PRINCIPLES OF INFORMATION PROCESSING IN NEURONAL AVALANCHES

How the brain processes information is poorly understood. It has been suggested that the imbalance of excitation and inhibition (E/I) can significantly affect information processing in the brain. Neuronal avalanches, a type of spontaneous activity recently discovered, have been ubiquitously observed in vitro and in vivo when the cortical network is in the E/I balanced state. In this dissertation, I experimentally demonstrate that several properties regarding information processing in the cortex, i.e. the entropy of spontaneous activity, the information transmission between stimulus and response, the diversity of synchronized states and the discrimination of external stimuli, are optimized when the cortical network is in the E/I balanced state, exhibiting neuronal avalanche dynamics. These experimental studies not only support the hypothesis that the cortex operates in the critical state, but also suggest that criticality is a potential principle of information processing in the cortex. Further, we study the interaction structure in population neuronal dynamics, and discovered a special structure of higher order interactions that are inherent in the neuronal dynamics

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. ..

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

Attention, Uncertainty, and Free-Energy

We suggested recently that attention can be understood as inferring the level of uncertainty or precision during hierarchical perception. In this paper, we try to substantiate this claim using neuronal simulations of directed spatial attention and biased competition. These simulations assume that neuronal activity encodes a probabilistic representation of the world that optimizes free-energy in a Bayesian fashion. Because free-energy bounds surprise or the (negative) log-evidence for internal models of the world, this optimization can be regarded as evidence accumulation or (generalized) predictive coding. Crucially, both predictions about the state of the world generating sensory data and the precision of those data have to be optimized. Here, we show that if the precision depends on the states, one can explain many aspects of attention. We illustrate this in the context of the Posner paradigm, using the simulations to generate both psychophysical and electrophysiological responses. These simulated responses are consistent with attentional bias or gating, competition for attentional resources, attentional capture and associated speed-accuracy trade-offs. Furthermore, if we present both attended and non-attended stimuli simultaneously, biased competition for neuronal representation emerges as a principled and straightforward property of Bayes-optimal perception

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Biophysical mechanisms of frequency-dependence and its neuromodulation in neurons in oscillatory networks

In response to oscillatory input, many isolated neurons exhibit a preferred frequency response in their voltage amplitude and phase shift. Membrane potential resonance (MPR), a maximum amplitude in a neuron’s input impedance at a non-zero frequency, captures the essential subthreshold properties of a neuron, which may provide a coordinating mechanism for organizing the activity of oscillatory neuronal networks around a given frequency. In the pyloric central pattern generator network of the crab Cancer borealis, for example, the pacemaker group pyloric dilator neurons show MPR at a frequency that is correlated with the network frequency. This dissertation uses the crab pyloric CPG to examine how, in one neuron type, interactions of ionic currents, even when expressed at different levels, can produce consistent MPR properties, how MPR properties are modified by neuromodulators and how such modifications may lead to distinct functional effects at different network frequencies. In the first part of this dissertation it is demonstrated that, despite the extensive variability of individual ionic currents in a neuron type such as PD, these currents can generate a consistent impedance profile as a function of input frequency and therefore result in stable MPR properties. Correlated changes in ionic current parameters are associated with the dependence of MPR on the membrane potential range. Synaptic inputs or neuromodulators that shift the membrane potential range can modify the interaction of multiple resonant currents and therefore shift the MPR frequency. Neuromodulators change the properties of voltage-dependent ionic currents. Since ionic current interactions are nonlinear, the modulation of excitability and the impedance profile may depend on all ionic current types expressed by the neuron. MPR is generated by the interaction of positive and negative feedback effects due to fast amplifying and slower resonant currents. Neuromodulators can modify existing MPR properties to generate antiresonance (a minimum amplitude response). In the second part of this dissertation, it is shown that the neuropeptide proctolin produces antiresonance in the follower lateral pyloric neuron, but not in the PD neuron. This finding is inconsistent with the known influences of proctolin. However, a novel proctolin-activated ionic current is shown to produce the antiresonance. Using linear models, antiresonance is then demonstrated to amplify MPR in synaptic partner neurons, indicating a potential function in the pyloric network. Neuromodulators are state dependent, so that their action may depend on the prior activity history of the network. It is shown that state-dependence may arise in part from the time-dependence of an inactivating inward current targeted by the neuromodulator proctolin. Due to the kinetics of inactivation, this current advances the burst phase and increases the duty cycle of the neuron, but mainly at higher network frequencies. These results demonstrate that the effect of neuromodulators on MPR in individual neuron types depends on the nonlinear interaction of modulator-activated and other ionic currents as well as the activation of currents with frequency-dependent properties. Consequently, the action of neuromodulators on the output of oscillatory networks may depend on the frequency of oscillations and be predictable from the MPR properties of the network neurons