scholarly journals Is realistic neuronal modeling realistic?

2016 ◽  
Vol 116 (5) ◽  
pp. 2180-2209 ◽  
Author(s):  
Mara Almog ◽  
Alon Korngreen
Keyword(s):  
Single Neuron ◽  
Ad Hoc ◽  
Pyramidal Neurons ◽  
Scientific Models ◽  
Test Case ◽  
Single Neurons ◽  
Neuron Models ◽  
Neuronal Modeling ◽  
Basic Premise ◽  
Successful Model

Scientific models are abstractions that aim to explain natural phenomena. A successful model shows how a complex phenomenon arises from relatively simple principles while preserving major physical or biological rules and predicting novel experiments. A model should not be a facsimile of reality; it is an aid for understanding it. Contrary to this basic premise, with the 21st century has come a surge in computational efforts to model biological processes in great detail. Here we discuss the oxymoronic, realistic modeling of single neurons. This rapidly advancing field is driven by the discovery that some neurons don't merely sum their inputs and fire if the sum exceeds some threshold. Thus researchers have asked what are the computational abilities of single neurons and attempted to give answers using realistic models. We briefly review the state of the art of compartmental modeling highlighting recent progress and intrinsic flaws. We then attempt to address two fundamental questions. Practically, can we realistically model single neurons? Philosophically, should we realistically model single neurons? We use layer 5 neocortical pyramidal neurons as a test case to examine these issues. We subject three publically available models of layer 5 pyramidal neurons to three simple computational challenges. Based on their performance and a partial survey of published models, we conclude that current compartmental models are ad hoc, unrealistic models functioning poorly once they are stretched beyond the specific problems for which they were designed. We then attempt to plot possible paths for generating realistic single neuron models.

scholarly journals A Single-Neuron: Current Trends and Future Prospects

Cells ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1528 ◽  
Author(s):  
Pallavi Gupta ◽  
Nandhini Balasubramaniam ◽  
Hwan-You Chang ◽  
Fan-Gang Tseng ◽  
Tuhin Subhra Santra
Keyword(s):  
Single Neuron ◽  
Microelectrode Array ◽  
Electrophysiological Recording ◽  
Single Neurons ◽  
Future Prospects ◽  
Neuron Models ◽  
Bulk Analysis ◽  
Essential Information ◽  
Functional Features ◽  
The Brain

The brain is an intricate network with complex organizational principles facilitating a concerted communication between single-neurons, distinct neuron populations, and remote brain areas. The communication, technically referred to as connectivity, between single-neurons, is the center of many investigations aimed at elucidating pathophysiology, anatomical differences, and structural and functional features. In comparison with bulk analysis, single-neuron analysis can provide precise information about neurons or even sub-neuron level electrophysiology, anatomical differences, pathophysiology, structural and functional features, in addition to their communications with other neurons, and can promote essential information to understand the brain and its activity. This review highlights various single-neuron models and their behaviors, followed by different analysis methods. Again, to elucidate cellular dynamics in terms of electrophysiology at the single-neuron level, we emphasize in detail the role of single-neuron mapping and electrophysiological recording. We also elaborate on the recent development of single-neuron isolation, manipulation, and therapeutic progress using advanced micro/nanofluidic devices, as well as microinjection, electroporation, microelectrode array, optical transfection, optogenetic techniques. Further, the development in the field of artificial intelligence in relation to single-neurons is highlighted. The review concludes with between limitations and future prospects of single-neuron analyses.

Automatic Sorting for Multi-Neuronal Activity Recorded With Tetrodes in the Presence of Overlapping Spikes

2003 ◽  
Vol 89 (4) ◽  
pp. 2245-2258 ◽  
Author(s):  
Susumu Takahashi ◽  
Yuichiro Anzai ◽  
Yoshio Sakurai
Keyword(s):  
Single Neuron ◽  
Pyramidal Neurons ◽  
Spike Sorting ◽  
Single Neurons ◽  
Functional Connections ◽  
Present Procedure ◽  
Sorting Technique ◽  
Non Gaussian ◽  
Behaving Monkeys ◽  
Spike Waveforms

Multi-neuronal recording is a powerful electrophysiological technique that has revealed much of what is known about the neuronal interactions in the brain. However, it is difficult to detect precise spike timings, especially synchronized simultaneous firings, among closely neighboring neurons recorded by one common electrode because spike waveforms overlap on the electrode when two or more neurons fire simultaneously. In addition, the non-Gaussian variability (nonstationarity) of spike waveforms, typically seen in the presence of so-called complex spikes, limits the ability to sort multi-neuronal activities into their single-neuron components. Because of these problems, the ordinary spike-sorting techniques often give inaccurate results. Our previous study has shown that independent component analysis (ICA) can solve these problems and separate single-neuron components from multi-neuronal recordings. The ICA has, however, one serious limitation that the number of separated neurons must be less than the number of electrodes. The present study combines the ICA and the efficiency of the ordinary spike-sorting technique (k-means clustering) to solve the spike-overlapping and the nonstationarity problems with no limitation on the number of single neurons to be separated. First, multi-neuronal activities are sorted into an overly large number of clusters by k-means clustering. Second, the sorted clusters are decomposed by ICA. Third, the decomposed clusters are progressively aggregated into a minimal set of putative single neurons based on similarities of basis vectors estimated by ICA. We applied the present procedure to multi-neuronal waveforms recorded with tetrodes composed of four microwires in the prefrontal cortex of awake behaving monkeys. The results demonstrate that there are functional connections among neighboring pyramidal neurons, some of which fire in a precise simultaneous manner and that precisely time-locked monosynaptic connections are working between neighboring pyramidal neurons and interneurons. Detection of these phenomena suggests that the present procedure can sort multi-neuronal activities, which include overlapping spikes and realistic non-Gaussian variability of spike waveforms, into their single-neuron components. We processed several types of synthesized data sets in this procedure and confirmed that the procedure was highly reliable and stable. The present method provides insights into the local circuit bases of excitatory and inhibitory interactions among neighboring neurons.

Single Neuron Models: Interneurons

2010 ◽  
pp. 399-422 ◽  
Author(s):  
Frances Skinner ◽  
Fernanda Saraga
Keyword(s):  
Single Neuron ◽  
Neuron Models

scholarly journals Optical measurement of physiological sodium currents in the axon initial segment

2020 ◽  
Author(s):  
Luiza Filipis ◽  
Marco Canepari
Keyword(s):  
Temporal Resolution ◽  
Initial Segment ◽  
Pyramidal Neurons ◽  
Optical Measurement ◽  
Brain Slices ◽  
Axon Initial Segment ◽  
Neuron Models ◽  
Pixel Resolution ◽  
Fluorescence Measurements ◽  
Kinetics Of

ABSTRACTIn most neurons of the mammalian central nervous system, the action potential (AP) is triggered in the axon initial segment (AIS) by a fast Na+ current mediated by voltage-gated Na+ channels. The intracellular Na+ increase associated with the AP has been measured using fluorescent Na+ indicators, but with insufficient resolution to resolve the Na+ current in the AIS. In this article, we report the critical improvement in temporal resolution of the Na+ imaging technique allowing the direct experimental measurement of Na+ currents in the AIS. We determined the AIS Na+ current, from fluorescence measurements at temporal resolution of 100 µs and pixel resolution of half a micron, in pyramidal neurons of the layer-5 of the somatosensory cortex from brain slices of the mouse. We identified a subthreshold current before the AP, a fast inactivating current peaking during the rise of the AP and a persistent current during the AP repolarisation. We correlated the kinetics of the current at different distances from the soma with the kinetics of the somatic AP. We quantitatively compared the experimentally measured Na+ current with the current obtained by computer simulation of published NEURON models and we show how the present approach can lead to the correct estimate of the native behaviour of Na+ channels. Thus, it is expected that the present method will be adopted to investigate the function of different channel types under physiological or pathological conditions.

scholarly journals Event-based pattern detection in active dendrites

2019 ◽  
Author(s):  
Johannes Leugering ◽  
Pascal Nieters ◽  
Gordon Pipa
Keyword(s):  
Single Neuron ◽  
Logic Gate ◽  
Dendritic Tree ◽  
Dendritic Morphology ◽  
Neuron Models ◽  
Plateau Potentials ◽  
Computational Function ◽  
Movement Trajectories ◽  
Timing Variability ◽  
Long Timescales

AbstractMany behavioural tasks require an animal to integrate information on a slow timescale that can exceed hundreds of milliseconds. How this is realized by neurons with membrane time constants on the order of tens of milliseconds or less remains an open question. We show, how the interaction of two kinds of events within the dendritic tree, excitatory postsynaptic potentials and locally generated dendritic plateau potentials, can allow a single neuron to detect specific sequences of spiking input on such slow timescales. Our conceptual model reveals, how the morphology of a neuron’s dendritic tree determines its computational function, which can range from a simple logic gate to the gradual integration of evidence to the detection of complex spatio-temporal spike-sequences on long timescales. As an example, we illustrate in a simulated navigation task how this mechanism can even allow individual neurons to reliably detect specific movement trajectories with high tolerance for timing variability. We relate our results to conclusive findings in neurobiology and discuss implications for both experimental and theoretical neuroscience.Author SummaryThe recognition of patterns that span multiple timescales is a critical function of the brain. This is a conceptual challenge for all neuron models that rely on the passive integration of synaptic inputs and are therefore limited to the rigid millisecond timescale of post-synaptic currents. However, detailed biological measurements recently revealed that single neurons actively generate localized plateau potentials within the dendritic tree that can last hundreds of milliseconds. Here, we investigate single-neuron computation in a model that adheres to these findings but is intentionally simple. Our analysis reveals how plateaus act as memory traces, and their interaction as defined by the dendritic morphology of a neuron gives rise to complex non-linear computation. We demonstrate how this mechanism enables individual neurons to solve difficult, behaviorally relevant tasks that are commonly studied on the network-level, such as the detection of variable input sequences or the integration of evidence on long timescales. We also characterize computation in our model using rate-based analysis tools, demonstrate why our proposed mechanism of dendritic computation cannot be detected under this analysis and suggest an alternative based on plateau timings. The interaction of plateau events in dendritic trees is, according to our argument, an elementary principle of neural computation which implies the need for a fundamental change of perspective on the computational function of neurons.

Comments on Maddieson's ‘Revision of the IPA: Linguo-labials as a test case’

1987 ◽  
Vol 17 (1) ◽  
pp. 31-32 ◽  
Author(s):  
John Kelly
Keyword(s):  
Ad Hoc ◽  
Spoken Language ◽  
Test Case ◽  
Theoretical Reason ◽  
Front Line ◽  
Level I ◽  
Letter Shape

The IPA alphabet is, first and foremost, a device for use in the making of impressionistic records of spoken language events. There is no alternative device in widespread use. It is neither full nor systematic as a device for this purpose, but can be used remarkably effectively if supplemented with ad hoc extensions and adaptations. Such adaptations need to be explicit if used in published material, but they need not be standard. They do need, though, to be sensible. Although the IPA letter-shapes are not systematic, the articulatory classification that underlies them is. Within this classification it would be perverse to relate a ‘linguo-labial’ to, say, the category ‘velar’, and so a representation based on the letter-shape ‘k’ would be undesirable. Given these limits, though, I can see no theoretical reason why ‘linguo-labials’ should not be written in a number of ways. [P‥] etc. is one way. It is not a particularily good one, in my view, as [‥] is a diacritic that distinguishes a PASSIVE articulator, and this passive articulator is nowhere involved in ‘linguo-labials’. But I would happily accept [P‥]. It would be desirable for a degree of uniformity to apply in PUBLISHED material, but this is a matter more for editors and for publishers than for front-line linguists. At the PHONETIC level, I can't see that it matters whether we use [P‥] or, say, [‡] in published material: and other suggestions will no doubt be forthcoming, PRACTICAL considerations are paramount here, some solutions being easier to print, type and read than others.

Hierarchal Fuzzy Logic Controller and Internet of Things (IoT) Information

2017 ◽  
Vol 6 (3) ◽  
pp. 59-86 ◽  
Author(s):  
Rabie A. Ramadan ◽  
Ahmed B. Altamimi
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Ad Hoc ◽  
Health Care Organizations ◽  
Test Case ◽  
Weather Information ◽  
Huge Data ◽  
Vehicle Ad Hoc Networks ◽  
Disease Spreading ◽  
Hoc Networks

With the advances of networks and sensing technologies, it is possible to benefit from the surrounding environment's data in enhancing peoples' life. Currently, we have different types of networks such as Wireless Sensor Networks (WSNs), Vehicle Ad Hoc Networks (VANETs), Cellular Networks (CNs), and Social Networks (SNs) along with underlying computing such as Cloud computing. These types of networks provide huge data about the surrounding environments including weather information, peoples' relations, peoples' interest, and location information. This paper examines the suitability of hierarchal fuzzy logic controller in classifying the IoT data. The paper also tries to answer “if-else “questions about the effect of each of the input parameters. The authors' test case in this paper is related to the disease spreading prediction problem. This test case is highly important to the health care organizations. Different case studies are generated to examine the efficiency of the proposed framework and methodologies.

Information coding in the rodent prefrontal cortex. II. Ensemble activity in orbitofrontal cortex

1995 ◽  
Vol 74 (2) ◽  
pp. 751-762 ◽  
Author(s):  
G. Schoenbaum ◽  
H. Eichenbaum
Keyword(s):  
Orbitofrontal Cortex ◽  
Single Neuron ◽  
Activity Space ◽  
Behavioral Performance ◽  
Single Neurons ◽  
Reward Contingency ◽  
Neuron Firing ◽  
Firing Patterns ◽  
Neural Ensembles ◽  
Odor Discrimination Task

1. Neural activity was recorded from the orbitofrontal cortex (OF) of rats performing an eight-odor discrimination task that included predictable associations between particular odor pairs. A modified linear discriminant analysis was employed to characterize the population response in each trial of the task as a point in an N-dimensional activity space with the firing rate of each cell in the population represented on one of the N dimensions. The ability of the ensemble to discriminate among conditions of a variable was reflected in the tendency of population responses to cluster together in this activity space for repetitions of a given condition. We assessed coding of several variables describing the period of odor sampling, focusing on aspects of current, past, and future events reflected in single-neuron firing patterns, in ensembles composed of 22-138 cells active during the period when the rats sampled the discriminative stimulus in each trial. 2. OF ensembles performed well at discriminating variables with relevance to task demands represented in single-neuron firing patterns, specifically the physical attributes and assigned reward contingency of the current odor as well as the expectation of reward in the following trial that could be inferred from the predictable associations between particular pairs of odors. OF ensembles were able to correctly identify the identity and assigned reward contingency of the current odor in up to 52% (chance = 12.5%) and 99% (chance = 50%) of all trials, respectively, such that the observed behavioral performance required a population of 5,364 odor-responsive cells in the case of odor identity and only 40 cells in the case of valence. Expectations regarding upcoming rewards based on both assigned response contingency and associations between particular pairs of odors were correctly classified in up to 67% (chance = 20%) of all trials such that the observed level of behavioral performance required a population of 3,169 cells. 3. Other information represented in the single-neuron firing patterns, such as the identity and reward contingency of the preceding odor and specific odor-odor associations, was poorly encoded by OF ensembles. Thus neural ensembles in OF may represent only some of the information reflected in single-neuron activity. Stable coding of only the most useful and relevant information by the ensemble might emerge from the tuning properties of single neurons under the influence of the task at hand, producing in the well-trained animal the observed pattern of broad and diverse coding by single neurons and selective, task-relevant coding by neural ensembles in OF.

Single Neuron Models

2015 ◽  
pp. 129-182
Author(s):  
Peiji Liang ◽  
Si Wu ◽  
Fanji Gu
Keyword(s):  
Single Neuron ◽  
Neuron Models

scholarly journals PERIODIC ORBITS OF SINGLE NEURON MODELS WITH INTERNAL DECAY RATE 0 < Β ≤ 1

2013 ◽  
Vol 18 (3) ◽  
pp. 325-345 ◽  
Author(s):  
Aija Anisimova ◽  
Maruta Avotina ◽  
Inese Bula
Keyword(s):  
Dynamical System ◽  
Decay Rate ◽  
Single Neuron ◽  
Discrete Dynamical System ◽  
Constant Function ◽  
Piecewise Constant Function ◽  
Sigmoid Function ◽  
Signal Function ◽  
Neuron Models ◽  
Piecewise Constant

In this paper we consider a discrete dynamical system x n+1=βx n – g(x n ), n=0,1,..., arising as a discrete-time network of a single neuron, where 0 &lt; β ≤ 1 is an internal decay rate, g is a signal function. A great deal of work has been done when the signal function is a sigmoid function. However, a signal function of McCulloch-Pitts nonlinearity described with a piecewise constant function is also useful in the modelling of neural networks. We investigate a more complicated step signal function (function that is similar to the sigmoid function) and we will prove some results about the periodicity of solutions of the considered difference equation. These results show the complexity of neurons behaviour.

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