Can extremism guarantee pluralism?

Many models have been proposed to explain opinion formation in groups of individuals; most of these models study opinion propagation as the interaction between nodes/agents in a social network. Opinion formation is a complex process and a realistic model should also take into account the important feedbacks that the opinions of the agents have on the structure of the social networks and on the characteristics of the opinion dynamics. In this paper we will show that associating to different agents different kinds of interconnections and different interacting behaviours can lead to interesting scenarios, like the coexistence of several opinion clusters, namely pluralism. In our model agents have opinions uniformly and continuously distributed between two extremes. The social network is formed through a social aggregation mechanism including the segregation process of the extremists that results in many real communities. We show how this process affects the opinion dynamics in the whole society. In the opinion evolution we consider the different predisposition of single individuals to interact and to exchange opinion with each other; we associate to each individual a different tolerance threshold, depending on its own opinion: extremists are less willing to interact with individuals with strongly different opinions and to change significantly their ideas. A general result is obtained: when there is no interaction restriction, the opinion always converges to uniformity, but the same is happening whenever a strong segregation process of the extremists occurs. Only when extremists are forming clusters but these clusters keep interacting with the rest of the society, the survival of a wide opinion range is guaranteed.Comment: 20 pages, 10 figure

Can Extremism Guarantee Pluralism?

Many models have been proposed to explain the opinion formation in a group of individuals; most of these models study the opinion propagation as the interaction between nodes/agents in a social network. Opinion formation is a very complex process and a realistic model should also take into account the important feedbacks that the opinions of the agents have on the structure of the social networks and on the characteristics of the opinion dynamics. In this paper we will show that associating to different agents different kind of interconnections and different interacting behaviour can lead to interesting scenarios, like the co-existence of several opinion clusters, namely pluralism. In our model agents have opinions uniformly and continuously distributed between two extremes. The social network is formed through a social aggregation mechanism including the segregation process of the extremists that results in many real communities. We show how this process affects opinion dynamics in the whole society. In the opinion evolution we consider the different predisposition of single individuals to interact and to to modify each other's opinions; we associate to each individual a different tolerance threshold, depending on its own opinion: extremists are less willing to interact with individuals with strongly different opinions and to change significantly their ideas. A general result is obtained: when there is no interaction restriction, the opinion always converges to uniformity, but the same is happening whenever a strong segregation process of the extremists occurs. Only when extremists are forming clusters but these clusters keep interacting with the rest of the society, the survival of a wide opinion range is guaranteed.Extremists, Segregation, Opinion Dynamics

From neuronal networks to behavior: dynamics of spontaneous activity and onset of movement in the leech

Animal behavior was once seen as a chain of reactions to stimuli from the environment. From chemotaxis in bacteria to mammals withdrawing from painful stimuli, most of the actions taken by animals are clearly driven by external inputs. Reflexes were among the first phenomena to be studied to have an insight on the dynamics of the nervous system. Later, a step forward was the discovery of central pattern generators: once a behavior is started by a stimulus, some neuronal networks are able to maintain it without further inputs from the environment. The nervous system of all animals, however, is so complex that is displaying a rich dynamics even in the absence of external inputs or, in a more realistic situation, when no single input is able to drive a clear-cut reaction. In the same way, at the motor output level, animals keep moving in the absence of evident stimuli. These spontaneous behaviors are still far from being understood. Difficult problems are often easier to solve in simple systems. The leech has a relatively simple nervous system, composed of ~103 neurons disposed in a regular structure, but at the same time displays a variety of different behaviors. It seems then a good preparation to approach the spontaneous dynamics problem. The aim of my PhD research is to describe the spontaneous behavior of the leech and the spontaneous activity of its nervous system. A first, necessary step for this study was to develop a method of automatic classification and analysis of the leech movements. Thanks to this method we described accurately the properties of the different behaviors: we focused particularly on the largely unknown irregular exploratory behavior, which is found to display a broad range of oscillation frequencies and displacement speeds, but with some recurrent movement patterns. Finding the complete list of the leech spontaneous behaviors, and the probability of the transitions between them, it was possible to demonstrate that decision making in the leech is a Markovian process. The spontaneous activity in the isolated leech ganglion was found to be characterized by long-term correlations and a large variability in bursts size and duration. The same dynamics was observed in dissociated culture of rat hippocampal neurons, despite the difference in the structure between the two networks. We studied the effects of pharmacological modulations of inhibitory and excitatory processes on the spontaneous activity, and the role of single identified motor neurons in spontaneous bursts. Finally we proposed a simple statistical model accounting for experimental results. We studied then the spontaneous activity of the leech ganglion when it was connected to the other ganglia and in the semi-intact moving animal. Inputs received from the head and tail brain caused a drastic change in the activity of the ganglion, increasing synchronization among neurons and leading to a regime dominated by very large bursts. By recording at the same the movements of the leech and its nervous activity it was possible to have a better understanding of the relationship between the motor neuron bursts and the onset of movements

Phase analysis method for burst onset prediction

The response of bursting neurons to fluctuating inputs is usually hard to predict, due to their strong nonlinearity. For the same reason, decoding the injected stimulus from the activity of a bursting neuron is generally difficult. In this paper we propose a method describing (for neuron models) a mechanism of phase coding relating the burst onsets with the phase profile of the input current. This relation suggests that burst onset may provide a way for postsynaptic neurons to track the input phase. Moreover, we define a method of phase decoding to solve the inverse problem and estimate the likelihood of burst onset given the input state. Both methods are presented here in a unified framework, describing a complete coding-decoding procedure. This procedure is tested by using different neuron models, stimulated with different inputs (stochastic, sinusoidal, up, and down states). The results obtained show the efficacy and broad range of application of the proposed methods. Possible applications range from the study of sensory information processing, in which phase-of-firing codes are known to play a crucial role, to clinical applications such as deep brain stimulation, helping to design stimuli in order to trigger or prevent neural bursting

How gamma-band oscillatory activity participates in encoding of naturalistic stimuli in random networks of excitatory and inhibitory neurons

The network was a randomly connected network of excitatory and inhibitory neurons, that can exhibit both asynchronous and synchronous irregular activity, in which the global activity oscillates in time with a frequency that depends both on synaptic time constants and on the excitation/inhibition balance [1]. The LFP was modeled as a linear combination of excitatory and inhibitory currents. This choice allows us to reproduce the dependence of the LFP spectrum on frequency and contrast as recorded in vivo [2]

Spontaneous Electrical Activity and Behavior in the Leech Hirudo Medicinalis

In the absence of external stimuli, animals explore the environment by performing irregular movements, but the neuronal mechanisms underlying this arrhythmic motion are largely unknown. In this paper, we studied the relationship between the spontaneous neuronal activity in the leech (Hirudo medicinalis) and its behavior. We analyzed the electrical activity of isolated ganglia, chains of two connected ganglia, and semi-intact preparations. The spontaneous electrical activity in ganglia was characterized by the occurrence of irregular bursts of spikes with variable duration and size. Properties of these bursts were modified by synaptic inputs arriving from the neighboring ganglia and from the two primitive brains located in the head and tail. In fact, in semi-intact preparations, unusually large bursts of spikes occurring spontaneously were recorded and caused the leech to move even in the absence of any external sensory stimulation. These large bursts appear to act as internal triggers controlling the spontaneous leech behavior and determining the duration of stereotypical motor patterns

Dopamine depletion leads to pathological synchronization of distinct basal ganglia loops in the beta band

: Motor symptoms of Parkinson's Disease (PD) are associated with dopamine deficits and pathological oscillation of basal ganglia (BG) neurons in the β range ([12-30] Hz). However, how dopamine depletion affects the oscillation dynamics of BG nuclei is still unclear. With a spiking neurons model, we here capture the features of BG nuclei interactions leading to oscillations in dopamine-depleted condition. We highlight that both the loop between subthalamic nucleus (STN) and Globus Pallidus pars externa (GPe) and the loop between striatal fast spiking and medium spiny neurons and GPe display resonances in the β range, and synchronize to a common β frequency through interaction. Crucially, the synchronization depends on dopamine depletion: the two loops are largely independent for high levels of dopamine, but progressively synchronize as dopamine is depleted due to the increased strength of the striatal loop. The model is validated against recent experimental reports on the role of cortical inputs, STN and GPe activity in the generation of β oscillations. Our results highlight the role of the interplay between the GPe-STN and the GPe-striatum loop in generating sustained β oscillations in PD subjects, and explain how this interplay depends on the level of dopamine. This paves the way to the design of therapies specifically addressing the onset of pathological β oscillations