Self-sustained Activity Research Articles

Reconsidering the Notion of Social Justice from an Elite Theory Perspective

ABSTRACT We explore social justice advocacy and education from the vantage point of elite theory as articulated in the works of Gaetano Mosca and Vilfredo Pareto. Elite theory is applied here to re-appraise the explicit and implicit educational means and ends inspired by contemporary social justice along three inter-related dimensions: the place of ideology, particularly of the utopian kind; systems of juridical justice; and the pursuit and maintenance of power by elites. We identify in the utopian stance of social justice the potential for privileging self-sustaining and perpetual activity in the here and now – as a superhuman and heroic project – at the future’s expense. The paper also explores how social justice may disrupt and exploit Mosca’s ‘juridical defence’ (in which power is a check on power). Finally, we suggest that social justice education can be understood as the political positioning of an elite characterised by an instinct for combining ideas and concepts (Pareto’s Class I), over and against elites seeking the preservation of group identities and categories (Class II). These themes are explored in the context of education.

Flexible gating between subspaces in a neural network model of internally guided task switching

Behavioral flexibility relies on the brain’s ability to switch rapidly between multiple tasks, even when the task rule is not explicitly cued but must be inferred through trial and error. The underlying neural circuit mechanism remains poorly understood. We investigated recurrent neural networks (RNNs) trained to perform an analog of the classic Wisconsin Card Sorting Test. The networks consist of two modules responsible for rule representation and sensorimotor mapping, respectively, where each module is comprised of a circuit with excitatory neurons and three major types of inhibitory neurons. We found that rule representation by self-sustained persistent activity across trials, error monitoring and gated sensorimotor mapping emerged from training. Systematic dissection of trained RNNs revealed a detailed circuit mechanism that is consistent across networks trained with different hyperparameters. The networks’ dynamical trajectories for different rules resided in separate subspaces of population activity; the subspaces collapsed and performance was reduced to chance level when dendrite-targeting somatostatin-expressing interneurons were silenced, illustrating how a phenomenological description of representational subspaces is explained by a specific circuit mechanism.

Flexible gating between subspaces in a neural network model of internally guided task switching.

Behavioral flexibility relies on the brain's ability to switch rapidly between multiple tasks, even when the task rule is not explicitly cued but must be inferred through trial and error. The underlying neural circuit mechanism remains poorly understood. We investigated recurrent neural networks (RNNs) trained to perform an analog of the classic Wisconsin Card Sorting Test. The networks consist of two modules responsible for rule representation and sensorimotor mapping, respectively, where each module is comprised of a circuit with excitatory neurons and three major types of inhibitory neurons. We found that rule representation by self-sustained persistent activity across trials, error monitoring and gated sensorimotor mapping emerged from training. Systematic dissection of trained RNNs revealed a detailed circuit mechanism that is consistent across networks trained with different hyperparameters. The networks' dynamical trajectories for different rules resided in separate subspaces of population activity; the subspaces collapsed and performance was reduced to chance level when dendrite-targeting somatostatin-expressing interneurons were silenced, illustrating how a phenomenological description of representational subspaces is explained by a specific circuit mechanism.

Intrinsic motoneuron properties in typical human development.

Motoneuron properties and their firing patterns undergo significant changes throughout development and in response to neuromodulators such as serotonin. Here, we examined the age-related development of self-sustained firing and general excitability of tibialis anterior motoneurons in a young development (7-17years), young adult (18-28years) and adult (32-53years) group, as well as in a separate group of participants taking selective serotonin reuptake inhibitors (SSRIs, aged 11-28years). Self-sustained firing, as measured by ΔF, was larger in the young development (∼5.8Hz, n=20) compared to the young adult (∼4.9Hz, n=13) and adult (∼4.8Hz, n=8) groups, consistent with a developmental decrease in self-sustained firing mediated by persistent inward currents (PIC). ΔF was also larger in participants taking SSRIs (∼6.5Hz, n=9) compared to their age-matched controls (∼5.3Hz, n=26), consistent with increased levels of spinal serotonin facilitating the motoneuron PIC. Participants in the young development and SSRI groups also had higher firing rates and a steeper acceleration in initial firing rates (secondary ranges), consistent with the PIC producing a steeper acceleration in membrane depolarization at the onset of motoneuron firing. In summary, both the young development and SSRI groups exhibited increased intrinsic motoneuron excitability compared to the adults, which, in the young development group, was also associated with a larger unsteadiness in the dorsiflexion torque profiles. We propose several intrinsic and extrinsic factors that affect both motoneuron PICs and cell discharge which vary during development, with a time course similar to the changes in motoneuron firing behaviour observed in the present study. KEY POINTS: Neurons in the spinal cord that activate muscles in the limbs (motoneurons) undergo increases in excitability shortly after birth to help animals stand and walk. We examined whether the excitability of human ankle flexor motoneurons also continues to change from child to adulthood by recording the activity of the muscle fibres they innervate. Motoneurons in children and adolescents aged 7-17years (young development group) had higher signatures of excitability that included faster firing rates and more self-sustained activity compared to adults aged ≥18years. Participants aged 11-28years of age taking serotonin reuptake inhibitors had the highest measures of motoneuron excitability compared to their age-matched controls. The young development group also had more unstable contractions, which might partly be related to the high excitability of the motoneurons.

Reduction and recovery of self-sustained muscle activity after fatiguing plantar flexor contractions.

Persistent inward calcium and sodium currents (PICs) are crucial for initiation and maintenance of motoneuron firing, and thus muscular force. However, there is a lack of data describing the effects of fatiguing exercise on PIC activity in humans. We simultaneously applied tendon vibration and neuromuscular electrical stimulation (VibStim) before and after fatiguing exercise. VibStim induces self-sustained muscle activity that is proposed to result from PIC activation. Twelve men performed 5-s maximal isometric plantar flexor contractions (MVC) with 5-s rests until joint torque was reduced to 70%MVC. VibStim trials consisted of five 2-s trains of neuromuscular electrical stimulation (20 Hz, evoking 10% MVC) of triceps surae with simultaneous Achilles tendon vibration (115 Hz) without voluntary muscle activation. VibStim was applied before (PRE), immediately (POST), 5-min (POST-5), and 10-min (POST-10) after exercise completion. Sustained torque (Tsust) and soleus electromyogram amplitudes (EMG) measured 3 s after VibStim were reduced (Tsust: -59.0%, p<0.001; soleus EMG: -38.4%, p<0.001) but largely recovered by POST-5, and changes in MVC and Tsust were correlated across the four time points (r=0.69; p<0.001). After normalisation to values obtained at the end of the vibration phase to control for changes in fibre-specific force and EMG signal characteristics, decreases in Tsust (-42.9%) and soleus EMG (-22.6%) remained significant and were each correlated with loss and recovery of MVC (r=0.41 and 0.46, respectively). The parallel changes observed in evoked self-sustained muscle activity and force generation capacity provide motivation for future examinations on the potential influence of fatigue-induced PIC changes on motoneuron output.

Reservoir computing using self-sustained oscillations in a locally connected neural network

Understanding how the structural organization of neural networks influences their computational capabilities is of great interest to both machine learning and neuroscience communities. In our previous work, we introduced a novel learning system, called the reservoir of basal dynamics (reBASICS), which features a modular neural architecture (small-sized random neural networks) capable of reducing chaoticity of neural activity and of producing stable self-sustained limit cycle activities. The integration of these limit cycles is achieved by linear summation of their weights, and arbitrary time series are learned by modulating these weights. Despite its excellent learning performance, interpreting a modular structure of isolated small networks as a brain network has posed a significant challenge. Here, we investigate how local connectivity, a well-known characteristic of brain networks, contributes to reducing neural system chaoticity and generates self-sustained limit cycles based on empirical experiments. Moreover, we present the learning performance of the locally connected reBASICS in two tasks: a motor timing task and a learning task of the Lorenz time series. Although its performance was inferior to that of modular reBASICS, locally connected reBASICS could learn a time series of tens of seconds while the time constant of neural units was ten milliseconds. This work indicates that the locality of connectivity in neural networks may contribute to generation of stable self-sustained oscillations to learn arbitrary long-term time series, as well as the economy of wiring cost.

Neural Responses to Repeated Noise Structure in Sounds Are Invariant to Temporal Interruptions

ABSTRACT The ability to extract meaning from acoustic environments requires sensitivity to repeating sound structures. Yet, how events that repeat are encoded and maintained in the brain and how the brain responds to events that reoccur at later points in time is not well understood. In two electroencephalography experiments, participants listened to a longer, ongoing white-noise sound which comprised shorter, frozen noise snippets that repeated at a regular 2-Hz rate. In several conditions, the snippet repetition discontinued for a brief period after which the noise snippet reoccurred. The experiments aimed to answer whether neural activity becomes entrained by the regular repetition of noise snippets, whether entrained neural activity self-sustains during the discontinuation period, and how the brain responds to a reoccurring noise snippet. Results show that neural activity is entrained by the snippet repetition, but there was no evidence for self-sustained neural activity during the discontinuation period. However, the auditory cortex responded with similar magnitude to a noise snippet reoccurring after a brief discontinuation as it responded to a noise snippet for which the snippet repetition had not been discontinued. This response invariance was observed for different onset times of the reoccurring noise snippet relative to the previously established regularity. The results thus demonstrate that the auditory cortex sensitively responds to, and thus maintains a memory trace of, previously learned acoustic noise independent of temporal interruptions.

Effect of short-term plasticity on working memory

The way in which persistent firing activity and synaptic plasticity are orchestrated to underlie working memory in recurrent neural networks is not fully understood. Here, we build a continuous attractor network of pyramidal cells and interneurons to simulate an oculomotor delayed response task. Both short-term facilitation (STF) and short-term depression (STD) manifest at synapses between pyramidal cells. The efficacy of individual synapses depends on the time constants of STF and STD as well as the presynaptic firing rate. Self-sustained firing activity (i.e., a bump attractor) during the delay period encodes the cue position. The bump attractor becomes more robust against random drifts and distractions with enhancing STF or reducing STD. Keeping STF and STD at appropriate levels is crucial for optimizing network performance. Our results suggest that, besides slow recurrent excitation and strong global inhibition, short-term plasticity plays a prominent role in facilitating mnemonic behavior.

The Importance of Cajal's and Lorente de Nó's Neuroscience to the Birth of Cybernetics.

The beginnings of cybernetics were marked by the publication of two papers in 1943. In the first one, Rosenblueth, Wiener, and Bigelow claimed that purposeful behavior is a circular process controlled by negative feedback. In the second seminal paper, McCulloch and Pitts proposed that neurons are interconnected working as logical operators. Both articles raised human-machine analogies and mathematically formulated cognitive mechanisms. These ideas ignited the interest of von Neumann, who was developing the first stored-program computer. Thus, after a preliminary meeting in 1945, a series of meetings were held between 1946 and 1953. The role of the Spanish neurophysiologist Rafael Lorente de Nó in the beginnings of cybernetics is attested not only by his participation in the core members of these Macy conferences but also for his previous description of reverberating circuits formed by a closed chain of internuncial neurons. This was the first neurobiologic demonstration of a feedback loop. Most researchers considered the central nervous system as a mere reflex organ until then; nevertheless, he demonstrated a self-sustained central activity in the nervous system, supporting the idea of self-regulating mechanisms as a key concept not just in machines but also in the brain.

Paradoxical self-sustained dynamics emerge from orchestrated excitatory and inhibitory homeostatic plasticity rules

Self-sustained neural activity maintained through local recurrent connections is of fundamental importance to cortical function. Converging theoretical and experimental evidence indicates that cortical circuits generating self-sustained dynamics operate in an inhibition-stabilized regime. Theoretical work has established that four sets of weights (WE←E, WE←I, WI←E, and WI←I) must obey specific relationships to produce inhibition-stabilized dynamics, but it is not known how the brain can appropriately set the values of all four weight classes in an unsupervised manner to be in the inhibition-stabilized regime. We prove that standard homeostatic plasticity rules are generally unable to generate inhibition-stabilized dynamics and that their instability is caused by a signature property of inhibition-stabilized networks: the paradoxical effect. In contrast, we show that a family of "cross-homeostatic" rules overcome the paradoxical effect and robustly lead to the emergence of stable dynamics. This work provides a model of how-beginning from a silent network-self-sustained inhibition-stabilized dynamics can emerge from learning rules governing all four synaptic weight classes in an orchestrated manner.

Синтез оптической нейроморфной структуры с функциональным разделением нейронов для распределения потоков информации

The work presents analysis of possible application of self-generating neural networks, which can independently generate a topological map of neuron connections while modelling biological neurogenesis, in multi-threaded information communication systems. A basic optical neural network cell is designed on the basis of the applied layered composition performing data processing. A map of neuron connections represents not an ordered structure providing a regular graph for exchange of information between neurons, but a set of cognitive reserve represented as an unconnected set of neuromorphic cells. Modelling of neuron death (apoptosis) and creation of dendrite-axon connections makes it possible to implement a stepwise neural network growth algorithm. Despite challenges in implementing this process, creating a growing network in an optical neural network framework solves the problem of initial forming of the neural network architecture, which greatly simplifies the learning process. Neural network cells used with the network growth algorithm resulted in neural network structures that use internal self-sustaining rhythmic activity to process information. This activity is a result of spontaneously formed closed neural circuits with common neurons among neuronal cells. Such organisation of recirculation memory leads to solutions with reference to such intra-network activity. As a result, response of the network is determined not only by stimuli, but also by the internal state of the network and its rhythmic activity. Network functioning is affected by internal rhythms, which depend on the information passing through the neuron clusters, which results in formation of a specific rhythmic memory. This can be used for tasks that require solutions to be worked out based on certain parameters, but they shall be unreproducible when the network is repeatedly stimulated by the same influences. Such tasks include ensuring information transmission security when using some set of carriers. The task of determining a number of frequencies and their frequency plan depends on external factors. To exclude possible repeating generation of the same carrier allocation, it is necessary to use networks of the configuration under consideration that can influence generation of solutions through the gathered experience.

Detection of focal source and arrhythmogenic substrate from body surface potentials to guide atrial fibrillation ablation.

Focal sources (FS) are believed to be important triggers and a perpetuation mechanism for paroxysmal atrial fibrillation (AF). Detecting FS and determining AF sustainability in atrial tissue can help guide ablation targeting. We hypothesized that sustained rotors during FS-driven episodes indicate an arrhythmogenic substrate for sustained AF, and that non-invasive electrical recordings, like electrocardiograms (ECGs) or body surface potential maps (BSPMs), could be used to detect FS and AF sustainability. Computer simulations were performed on five bi-atrial geometries. FS were induced by pacing at cycle lengths of 120–270 ms from 32 atrial sites and four pulmonary veins. Self-sustained reentrant activities were also initiated around the same 32 atrial sites with inexcitable cores of radii of 0, 0.5 and 1 cm. FS fired for two seconds and then AF inducibility was tested by whether activation was sustained for another second. ECGs and BSPMs were simulated. Equivalent atrial sources were extracted using second-order blind source separation, and their cycle length, periodicity and contribution, were used as features for random forest classifiers. Longer rotor duration during FS-driven episodes indicates higher AF inducibility (area under ROC curve = 0.83). Our method had accuracy of 90.6±1.0% and 90.6±0.6% in detecting FS presence, and 93.1±0.6% and 94.2±1.2% in identifying AF sustainability, and 80.0±6.6% and 61.0±5.2% in determining the atrium of the focal site, from BSPMs and ECGs of five atria. The detection of FS presence and AF sustainability were insensitive to vest placement (±9.6%). On pre-operative BSPMs of 52 paroxysmal AF patients, patients classified with initiator-type FS on a single atrium resulted in improved two-to-three-year AF-free likelihoods (p-value < 0.01, logrank tests). Detection of FS and arrhythmogenic substrate can be performed from ECGs and BSPMs, enabling non-invasive mapping towards mechanism-targeted AF treatment, and malignant ectopic beat detection with likely AF progression.

Generational knowledge and learning in the productive practices of traditional coastal communities, Brazilian Amazon

The present study took place in traditional coastal communities located around and within the Marine Extractive Reserves (RESEX-Mar) of Tracuateua and Araí-Peroba, Northeast of Pará, Brazilian Amazon, in the period between 2019 and 2020, involving thirty families, whose self-sustaining activities depend on family farming, artisanal fishing and crab fishing. The aimed objectives were to investigate the generational knowledges and how descendants learn about them, considering the environmental productive practices of such traditional communities, as well as to reflect on the ways the transmission processes occur to the following generations. The employed method involved a qualitative approach, with field observation and interview techniques. In data collection, the following instruments were used: a script with semi-structured questions to conduct the interviews and field notes taken during observation periods. Data analysis was based on the organization, tabulation and treatment of the linguistic terms collected from the interviews with the support of graphics generated by the IRAMUTEQ Software. Subsequently, content analysis was applied. The results showed there are riches constituted from the existing traditional knowledge, mainly in family farming and artisanal fishing (fish and crab), which are taught and transmitted by families to new generations. Conclusively, the research reveals that in the relationship between human beings and nature there are feelings of belonging perceived from the interviewees' identities, adding symbols and meanings, which are experienced and respected by all.

Mechanisms of distributed working memory in a large-scale network of macaque neocortex.

Neural activity underlying working memory is not a local phenomenon but distributed across multiple brain regions. To elucidate the circuit mechanism of such distributed activity, we developed an anatomically constrained computational model of large-scale macaque cortex. We found that mnemonic internal states may emerge from inter-areal reverberation, even in a regime where none of the isolated areas is capable of generating self-sustained activity. The mnemonic activity pattern along the cortical hierarchy indicates a transition in space, separating areas engaged in working memory and those which do not. A host of spatially distinct attractor states is found, potentially subserving various internal processes. The model yields testable predictions, including the idea of counterstream inhibitory bias, the role of prefrontal areas in controlling distributed attractors, and the resilience of distributed activity to lesions or inactivation. This work provides a theoretical framework for identifying large-scale brain mechanisms and computational principles of distributed cognitive processes.

Active Role of Self-Sustained Neural Activity on Sensory Input Processing: A Minimal Theoretical Model.

A growing body of work has demonstrated the importance of ongoing oscillatory neural activity in sensory processing and the generation of sensorimotor behaviors. It has been shown, for several different brain areas, that sensory-evoked neural oscillations are generated from the modulation by sensory inputs of inherent self-sustained neural activity (SSA). This letter contributes to that strand of research by introducing a methodology to investigate how much of the sensory-evoked oscillatory activity is generated by SSA and how much is generated by sensory inputs within the context of sensorimotor behavior in a computational model. We develop an abstract model consisting of a network of three Kuramoto oscillators controlling the behavior of a simulated agent performing a categorical perception task. The effects of sensory inputs and SSAs on sensory-evoked oscillations are quantified by the cross product of velocity vectors in the phase space of the network under different conditions (disconnected without input, connected without input, and connected with input). We found that while the agent is carrying out the task, sensory-evoked activity is predominantly generated by SSA (93.10%) with much less influence from sensory inputs (6.90%). Furthermore, the influence of sensory inputs can be reduced by 10.4% (from 6.90% to 6.18%) with a decay in the agent's performance of only 2%. A dynamical analysis shows how sensory-evoked oscillations are generated from a dynamic coupling between the level of sensitivity of the network and the intensity of the input signals. This work may suggest interesting directions for neurophysiological experiments investigating how self-sustained neural activity influences sensory input processing, and ultimately affects behavior.

Using an Intervention Mapping Approach to Develop a Program for Preventing High Blood Pressure in a Marginalized Afro-Colombian Population: A Community-Based Participatory Research.

The prevention of high blood pressure (HBP) is an important public health initiative worldwide, since HBP is the main risk factor for cardiovascular diseases and increases the damage caused by coronavirus disease 2019 (COVID-19). We designed, implemented, and evaluated a program to identify effective and sustainable interventions for preventing HBP in a marginalized black population. Our study was conducted in Quibdó, a city in Colombia with the highest poverty rate and located in the Pacific coast, a subregion in Colombia with the highest prevalence of HBP. We followed an intervention mapping framework using a community-based participatory research approach. Focus groups, photovoice, literature reviews, and cross-sectional quantitative surveys were used for data collection. The community chose the time, place, and type of physical activity; led the physical activities; and strengthened their skills in seeking resources in their community to sustain the program. The evaluation was aimed at determining whether the interventions were able to achieve the program’s primary aim. We used a before (September 2016) and after (December 2017) design for the evaluation. To decrease the selection bias and allow comparisons between homogeneous groups, we used a propensity score matching technique. The steps required to create a self-sustaining physical activity program were provided in detail. The pre-post test showed a decrease of the HBP (systolic, 13.4% points; p = 0.018; diastolic, 6.5% points; p = 0.002). The program may be an effective and self-sustaining intervention, and it can be replicated by policymakers and implemented in other population groups.

Looking at the bigger PICture: understanding and counteracting the decline of persistent inward currents in older adults.

Looking at the bigger PICture: understanding and counteracting the decline of persistent inward currents in older adults.

Enhancement of self-sustained muscle activity through external dead space ventilation appears to be associated with hypercapnia

We reported that external dead space ventilation (EDSV) enhanced self-sustained muscle activity (SSMA) of the human soleus muscle, which is an indirect observation of plateau potentials. However, the main factor for EDSV to enhance SSMA remains unclear. The purpose of the present study was to examine the effects of EDSV-induced hypercapnia, hypoxia, and hyperventilation on SSMA. In Experiment 1 (n = 11; normal breathing [NB], EDSV, hypoxia, and voluntary hyperventilation conditions) and Experiment 2 (n = 9; NB and normoxic hypercapnia [NH] conditions), SSMA was evoked by electrical train stimulations of the right tibial nerve and measured using surface electromyography under each respiratory condition. In Experiment 1, SSMA was significantly higher than that in the NB condition only in the EDSV condition (P < 0.05). In Experiment 2, SSMA was higher in the NH condition than in the NB condition (P < 0.05). These results suggest that the EDSV-enhanced SSMA is due to hypercapnia, not hypoxia or increased ventilation.

The role of rebound spikes in the maintenance of self-sustained neural spiking activity

In general, the mechanisms that maintain the activity of neural systems after a triggering stimulus has been removed are not well understood. Different mechanisms involving at the cellular and network levels have been proposed. In this work, based on analysis of a computational model of a spiking neural network, it is proposed that the spike that occurs after a neuron is inhibited (the rebound spike) can be used to sustain the activity in a recurrent inhibitory neural circuit after the stimulation has been removed. It is shown that, in order to sustain the activity, the neurons participating in the recurrent circuit should fire at low frequencies. It is also shown that the occurrence of a rebound spike depends on a combination of factors including synaptic weights, synaptic conductances and the neuron state. We point out that the model developed here is minimalist and does not aim at empirical accuracy. Its purpose is to raise and discuss theoretical issues that could contribute to the understanding of neural mechanisms underlying self-sustained neural activity.

Формирование нейросетевых структур с самоподдерживающейся ритмической активностью с применением искусственного нейрогенеза

Формирование нейросетевых структур с самоподдерживающейся ритмической активностью с применением искусственного нейрогенеза