Ioannis Vlachos

Encoding of conditioned fear in central amygdala inhibitory circuits

The central amygdala (CEA), a nucleus predominantly composed of GABAergic inhibitory neurons, is essential for fear conditioning. How the acquisition and expression of conditioned fear are encoded within CEA inhibitory circuits is not understood. Using in vivo electrophysiological, optogenetic and pharmacological approaches in mice, we show that neuronal activity in the lateral subdivision of the central amygdala (CEl) is required for fear acquisition, whereas conditioned fear responses are driven by output neurons in the medial subdivision (CEm). Functional circuit analysis revealed that inhibitory CEA microcircuits are highly organized and that cell-type-specific plasticity of phasic and tonic activity in the CEl to CEm pathway may gate fear expression and regulate fear generalization. Our results define the functional architecture of CEA microcircuits and their role in the acquisition and regulation of conditioned fear behaviour.

Context-Dependent Encoding of Fear and Extinction Memories in a Large-Scale Network Model of the Basal Amygdala

The basal nucleus of the amygdala (BA) is involved in the formation of context-dependent conditioned fear and extinction memories. To understand the underlying neural mechanisms we developed a large-scale neuron network model of the BA, composed of excitatory and inhibitory leaky-integrate-and-fire neurons. Excitatory BA neurons received conditioned stimulus (CS)-related input from the adjacent lateral nucleus (LA) and contextual input from the hippocampus or medial prefrontal cortex (mPFC). We implemented a plasticity mechanism according to which CS and contextual synapses were potentiated if CS and contextual inputs temporally coincided on the afferents of the excitatory neurons. Our simulations revealed a differential recruitment of two distinct subpopulations of BA neurons during conditioning and extinction, mimicking the activation of experimentally observed cell populations. We propose that these two subgroups encode contextual specificity of fear and extinction memories, respectively. Mutual competition between them, mediated by feedback inhibition and driven by contextual inputs, regulates the activity in the central amygdala (CEA) thereby controlling amygdala output and fear behavior. The model makes multiple testable predictions that may advance our understanding of fear and extinction memories.

Challenges of understanding brain function by selective modulation of neuronal subpopulations

Neuronal networks confront researchers with an overwhelming complexity of interactions between their elements. A common approach to understanding neuronal processing is to reduce complexity by defining subunits and infer their functional role by selectively modulating them. However, this seemingly straightforward approach may lead to confusing results if the network exhibits parallel pathways leading to recurrent connectivity. We demonstrate limits of the selective modulation approach and argue that, even though highly successful in some instances, the approach fails in networks with complex connectivity. We argue to refine experimental techniques by carefully considering the structural features of the neuronal networks involved. Such methods could dramatically increase the effectiveness of selective modulation and may lead to a mechanistic understanding of principles underlying brain function.

Lateralized reward-related visual discrimination in the avian entopallium

In humans and many other animals, the two cerebral hemispheres are partly specialized for different functions. However, knowledge about the neuronal basis of lateralization is mostly lacking. The visual system of birds is an excellent model in which to investigate hemispheric asymmetries as birds show a pronounced left hemispheric advantage in the discrimination of various visual objects. In addition, visual input crosses at the optic chiasm and thus testing of each hemisphere is easily accomplished. We aimed to find a neuronal correlate for three hallmarks of visual lateralization in pigeons: first, the animals learn faster with the right eye–left hemisphere; second, they reach higher performance levels under this condition; third, visually guided behavior is mostly under left hemisphere control. To this end, we recorded from the left and right forebrain entopallium while the animals performed a colour discrimination task. We found that, even before learning, left entopallial neurons were more responsive to visual stimulation. Subsequent discrimination acquisition recruited more neuronal responses in the left entopallium and these cells showed a higher degree of differentiation between the rewarded and the unrewarded stimulus. Thus, differential left–right responses are already present, albeit to a modest degree, before learning. As soon as some cues are associated with reward, however, this asymmetry increases substantially and the higher discrimination ratio of the left hemispheric tectofugal pathway would not only contribute to a higher performance of this hemisphere but could thereby also result in a left hemispheric dominance over downstream motor structures via reward‐associated feedback systems.

Beyond Statistical Significance: Implications of Network Structure on Neuronal Activity

It is a common and good practice in experimental sciences to assess the statistical significance of measured outcomes. For this, the probability of obtaining the actual results is estimated under the assumption of an appropriately chosen null-hypothesis. If this probability is smaller than some threshold, the results are deemed statistically significant and the researchers are content in having revealed, within their own experimental domain, a “surprising” anomaly, possibly indicative of a hitherto hidden fragment of the underlying “ground-truth”. What is often neglected, though, is the actual importance of these experimental outcomes for understanding the system under investigation. We illustrate this point by giving practical and intuitive examples from the field of systems neuroscience. Specifically, we use the notion of embeddedness to quantify the impact of a neurons activity on its downstream neurons in the network. We show that the network response strongly depends on the embeddedness of stimulated neurons and that embeddedness is a key determinant of the importance of neuronal activity on local and downstream processing. We extrapolate these results to other fields in which networks are used as a theoretical framework.

Dynamical state of the network determines the efficacy of single neuron properties in shaping the network activity

Spike patterns are among the most common electrophysiological descriptors of neuron types. Surprisingly, it is not clear how the diversity in firing patterns of the neurons in a network affects its activity dynamics. Here, we introduce the state-dependent stochastic bursting neuron model allowing for a change in its firing patterns independent of changes in its input-output firing rate relationship. Using this model, we show that the effect of single neuron spiking on the network dynamics is contingent on the network activity state. While spike bursting can both generate and disrupt oscillations, these patterns are ineffective in large regions of the network state space in changing the network activity qualitatively. Finally, we show that when single-neuron properties are made dependent on the population activity, a hysteresis like dynamics emerges. This novel phenomenon has important implications for determining the network response to time-varying inputs and for the network sensitivity at different operating points.

Recovery of Dynamics and Function in Spiking Neural Networks with Closed-Loop Control.

There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks (SNNs). Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC, besides steering the system back to a healthy state, also recovers the computations performed by the underlying network. Finally, using our theory we identify the role of single neuron and synapse properties in determining the stability of the closed-loop system.

Neural system prediction and identification challenge

Can we infer the function of a biological neural network (BNN) if we know the connectivity and activity of all its constituent neurons?This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC). We provide the connectivity and activity of all neurons and invite participants (1) to infer the functions implemented (hard-wired) in spiking neural networks (SNNs) by stimulating and recording the activity of neurons and, (2) to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

Dynamical emergence of fear and extinction cells in the amygdala – a computational model

During a typical fear conditioning experiment a neutral stimulus is paired with a fearful one and after multiple trials the former acquires aversive properties. This learning can be suppressed by repeated presentations of the initially neutral stimulus alone (fear extinction). The major brain structure involved in these fear related processes is the amygdaloid complex, but the exact functional mechanisms are not well understood. A recent article reported that two distinct fear and extinction cell populations within the basal nucleus of the amygdala (BA) are exclusively activated during fear conditioning and extinction respectively [1].

The impact of structural embeddedness of neurons on network dynamics

It is a common practice in experimental neuroscience to assess the statistical significance of spiking activity variations, measured from single or multiple neurons. For instance, in behavioral experiments, the probability of an increase in firing rate or correlation strength among a group of neurons is estimated under the assumption that an appropriately chosen null-hypothesis were true. If the probability for observing the experimental results under the null-hypothesis is small, the results are deemed statistically significant and the neural activity is assumed to be functionally related to the task. Thus, it is also implicitly assumed that the statistically significant neural activity must have some effect on the network dynamics. However, this tacit inference is not warranted a priori. That is, the fact that the recorded neuronal activity is not a chance event does not necessarily imply that it will have an impact on local or downstream network activity, particularly when the network is not homogeneously random. Therefore, any strong, or even causal association to behavior is not justified either. We illustrate this largely ignored point by systematically analyzing the responses of 100 simulated spiking neuron networks, each composed of 10,000 neurons, to external stimulation. All networks had different topologies, however, the average connectivity parameters were kept constant. We measured the population activity and related it to network properties that characterize the way in which the stimulated neurons are embedded in their local environment. To estimate the embeddedness of neurons, we used known metrics from graph theory such as centrality and k-shell decomposition. Our results indicate that the impact neuronal events have on local or downstream network dynamics strongly depends on the structural embeddedness of participating neurons. We discuss potential implications of our findings for the analysis of neuronal activity. We also point out additional hurdles that need to be overcome in extracting network function, which go beyond knowledge of the structure and dynamics.