanatomical and neurophysiological experiments on multisensory integration are also reviewed

The Role of The Locus Coeruleus Noradrenergic System in Tracking the Statistics of Rapid Sound Sequences

The sensory world is full of uncertainty; most perception-relevant statistics are highly dynamic, featuring frequently-changing patterns. Rapid adaptation to the everchanging world requires brain sensitivity to environmental changes and resetting of functional neural networks as needed. Norepinephrine (NE) is proposed to mediate this process by initiating functional resetting (Dayan and Yu, 2006; Sara and Bouret, 2012) via the Locus Coeruleus (LC)-NE system. This doctoral thesis employs pupil diameter measurements – a reliable indicator of NE neural activity in the LC (Aston-Jones and Cohen, 2005; Joshi et al. 2016). Human participants listened to sequences of adjoined 50ms tone-pips (adapted from Barascud et al., 2016) containing transitions from random to regular frequency patterns and vice-versa. Participants were instructed to detect occasionally inserted silent gaps, ensuring attention to the auditory stream, not the transition itself. Although both transitions (regular-to-random and random-to-regular) are clearly detectable behaviourally and evoke strong MEG (Barascud et al., 2016), only violations of regularity (prediction errors) appear to elicit pupil responses. Noteworthily, this response is driven by pattern changes and not merely deviant detection. However, stimuli containing pattern emergences (precision increase) evoke no measurable pupil response; this is not due to pre-transition pupillary saturation, as transitions from random patterns to repeating single tones (random-to-repeating) evoke transient pupil dilation. Only when subjects actively reported changes in button-press did random-to-regular transitions evoke pupil dilations. Investigating the effect of task on evoked pupil responses found no response if subjects were not continuously tracking the sequences, e.g. with attention directed to visual or tactile stimuli. Multiple self-replications of these findings provide robust evidence that NE release acts as an automatic switch, resetting the brain’s internal model of the sensory environment and demonstrating that the unexpected uncertainty signalling process operates over much faster timescales than previously known, implicating NE in the fundamental bases of perception