Symmetry-Breaking Bifurcations of the Information Bottleneck and Related Problems.

In this paper, we investigate the bifurcations of solutions to a class of degenerate constrained optimization problems. This study was motivated by the Information Bottleneck and Information Distortion problems, which have been used to successfully cluster data in many different applications. In the problems we discuss in this paper, the distortion function is not a linear function of the quantizer. This leads to a challenging annealing optimization problem, which we recast as a fixed-point dynamics problem of a gradient flow of a related dynamical system. The gradient system possesses an SN symmetry due to its invariance in relabeling representative classes. Its flow hence passes through a series of bifurcations with specific symmetry breaks. Here, we show that the dynamical system related to the Information Bottleneck problem has an additional spurious symmetry that requires more-challenging analysis of the symmetry-breaking bifurcation. For the Information Bottleneck, we determine that when bifurcations occur, they are only of pitchfork type, and we give conditions that determine the stability of the bifurcating branches. We relate the existence of subcritical bifurcations to the existence of first-order phase transitions in the corresponding distortion function as a function of the annealing parameter, and provide criteria with which to detect such transitions.

Towards a dynamic clamp for neurochemical modalities.

The classic dynamic clamp technique uses a real-time electrical interface between living cells and neural simulations in order to investigate hypotheses about neural function and structure. One of the acknowledged drawbacks of that technique is the limited control of the cells' chemical microenvironment. In this manuscript, we use a novel combination of nanosensor and microfluidic technology and microfluidic and neural simulations to add sensing and control of chemical concentrations to the dynamic clamp technique. Specifically, we use a microfluidic lab-on-a-chip to generate distinct chemical concentration gradients (ions or neuromodulators), to register the concentrations with embedded nanosensors and use the processed signals as an input to simulations of a neural cell. The ultimate goal of this project is to close the loop and provide sensor signals to the microfluidic lab-on-a-chip to mimic the interaction of the simulated cell with other cells in its chemical environment.

Internodal mechanism of pathological afterdischarges in myelinated axons.

INTRODUCTION: Recent optical recordings of transmembrane potentials in the axons of pyramidal neurons have shown that the internodal action potentials (APs) predicted in our previous studies do exist. These novel processes are not well understood. In this study we aim to clarify electrical phenomena in peripheral myelinated axons (MAs). METHODS: We used a multi-cable Hodgkin-Huxley-type model to simulate MAs with potassium channels that were either normal or inhibited along a short region of the internodal membrane. A brief stimulus was applied to the first node. RESULTS: We demonstrated peculiarities in the internodal APs induced by a saltatory AP: They existed across internodal membranes, were detectable in periaxonal space but not in intracellular space, propagated continuously, collided near the mid-internodes, and produced internodal sources of afterdischarges. CONCLUSIONS: These results highlight the importance of the MA internodal regions as new therapeutic targets for avoiding afterdischarges provoked by reduced axonal fast potassium channel expression.

Identification of sleep and circadian alternative polyadenylation sites associated with APA-linked human brain disorders.

Sleep and circadian rhythm disruptions are comorbid features of many pathologies and can negatively influence numerous health conditions, including degenerative diseases, metabolic illnesses, cancer, and various neurological disorders. Genetic association studies linking sleep and circadian disturbances with disease susceptibility have mainly focused on changes in gene expression due to mutations, such as single-nucleotide polymorphisms. Thus, associations between sleep and/or circadian rhythm and alternative polyadenylation (APA), particularly in the context of other health challenges, are largely undescribed. APA is a process that generates various transcript isoforms from the same gene, resulting in effects on mRNA translation, stability, localization, and subsequent function. Here, we have identified unique APAs in rat brain that exhibit time-of-day-dependent oscillations in expression as well as APAs that are altered by sleep deprivation and the subsequent recovery period. Genes affected by APA usage include Mapt/Tau, Ntrk2, Homer1A, Sin3band Sorl. Sorl1 has two APAs which cycle with a 24 h period, one additional APA cycles with a 12 h period and one more that is reduced during recovery sleep. Finally, we compared sleep- or circadian-associated APAs with recently described APA-linked brain disorder susceptibility genes and found 46 genes in common.

Sensitivity analysis of point neuron model simulations implemented on neuromorphic hardware.

With the ongoing growth in the field of neuro-inspired computing, newly arriving computational architectures demand extensive validation and testing against existing benchmarks to establish their competence and value. In our work, we break down the validation step into two parts-(1) establishing a methodological and numerical groundwork to establish a comparison between neuromorphic and conventional platforms and, (2) performing a sensitivity analysis on the obtained model regime to assess its robustness. We study the neuronal dynamics based on the Leaky Integrate and Fire (LIF) model, which is built upon data from the mouse visual cortex spanning a set of anatomical and physiological constraints. Intel Corp.'s first neuromorphic chip "Loihi" serves as our neuromorphic platform and results on it are validated against the classical simulations. After setting up a model that allows a seamless mapping between the Loihi and the classical simulations, we find that Loihi replicates classical simulations very efficiently with high precision. This model is then subjected to the second phase of validation, through sensitivity analysis, by assessing the impact on the cost function as values of the significant model parameters are varied. The work is done in two steps-(1) assessing the impact while changing one parameter at a time, (2) assessing the impact while changing two parameters at a time. We observe that the model is quite robust for majority of the parameters with slight change in the cost function. We also identify a subset of the model parameters changes which make the model more sensitive and thus, need to be defined more precisely.

A possible link of oxaliplatin-induced neuropathy with potassium channel deficit.

INTRODUCTION: The neurotoxic side effects of oxaliplatin (a reference drug in the treatment of digestive tract tumors) can force suspension of treatment. The mechanisms of neuropathy are unclear. We aimed to simulate oxaliplatin-induced hyperactivity in myelinated axons (MA) based on published experimental data. METHODS: A Hodgkin-Huxley-type multi-cable MA model was used, which took into account active internodal processes and accumulation of ions in MA with 21 nodes. RESULTS: Even a very short (110-220 µm) internodal region devoid of potassium channels was sufficient to produce after-discharges in response to a saltatory action potential. An increase in the density of sodium channels, slowdown of their inactivation, and negative shifts along one node-internode region of the voltage dependence of sodium and potassium activation and of sodium inactivation induced no after-discharge. CONCLUSION: A combination of sodium channel blockers with drugs that obstruct the blockage of potassium channels or contribute to their opening could be effective in preventing oxaliplatin-induced "hyperexcitability."

Temporal encoding in a nervous system.

We examined the extent to which temporal encoding may be implemented by single neurons in the cercal sensory system of the house cricket Acheta domesticus. We found that these neurons exhibit a greater-than-expected coding capacity, due in part to an increased precision in brief patterns of action potentials. We developed linear and non-linear models for decoding the activity of these neurons. We found that the stimuli associated with short-interval patterns of spikes (ISIs of 8 ms or less) could be predicted better by second-order models as compared to linear models. Finally, we characterized the difference between these linear and second-order models in a low-dimensional subspace, and showed that modification of the linear models along only a few dimensions improved their predictive power to parity with the second order models. Together these results show that single neurons are capable of using temporal patterns of spikes as fundamental symbols in their neural code, and that they communicate specific stimulus distributions to subsequent neural structures.

Generation of FHIR-Based International Patient Summaries from ELGA Data.

Patient summaries grant healthcare providers a concise overview of a patient's status. This paper showcases to which degree International Patient Summaries (IPS) represented in HL7 FHIR format can be generated using data from the nationwide Austrian Electronic Health Record system ELGA. A solution is presented which enables the automated software-assembled generation of an IPS using the FHIR Mapping Language. The generated document successfully validates against the IPS profiles. Our results show that all required IPS sections can be supplied from ELGA data.

Mapping and Validating a Point Neuron Model on Intel's Neuromorphic Hardware Loihi.

Neuromorphic hardware is based on emulating the natural biological structure of the brain. Since its computational model is similar to standard neural models, it could serve as a computational accelerator for research projects in the field of neuroscience and artificial intelligence, including biomedical applications. However, in order to exploit this new generation of computer chips, we ought to perform rigorous simulation and consequent validation of neuromorphic models against their conventional implementations. In this work, we lay out the numeric groundwork to enable a comparison between neuromorphic and conventional platforms. "Loihi"-Intel's fifth generation neuromorphic chip, which is based on the idea of Spiking Neural Networks (SNNs) emulating the activity of neurons in the brain, serves as our neuromorphic platform. The work here focuses on Leaky Integrate and Fire (LIF) models based on neurons in the mouse primary visual cortex and matched to a rich data set of anatomical, physiological and behavioral constraints. Simulations on classical hardware serve as the validation platform for the neuromorphic implementation. We find that Loihi replicates classical simulations very efficiently with high precision. As a by-product, we also investigate Loihi's potential in terms of scalability and performance and find that it scales notably well in terms of run-time performance as the simulated networks become larger.

Mapping and Validating a Point Neuron Model on Intel's Neuromorphic Hardware Loihi.

Neuromorphic hardware is based on emulating the natural biological structure of the brain. Since its computational model is similar to standard neural models, it could serve as a computational accelerator for research projects in the field of neuroscience and artificial intelligence, including biomedical applications. However, in order to exploit this new generation of computer chips, we ought to perform rigorous simulation and consequent validation of neuromorphic models against their conventional implementations. In this work, we lay out the numeric groundwork to enable a comparison between neuromorphic and conventional platforms. "Loihi"-Intel's fifth generation neuromorphic chip, which is based on the idea of Spiking Neural Networks (SNNs) emulating the activity of neurons in the brain, serves as our neuromorphic platform. The work here focuses on Leaky Integrate and Fire (LIF) models based on neurons in the mouse primary visual cortex and matched to a rich data set of anatomical, physiological and behavioral constraints. Simulations on classical hardware serve as the validation platform for the neuromorphic implementation. We find that Loihi replicates classical simulations very efficiently with high precision. As a by-product, we also investigate Loihi's potential in terms of scalability and performance and find that it scales notably well in terms of run-time performance as the simulated networks become larger.

Corrigendum: Mapping and validating a point neuron model on intel's neuromorphic hardware Loihi.

[This corrects the article DOI: 10.3389/fnins.2022.883360.].

Characterizing the fine structure of a neural sensory code through information distortion.

We present an application of the information distortion approach to neural coding. The approach allows the discovery of neural symbols and the corresponding stimulus space of a neuron or neural ensemble simultaneously and quantitatively, making few assumptions about the nature of either code or relevant features. The neural codebook is derived by quantitizing sensory stimuli and neural responses into small reproduction sets, and optimizing the quantization to minimize the information distortion function. The application of this approach to the analysis of coding in sensory interneurons involved a further restriction of the space of allowed quantitizers to a smaller family of parametric distributions. We show that, for some cells in this system, a significant amount of information is encoded in patterns of spikes that would not be discovered through analyses based on linear stimulus-response measures.

Evolution of teleost fish retroviruses: characterization of new retroviruses with cellular genes.

The interactions between retroviruses and their hosts can be of a beneficial or detrimental nature. Some endogenous retroviruses are involved in development, while others cause disease. The Genome Parsing Suite (GPS) is a software tool to track and trace all Retroid agents in any sequenced genome (M. A. McClure et al., Genomics 85:512-523, 2005). Using the GPS, the retroviral content was assessed in four model teleost fish. Eleven new species of fish retroviruses are identified and characterized. The reverse transcriptase protein sequences were used to reconstruct a fish retrovirus phylogeny, thereby, significantly expanding the epsilon-retrovirus family. Most of these novel retroviruses encode additional genes, some of which are homologous to cellular genes that would confer viral advantage. Although the fish divergence is much more ancient, retroviruses began infecting fish genomes approximately 4 million years ago.

A possible mechanism of repetitive firing of myelinated axon.

A unique mechanism is proposed according to which processes within the internodal axolemma are responsible for repetitive activation of myelinated axon with deficit of internodal potassium conductance. A numerical simulation of activity in axon with 21 nodes was performed. The axon was represented by cables for axoplasmic and periaxonal spaces. Accumulation and diffusion of ions were taken into account. Fine segmentation of each internode (338 segments) allowed simulation of internodal activation in response to a normal saltatorial action potential initiated by a short stimulus. The internodal membrane without potassium conductance experienced considerable depolarization. This resulted in formation of a transition zone and significant currents that caused repetitive activation of the internode and neighbor node. Decline of periaxonal sodium concentration during the spike production or lowering of sodium channel density decreased the sodium currents. As a result, the interspike intervals increased up to cessation of the burst. The cessation was reversible.

Spatial and temporal jitter distort estimated functional properties of visual sensory neurons.

The functional properties of neural sensory cells or small neural ensembles are often characterized by analyzing response-conditioned stimulus ensembles. Many widely used analytical methods, like receptive fields (RF), Wiener kernels or spatio-temporal receptive fields (STRF), rely on simple statistics of those ensembles. They also tend to rely on simple noise models for the residuals of the conditional ensembles. However, in many cases the response-conditioned stimulus set has more complex structure. If not taken explicitly into account, it can bias the estimates of many simple statistics, and lead to erroneous conclusions about the functionality of a neural sensory system. In this article, we consider sensory noise in the visual system generated by small stimulus shifts in two dimensions (2 spatial or 1-space 1-time jitter). We model this noise as the action of a set of translations onto the stimulus that leave the response invariant. The analysis demonstrates that the spike-triggered average is a biased estimator of the model mean, and provides a de-biasing method. We apply this approach to observations from the stimulus/response characteristics of cells in the cat visual cortex and provide improved estimates of the structure of visual receptive fields. In several cases the new estimates differ substantially from the classic receptive fields, to a degree that may require re-evaluation of the functional description of the associated cells.

Dejittered spike-conditioned stimulus waveforms yield improved estimates of neuronal feature selectivity and spike-timing precision of sensory interneurons.

What is the meaning associated with a single action potential in a neural spike train? The answer depends on the way the question is formulated. One general approach toward formulating this question involves estimating the average stimulus waveform preceding spikes in a spike train. Many different algorithms have been used to obtain such estimates, ranging from spike-triggered averaging of stimuli to correlation-based extraction of "stimulus-reconstruction" kernels or spatiotemporal receptive fields. We demonstrate that all of these approaches miscalculate the stimulus feature selectivity of a neuron. Their errors arise from the manner in which the stimulus waveforms are aligned to one another during the calculations. Specifically, the waveform segments are locked to the precise time of spike occurrence, ignoring the intrinsic "jitter" in the stimulus-to-spike latency. We present an algorithm that takes this jitter into account. "Dejittered" estimates of the feature selectivity of a neuron are more accurate (i.e., provide a better estimate of the mean waveform eliciting a spike) and more precise (i.e., have smaller variance around that waveform) than estimates obtained using standard techniques. Moreover, this approach yields an explicit measure of spike-timing precision. We applied this technique to study feature selectivity and spike-timing precision in two types of sensory interneurons in the cricket cercal system. The dejittered estimates of the mean stimulus waveforms preceding spikes were up to three times larger than estimates based on the standard techniques used in previous studies and had power that extended into higher-frequency ranges. Spike timing precision was approximately 5 ms.

Transient inability to distinguish between faces: electrophysiologic studies.

It is not known with certainty at which level of face processing by the cortex the distinction between a familiar and an unfamiliar face is made. Subdural electrodes were implanted under the fusiform gyrus of the right temporal lobe in a patient who developed an unusual inability to distinguish differences between faces as part of the epileptic aura ("all faces looked the same"). A cortical region located posterior to the epileptic focus was identified that exhibited a maximum evoked response to the presentation of facial images (N165), but not to objects, scenes, or character strings. Evoked potentials elicited by a variety of visual images indicated that any perturbation away from novel whole-face stimuli produced submaximal responses from this region of the right temporal lobe. Electrical stimulation of this region resulted in an impairment of face discrimination. It was found that presentation of familiar faces (grandmother, treating physician) produced a different response from that observed for novel faces. These observations demonstrate that within 165 msec of face presentation, and before the conscious precept of face familiarity has formed, this cortical region has already begun to distinguish between a familiar and an unfamiliar face.

Inhibition does not affect the timing code for vocalizations in the mouse auditory midbrain.

Many animals use a diverse repertoire of complex acoustic signals to convey different types of information to other animals. The information in each vocalization therefore must be coded by neurons in the auditory system. One way in which the auditory system may discriminate among different vocalizations is by having highly selective neurons, where only one or two different vocalizations evoke a strong response from a single neuron. Another strategy is to have specific spike timing patterns for particular vocalizations such that each neural response can be matched to a specific vocalization. Both of these strategies seem to occur in the auditory midbrain of mice. The neural mechanisms underlying rate and time coding are unclear, however, it is likely that inhibition plays a role. Here, we examined whether inhibition is involved in shaping neural selectivity to vocalizations via rate and/or time coding in the mouse inferior colliculus (IC). We examined extracellular single unit responses to vocalizations before and after iontophoretically blocking GABAA and glycine receptors in the IC of awake mice. We then applied a number of neurometrics to examine the rate and timing information of individual neurons. We initially evaluated the neuronal responses using inspection of the raster plots, spike-counting measures of response rate and stimulus preference, and a measure of maximum available stimulus-response mutual information. Subsequently, we used two different event sequence distance measures, one based on vector space embedding, and one derived from the Victor/Purpura D q metric, to direct hierarchical clustering of responses. In general, we found that the most salient feature of pharmacologically blocking inhibitory receptors in the IC was the lack of major effects on the functional properties of IC neurons. Blocking inhibition did increase response rate to vocalizations, as expected. However, it did not significantly affect spike timing, or stimulus selectivity of the studied neurons. We observed two main effects when inhibition was locally blocked: (1) Highly selective neurons maintained their selectivity and the information about the stimuli did not change, but response rate increased slightly. (2) Neurons that responded to multiple vocalizations in the control condition, also responded to the same stimuli in the test condition, with similar timing and pattern, but with a greater number of spikes. For some neurons the information rate generally increased, but the information per spike decreased. In many of these neurons, vocalizations that generated no responses in the control condition generated some response in the test condition. Overall, we found that inhibition in the IC does not play a substantial role in creating the distinguishable and reliable neuronal temporal spike patterns in response to different vocalizations.

Plasma neuropeptide Y levels differ in distinct diabetic conditions.

Neuropeptide Y (NPY) is an important hormone in appetite regulation. Although the contribution of NPY to metabolic disease has been previously demonstrated, there are only a few reports addressing NPY plasma levels under distinct diabetic conditions. In this study we evaluated NPY plasma levels in diabetes mellitus type 2 (DM2) patients with (n=34) and without (n=34) diabetic polyneuropathy (PNP) and compared these with age and gender matched healthy controls (n=34). We also analyzed NPY plasma levels in gestational diabetes mellitus (GDM) patients with age and pregnancy-week matched controls with normal glucose tolerance (NGT). NPY concentration was determined using a commercially available radioimmunoassay kit. In addition, metabolic parameters of DM2 and GDM patients were recorded. One-way ANOVA tests with appropriate post hoc corrections showed elevated levels of NPY in DM2 patients with and without PNP when compared with those of healthy controls (122.32±40.86 and 117.33±29.92 vs. 84.65±52.17 pmol/L; p<0.001, p<0.005, respectively). No significant difference was observed between diabetic patients with and without PNP. The NPY levels were similar in the GDM group and in pregnant women with NGT (74.87±14.36 vs. 84.82±51.13 pmol/L, respectively). Notably, the NPY concentration correlated positively with insulin levels in DM2 patients (R=0.35, p<0.01). Our data suggest a potential involvement of circulating NPY in DM2 pathology.