Reproduced neuron-like excitability and bursting synchronization of memristive Josephson junctions loaded inductor

Employing electronic component including memristor and Josephson junction to mimic biological neuron or synapse has elicited intense research in recent years. Neurons described by nonlinear oscillators can exhibit complex electrical activities. Josephson junctions are excellent candidates for neuron-inspired components because of their physical properties with low energy costs and high efficiency. In this paper, we revisit a prior work on memristive Josephson junction (MJJ) to identify the dynamical mechanisms to mimic neuron-like excitability and spiking. The inductive memristive Josephson junction (L-MJJ) model is further developed by adding an inductor with internal resistor. It is found that the L-MJJ model can reproduce square-wave bursting of the classical neuronal model from the neurodynamics point of view. The coupling L-MJJ oscillators can achieve in-phase and antiphase bursting synchronization similar with nonlinear coupling neurons. From the framework of nonlinear dynamics theory, this work aspires to build effective bridge between superconducting physics and theoretical neuroscience. Obtained results confirm the potential feasibility of this junction in designing a neuron-inspired computation to explore dynamics of larger-scale neuromorphic network.

The Human Side of Artificial Intelligence

Artificial moral agents raise complex ethical questions both in terms of the potential decisions they may make as well as the inputs that create their cognitive architecture. There are multiple differences between human and artificial cognition which create potential barriers for artificial moral agency, at least as understood anthropocentrically and it is unclear that artificial moral agents should emulate human cognition and decision-making. It is conceptually possible for artificial moral agency to emerge that reflects alternative ethical methodologies without creating ontological challenges or existential crises for human moral agents.

A simple three layer excitatory-inhibitory neuronal network for temporal decision-making

Humans and animals do not only keep track of time intervals but they can also make decisions about durations. Temporal bisection is a psychophysical task that is widely used to assess the latter ability via categorization of durations as short or long. Many existing models of performance in temporal bisection primarily account for choice proportions and tend to overlook the associated response times. We propose a time-cell neural network that implements both interval timing and temporal categorization. The proposed model can keep track of time intervals based on lurching wave activity, it can learn the reference durations along with their association with different categorization responses, and finally, it can carry out the comparison of arbitrary intermediate durations to the reference durations. We compared the model's predictions about choice behavior and response times to the empirical data previously gathered from rats. We showed that this time-cell neural network can predict the canonical behavioral signatures of temporal bisection performance. Specifically, (a) the proposed model can account for the sigmoidal relationship between the probability of the long choices and the test durations, (b) the superposition of choice functions on a relative time scale, (c) the localization of the point of subjective equality at the geometric mean of the reference durations, and (d) the differential modulation of short and long categorization response times as a function of the test durations.

Modeling the effect of the cephalic phase of insulin secretion on glucose metabolism

The nervous system has a significant impact in glucose homeostasis and endocrine pancreatic secretion in humans, especially during the cephalic phase of insulin release (CPIR); that is, before a meal is absorbed. However, the underlying mechanisms of this neural-pancreatic interaction are not well understood and therefore often neglected, despite their significance to achieving an optimal glucose control. As a result, the dynamics of insulin release from the pancreas are currently described by mathematical models that reproduce the behavior of the β cells using exclusively glucose levels and other hormones as inputs. To bridge this gap, we have combined, for the first time, metabolic and neural mathematical models in a unified system to reproduce to a great extent the ideal glucoregulation observed in healthy subjects. Our results satisfactorily replicate the CPIR and its impact during the post-absorptive phase. Furthermore, the proposed model gives insight into the physiological interaction between the brain and the pancreas in healthy people and suggests the potential of considering the neural information for restoring glucose control in people with diabetes. Graphical Abstract (a) Physiological scenario. Diagram of the biological interaction among the most important organs involved in glucose control during meal intake. (b) Scheme of the unified bio-inspired neural-metabolic model. Each of the boxes represents one subsystem of the model. The pink shades boxes depicts the novel subsystems introduced to the current metabolic models (grey shaded boxes). Insulin-related action and mass fluxes (solid black lines) and glucose-related action and mass flux (dotted black lines) are depicted to show the relationship among the blocks. I(t), Ic(t), G(t) and SI related to plasma insulin, plasma cephalic insulin, plasma glucose and insulin sensitivity, respectively.

A common goodness-of-fit framework for neural population models using marked point process time-rescaling

A critical component of any statistical modeling procedure is the ability to assess the goodness-of-fit between a model and observed data. For spike train models of individual neurons, many goodness-of-fit measures rely on the time-rescaling theorem and assess model quality using rescaled spike times. Recently, there has been increasing interest in statistical models that describe the simultaneous spiking activity of neuron populations, either in a single brain region or across brain regions. Classically, such models have used spike sorted data to describe relationships between the identified neurons, but more recently clusterless modeling methods have been used to describe population activity using a single model. Here we develop a generalization of the time-rescaling theorem that enables comprehensive goodness-of-fit analysis for either of these classes of population models. We use the theory of marked point processes to model population spiking activity, and show that under the correct model, each spike can be rescaled individually to generate a uniformly distributed set of events in time and the space of spike marks. After rescaling, multiple well-established goodness-of-fit procedures and statistical tests are available. We demonstrate the application of these methods both to simulated data and real population spiking in rat hippocampus. We have made the MATLAB and Python code used for the analyses in this paper publicly available through our Github repository at https://github.com/Eden-Kramer-Lab/popTRT .

Unmasking saccadic uncrowding

Stimuli that are briefly presented around the time of saccades are often perceived with spatiotemporal distortions. These distortions do not always have deleterious effects on the visibility and identification of a stimulus. Recent studies reported that when a stimulus is the target of an intended saccade, it is released from both masking and crowding. Here, we investigated pre-saccadic changes in single and crowded letter recognition performance in the absence (Experiment 1) and the presence (Experiment 2) of backward masks to determine the extent to which saccadic "uncrowding" and "unmasking" mechanisms are similar. Our results show that pre-saccadic improvements in letter recognition performance are mostly due to the presence of masks and/or stimulus transients which occur after the target is presented. More importantly, we did not find any decrease in crowding strength before impending saccades. A simplified version of a dual-channel neural model, originally proposed to explain masking phenomena, with several saccadic add-on mechanisms, could account for our results in Experiment 1. However, this model falls short in explaining how saccades drastically reduced the effect of backward masking (Experiment 2). The addition of a remapping mechanism that alters the relative spatial positions of stimuli was needed to fully account for the improvements observed when backward masks followed the letter stimuli. Taken together, our results (i) are inconsistent with saccadic uncrowding, (ii) strongly support saccadic unmasking, and (iii) suggest that pre-saccadic letter recognition is modulated by multiple perisaccadic mechanisms with different time courses.

Effects of reciprocal inhibitory coupling in model neurons

Central pattern generators are neuron networks that produce vital rhythmic motor outputs such as those observed in mastication, walking and breathing. Their activity patterns depend on the tuning of their intrinsic ionic conductances, their synaptic interconnectivity and entrainment by extrinsic neurons. The influence of two commonly found synaptic connectivities--reciprocal inhibition and electrical coupling--are investigated here using a neuron model with subthreshold oscillation capability, in different firing and entrainment regimes. We study the dynamics displayed by a network of a pair of neurons with various firing regimes, coupled by either (i) only reciprocal inhibition or by (ii) electrical coupling first and then reciprocal inhibition. In both scenarios a range of coupling strengths for the reciprocal inhibition is tested, and in general the neuron with the lower firing rate stops spiking for strong enough inhibitory coupling, while the faster neuron remains active. However, in scenario (ii) the originally slower neuron stops spiking at weaker inhibitory coupling strength, suggesting that the electrical coupling introduces an element of instability to the two-neuron network.

Transient-state studies and neural modeling of the removal of a gas-phase pollutant mixture in a biotrickling filter

The removal efficiency (RE) of gas-phase hydrogen sulfide (H), methanol (M) and α-pinene (P) in a biotrickling filter (BTF) was modeled using artificial neural networks (ANNs). The inlet concentrations of H, M, P, unit flow and operation time were used as the model inputs, while the outputs were the RE of H, M and P, respectively. After testing and validating the results, an optimal network topology of 5-8-3 was obtained. The model predictions were analyzed using Casual index (CI) values. M removal in the BTF was influenced positively by the inlet concentration of M in mixture (CI=3.79), while the removal of P and H were influenced more by the time of BTF operation (CI=25.36, 15.62). The BTF was subjected to different types of short-term shock-loads: 5-h shock-load of HMP mixture simultaneously, and 2.5-h shock-load of either H, M, or P, individually. It was observed that, short-term shock-loads of individual pollutants (M or H) did not significantly affect their own removal, but the removal of P was affected by 50%. The results from this study also show the sensitiveness of the well-acclimated BTF to handle sudden load variations and also revival capability of the BTF when pre-shock conditions were restored.

Computational modeling of stuttering caused by impairments in a basal ganglia thalamo-cortical circuit involved in syllable selection and initiation

Atypical white-matter integrity and elevated dopamine levels have been reported for individuals who stutter. We investigated how such abnormalities may lead to speech dysfluencies due to their effects on a syllable-sequencing circuit that consists of basal ganglia (BG), thalamus, and left ventral premotor cortex (vPMC). "Neurally impaired" versions of the neurocomputational speech production model GODIVA were utilized to test two hypotheses: (1) that white-matter abnormalities disturb the circuit via corticostriatal projections carrying copies of executed motor commands and (2) that dopaminergic abnormalities disturb the circuit via the striatum. Simulation results support both hypotheses: in both scenarios, the neural abnormalities delay readout of the next syllable's motor program, leading to dysfluency. The results also account for brain imaging findings during dysfluent speech. It is concluded that each of the two abnormality types can cause stuttering moments, probably by affecting the same BG-thalamus-vPMC circuit.