Memory replay in balanced recurrent networks

Complex patterns of neural activity appear during up-states in the neocortex and sharp waves in the hippocampus, including sequences that resemble those during prior behavioral experience. The mechanisms underlying this replay are not well understood. How can small synaptic footprints engraved by experience control large-scale network activity during memory retrieval and consolidation? We hypothesize that sparse and weak synaptic connectivity between Hebbian assemblies are boosted by pre-existing recurrent connectivity within them. To investigate this idea, we connect sequences of assemblies in randomly connected spiking neuronal networks with a balance of excitation and inhibition. Simulations and analytical calculations show that recurrent connections within assemblies allow for a fast amplification of signals that indeed reduces the required number of inter-assembly connections. Replay can be evoked by small sensory-like cues or emerge spontaneously by activity fluctuations. Global—potentially neuromodulatory—alterations of neuronal excitability can switch between network states that favor retrieval and consolidation.BMBF, 01GQ1001A, Verbundprojekt: Bernstein Zentrum für Computational Neuroscience, Berlin - "Präzision und Variabilität" - Teilprojekt A2, A3, A4, A8, B6, Zentralprojekt und ProfessurBMBF, 01GQ0972, Verbundprojekt: Bernstein Fokus Lernen - Zustandsabhängigkeit des Lernens, TP 2 und 3BMBF, 01GQ1201, Lernen und Gedächtnis in balancierten SystemenDFG, 103586207, GRK 1589: Verarbeitung sensorischer Informationen in neuronalen Systeme

Community-based benchmarking improves spike rate inference from two-photon calcium imaging data

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience

Network mechanisms underlying sharp wave ripples and memory replay

Komplexe Muster neuronaler Aktivität entstehen während der Sharp-wave Ripples (SWRs) im Hippocampus und während der Up States im Neokortex (Zuständen mit hoher Aktivität). Sequenzen von Verhalten, die in der Vergangenheit erlebt wurden, werden während des komplexen Musters abgespielt. Die zugrunde liegenden Mechanismen sind nicht gründlich erforscht: Wie können kleine synaptische Veränderungen die großflächige Netzwerkaktivität während des Gedächtnisabrufes und der Gedächtniskonsolidierung kontrollieren? Im ersten Teil dieser Abhandlung wird die Hypothese aufgestellt, dass eine schwache synaptische Konnektivität zwischen Hebbschen Assemblies von der bereits vorhandenen rekurrenten Konnektivität gefördert wird. Diese Hypothese wird auf folgende Weise geprüft: die vorwärts gekoppelten Assembly-Sequenzen werden in neuronale Netzwerke eingebettet, mit einem Gleichgewicht zwischen exzitatorischer und inhibitorischer Aktivität. Simulationen und analytische Berechnungen haben gezeigt, dass rekurrente Verbindungen innerhalb der Assemblies zu einer schnelleren Signalverstärkung führen, was eine Reduktion der notwendigen Verbindungen zwischen den Assemblies zur Folge hat. Diese Aktivität kann entweder von kleinen sensorisch ähnlichen Inputs hervorgerufen werden oder entsteht spontan infolge von Aktivitätsschwankungen. Globale -- möglicherweise neuromodulatorische -- Änderungen der neuronalen Erregbarkeit können daher die Netzwerkzustände steuern, die Gedächnisabruf und die Konsolidierung begünstigen. Der zweite Teil der Arbeit geht der Herkunft der SWRs nach, die in vitro beobachtet wurden. Neueste Studien haben gezeigt, dass SWR-ähnliche Erscheinungen durch optogenetische Stimulation der Subpopulationen von inhibitorischen Neuronen hervorgerufen werden können (Schlingloff et al., 2014). Um diese Ergebnisse zu erklären wird ein de-inhibierendes Schaltkreis-Modell diskutiert, das die beobachteten Populationsausbrüche generieren kann. Die Auswirkungen der pharmakologischen GABAergischen Modulatoren auf die SWR-Häufigkeit werden in vitro untersucht. Die gewonnenen Ergebnisse wurden in Rahmen des Schaltkreis-Modells analysiert. Insbesondere wird den folgenden Fragen nachgegangen: Wie unterdrückt Gabazine, ein GABA_A-Rezeptor-Antagonist, die Entwicklung von SWRs? Wird das Zeitintervall zwischen SWRs durch die Dynamik der GABA_B Rezeptoren moduliert?Complex patterns of neural activity appear during up-states in the neocortex and sharp-wave ripples (SWRs) in the hippocampus, including sequences that resemble those during prior behavioral experience. The mechanisms underlying this replay are not well understood. How can small synaptic footprints engraved by experience control large-scale network activity during memory retrieval and consolidation? In the first part of this thesis, I hypothesise that sparse and weak synaptic connectivity between Hebbian assemblies are boosted by pre-existing recurrent connectivity within them. To investigate this idea, sequences of assemblies connected in a feedforward manner are embedded in random neural networks with a balance of excitation and inhibition. Simulations and analytical calculations show that recurrent connections within assemblies allow for a fast amplification of signals that indeed reduces the required number of inter-assembly connections. Replay can be evoked by small sensory-like cues or emerge spontaneously by activity fluctuations. Global--potentially neuromodulatory--alterations of neuronal excitability can switch between network states that favor retrieval and consolidation. The second part of this thesis investigates the origin of the SWRs observed in in-vitro models. Recent studies have demonstrated that SWR-like events can be evoked after optogenetic stimulation of subpopulations of inhibitory neurons (Schlingloff et al., 2014; Kohus et al., 2016). To explain these results, a 3-population model is discussed as a hypothetical disinhibitory circuit that could generate the observed population bursts. The effects of pharmacological GABAergic modulators on the SWR incidence in vitro are analysed. The results are discussed in the light of the proposed disinhibitory circuit. In particular, how does gabazine, a GABA_A receptor antagonist, suppress the generation of SWRs? Another explored question is whether the slow dynamics of GABA_B receptors is modulating the time scale of the inter-event intervals

Autonomous Switching of Top-down and Bottom-up Attention Selection for Vision Guided Mobile Robots

Abstract — In this paper an autonomous switching between two basic attention selection mechanisms, top-down and bottom-up, is proposed, substituting manual switching. This approach fills the gab in object search using conventional topdown biased bottom-up attention selection: the latter one fails, if a group of objects is searched whose appearances can not be uniquely described by low-level features used in bottomup computation models. Two internal robot states, observing and operating, are included to determine the visual selection behavior. A vision guided mobile robot, equipped with an active stereo camera, is used to demonstrate our strategy and evaluate the performance experimentally. I

Evoked replay.

<p>Assembly-sequence activation as a function of the feedforward <i>p</i>_ff and the recurrent <i>p</i>_rc connectivities. The color code denotes the quality of replay, that is, the number of subsequent groups firing without bursting (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005359#sec015" target="_blank">Materials and Methods</a>). The black curve corresponds to the critical connectivity required for a replay where the slope <i>c</i> of the transfer function (See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005359#sec015" target="_blank">Materials and Methods</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005359#pcbi.1005359.e006" target="_blank">Eq 1</a>) is matched manually to fit the simulation results for connectivities <i>p</i>_rc = 0.08 and <i>p</i>_ff = 0.04. The slope <i>c</i> is also estimated analytically (dashed white line). The raster plots (<b>a-f</b>) illustrate the dynamic regimes observed for different connectivity values; neurons above the gray line belong to the background neurons.</p

Network connectivity.

<p><b>A:</b> Schematic of an assembly <i>i</i> consisting of an excitatory (<i>E</i>_<i>i</i>) and an inhibitory (<i>I</i>_<i>i</i>) population. Red and blue lines indicate excitatory and inhibitory connections, respectively. The symbols <i>w</i> and −<i>kw</i> denote total synaptic couplings between populations. <b>B:</b> Sketch of network connectivity. The inhomogeneous network is randomly connected with connection probability <i>p</i>_rand. A feedforward structure consisting of 10 assemblies (only <i>i</i> − 1 and <i>i</i> shown) is embedded into the network. Each assembly is formed by recurrently connecting its neurons with probability <i>p</i>_rc. Subsequent assemblies are connected with feedforward probability <i>p</i>_ff between their excitatory neurons. <b>C:</b> Embedded structure as a function of connectivities.</p

Pattern completion.

<p><b>A:</b> Quality of replay after partial activation of the first group for cue size 60% (left panel) and 20% (middle) as a function of feedforward and recurrent connectivity. The right-most panel shows the quality replay after a cue activation (20% and 60%) as a function of the recurrent connectivity (<i>p</i>_rc) while the feedforward connectivity is constant (<i>p</i>_ff = 0.05). <b>B:</b> Examples of network activity during 60% (left) and 20% (right) cue activation. The top and bottom raster plots correspond to assembly sequences with higher (<i>p</i>_rc = 0.10, top) and lower (<i>p</i>_rc = 0.06, bottom) recurrent connectivity, highlighted in A with white and black rectangles, respectively. <b>C:</b> State-space portraits representing the pulse-packet propagation. The activity in each group is quantified by the fraction of firing excitatory neurons (<i>α</i>) and the standard deviation of their spike times (<i>σ</i>). The initial stimulations are denoted with small black dots while the colored dots denote the response of the first group to the stimulations; red dot if the whole sequence is activated, and blue otherwise. Stimulations in the region with white background result in replays, while stimulating in the gray region results in no replay. The black arrows illustrate the evolution of pulse packets during the replays in B. Top: <i>p</i>_rc = 0.10; bottom: <i>p</i>_rc = 0.06.</p

Symmetric assembly sequence.

<p><b>A:</b> Schematic of an assembly sequence with symmetric connections between groups. <b>B:</b> Virtual rat position on a linear track (top) and the corresponding neuronal activity (bottom) as a function of time for 2 seconds. The rat rests at position “b” for half a second, then moves from “b” to “e” with constant speed for one second, where it rests for another 500 ms. While the rat is immobile at both ends of the track, a positive current input <i>I</i>^<i>e</i> = 2 pA is applied to the excitatory population of the first and last assembly as shown by the red background in the raster plot. Spontaneous replays start from the cued assemblies. During exploration, however, the network activity is decreased by a current <i>I</i>^<i>e</i> = −10 pA injected to the whole excitatory population, denoted with a blue horizontal bar. Strong sensory input during traversal activates the location-specific assemblies but does not result in any replay. The timing and location of the stimulations is denoted with red vertical bars in the raster plot. Recurrent and feedforward connectivities are <i>p</i>_rc = 0.15 and <i>p</i>_ff = 0.03, respectively.</p

Assembly-sequence activation for various group sizes and connectivities.

<p><b>A:</b> Simulation results for the quality of replay. <b>B:</b> Rate of spontaneous replay. <b>C:</b> Synchrony. <b>D:</b> Coefficient of variation <b>E:</b> Firing rate. <i>ρ</i>₀ = 5 spikes/sec is the target firing rate. In C, D, and E quantities are averaged over the neurons in the last group of the sequence. The black line is an analytical estimate for the evoked replay as in Figs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005359#pcbi.1005359.g003" target="_blank">3</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005359#pcbi.1005359.g005" target="_blank">5</a>.</p

Feedforward conductance versus feedforward connectivity.

<p><b>A:</b> Quality of replay as a function of connectivity and synaptic strength. <b>B:</b> The replay as a function of connectivity and total feedforward conductance input shows that the propagation is independent of connectivity as long as the total feed-forward input is kept constant. <b>C:</b> Spontaneous network dynamics described by the rate of spontaneous replay, synchrony, CV, and firing rate.</p