scholarly journals Constraint-induced intervention as an emergent phenomenon from synaptic competition in biological systems

2021 ◽  
Author(s):  
Won J. Sohn ◽  
Terence D. Sanger
Keyword(s):  
High Speed ◽  
Neuronal Response ◽  
Early Brain Injury ◽  
Spike Timing ◽  
Synaptic Competition ◽  
Emergent Phenomenon ◽  
Corticospinal Tracts ◽  
Synaptic Modification ◽  
Hemiplegic Cerebral Palsy ◽  
Suboptimal Solution

AbstractThe principle of constraint-induced therapy is widely practiced in rehabilitation. In hemiplegic cerebral palsy (CP) with impaired contralateral corticospinal projection due to unilateral injury, function improves after imposing a temporary constraint on limbs from the less affected hemisphere. This type of partially-reversible impairment in motor control by early brain injury bears a resemblance to the experience-dependent plastic acquisition and modification of neuronal response selectivity in the visual cortex. Previously, such mechanism was modeled within the framework of BCM (Bienenstock-Cooper-Munro) theory, a rate-based synaptic modification theory. Here, we demonstrate a minimally complex yet sufficient neural network model which provides a fundamental explanation for inter-hemispheric competition using a simplified spike-based model of information transmission and plasticity. We emulate the restoration of function in hemiplegic CP by simulating the competition between cells of the ipsilateral and contralateral corticospinal tracts. We use a high-speed hardware neural simulation to provide realistic numbers of spikes and realistic magnitudes of synaptic modification. We demonstrate that the phenomenon of constraint-induced partial reversal of hemiplegia can be modeled by simplified neural descending tracts with 2 layers of spiking neurons and synapses with spike-timing-dependent plasticity (STDP). We further demonstrate that persistent hemiplegia following unilateral cortical inactivation or deprivation is predicted by the STDP-based model but is inconsistent with BCM model. Although our model is a highly simplified and limited representation of the corticospinal system, it offers an explanation of how constraint as an intervention can help the system to escape from a suboptimal solution. This is a display of an emergent phenomenon from the synaptic competition.

scholarly journals Event-Driven Simulation Scheme for Spiking Neural Networks Using Lookup Tables to Characterize Neuronal Dynamics

2006 ◽  
Vol 18 (12) ◽  
pp. 2959-2993 ◽  
Author(s):  
Eduardo Ros ◽  
Richard Carrillo ◽  
Eva M. Ortigosa ◽  
Boris Barbour ◽  
Rodrigo Agís
Keyword(s):  
Synaptic Plasticity ◽  
High Speed ◽  
Large Scale ◽  
Computational Cost ◽  
Brain Structures ◽  
Spike Timing ◽  
Neuronal Dynamics ◽  
Event Driven ◽  
Synaptic Dynamics

Nearly all neuronal information processing and interneuronal communication in the brain involves action potentials, or spikes, which drive the short-term synaptic dynamics of neurons, but also their long-term dynamics, via synaptic plasticity. In many brain structures, action potential activity is considered to be sparse. This sparseness of activity has been exploited to reduce the computational cost of large-scale network simulations, through the development of event-driven simulation schemes. However, existing event-driven simulations schemes use extremely simplified neuronal models. Here, we implement and evaluate critically an event-driven algorithm (ED-LUT) that uses precalculated look-up tables to characterize synaptic and neuronal dynamics. This approach enables the use of more complex (and realistic) neuronal models or data in representing the neurons, while retaining the advantage of high-speed simulation. We demonstrate the method's application for neurons containing exponential synaptic conductances, thereby implementing shunting inhibition, a phenomenon that is critical to cellular computation. We also introduce an improved two-stage event-queue algorithm, which allows the simulations to scale efficiently to highly connected networks with arbitrary propagation delays. Finally, the scheme readily accommodates implementation of synaptic plasticity mechanisms that depend on spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks.

Cortical Representation of Auditory Space: Information-Bearing Features of Spike Patterns

2002 ◽  
Vol 87 (4) ◽  
pp. 1749-1762 ◽  
Author(s):  
Shigeto Furukawa ◽  
John C. Middlebrooks
Keyword(s):  
Sound Source ◽  
Neuronal Response ◽  
Source Location ◽  
Spike Timing ◽  
Spike Count ◽  
Cortical Representation ◽  
Auditory Space ◽  
Transmitted Information ◽  
Related Information ◽  
Spike Patterns

Previous studies have demonstrated that the spike patterns of cortical neurons vary systematically as a function of sound-source location such that the response of a single neuron can signal the location of a sound source throughout 360° of azimuth. The present study examined specific features of spike patterns that might transmit information related to sound-source location. Analysis was based on responses of well-isolated single units recorded from cortical area A2 in α-chloralose-anesthetized cats. Stimuli were 80-ms noise bursts presented from loudspeakers in the horizontal plane; source azimuths ranged through 360° in 20° steps. Spike patterns were averaged across samples of eight trials. A competitive artificial neural network (ANN) identified sound-source locations by recognizing spike patterns; the ANN was trained using the learning vector quantization learning rule. The information about stimulus location that was transmitted by spike patterns was computed from joint stimulus-response probability matrices. Spike patterns were manipulated in various ways to isolate particular features. Full-spike patterns, which contained all spike-count information and spike timing with 100-μs precision, transmitted the most stimulus-related information. Transmitted information was sensitive to disruption of spike timing on a scale of more than ∼4 ms and was reduced by an average of ∼35% when spike-timing information was obliterated entirely. In a condition in which all but the first spike in each pattern were eliminated, transmitted information decreased by an average of only ∼11%. In many cases, that condition showed essentially no loss of transmitted information. Three unidimensional features were extracted from spike patterns. Of those features, spike latency transmitted ∼60% more information than that transmitted either by spike count or by a measure of latency dispersion. Information transmission by spike patterns recorded on single trials was substantially reduced compared with the information transmitted by averages of eight trials. In a comparison of averaged and nonaveraged responses, however, the information transmitted by latencies was reduced by only ∼29%, whereas information transmitted by spike counts was reduced by 79%. Spike counts clearly are sensitive to sound-source location and could transmit information about sound-source locations. Nevertheless, the present results demonstrate that the timing of the first poststimulus spike carries a substantial amount, probably the majority, of the location-related information present in spike patterns. The results indicate that any complete model of the cortical representation of auditory space must incorporate the temporal characteristics of neuronal response patterns.

Application of Perfluorooctylbromide Nanoparticles with Ulinastatin for Early Brain Injury Caused by Carbon Monoxide Poisoning

2021 ◽  
Vol 13 (7) ◽  
pp. 1383-1390
Author(s):  
Guangcong Li ◽  
Dan Li
Keyword(s):  
Carbon Monoxide ◽  
Particle Size ◽  
Treatment Group ◽  
Cell Apoptosis ◽  
High Speed ◽  
Carbon Monoxide Poisoning ◽  
Neuronal Cell ◽  
Early Brain Injury ◽  
Group A ◽  
The Brain

ABSTRACTThis study aimed to explore the mechanism of perfluorooctylbromide (PFOB) nanoparticles (NPs) combined with ulinastatin (UTI) on early brain injury (EBI) caused by carbon monoxide poisoning (CMP). Firstly, PFOB NPs were prepared by high-speed dispersion and high-speed homogenization. The physicochemical characteristics of the particle size distribution and Zeta potential distribution of the NPs were analyzed using a laser particle size analyzer. The thermal and photoinduced phase transition characteristics of the NPs were analyzed under heating and laser irradiation conditions. Then, 50 Sprague Dawley (SD) rats were deemed as the research objects to establish the CMP rat models using hyperbaric oxygen chambers. According to different treatment methods, they were rolled into a healthy control group, a carbon monoxide (CO) model group, a PTOB treatment group, an UTI treatment group, and a PTOB + UTI treatment group. The brain tissues of each group of rats were collected 3 days after treatment. The neuronal cell apoptosis, expression of Caspase-3, messenger ribonucleic acid (mRNA) of inflammatory factors interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α) in rat brain tissue were detected through immunohistochemical staining, in situ cell apoptosis detection, Reverse transcription-polymerase chain reaction (RT-PCR), and Western blotting, so did the relative expression of target proteins B-cell lymphoma-2 (Bcl-2), Bcl2-Associated X (Bax) and myelin basic protein (MBP). As a result, the average particle size and the average Zeta potential of the prepared PFOB NPs was 103±31 nm and −23 ± 15 mV, respectively. When the PFOB NPs were heated to 80 °C, the particle size increased greatly and cracks appeared. The particle size of PFOB NPs also increased obviously after laser irradiation, and the PFOB inside the particles changed into gas phase. Compared to CO group, expression of Caspase-3, neuronal cell apoptosis rate, mRNA expression of IL-1β and TNF-α, and protein expression of Bax and Bcl-2 in the brain tissue of PTOB group, UTI group, and PFOB + UTI group were notably decreased (P < 0.05), while the MBP protein expression increased considerably (P < 0.05). Changes in PFOB + UTI group were more obvious than those in PTOB group and UTI group, and those indicators weren’t considerably different from the controls. In summary, PFOB NPs were successfully prepared with favorable phase transition characteristics. Moreover, PFOB NPs combined with UTI could reduce the apoptosis of brain neurons after CMP, improve the inflammatory response, and play a protective effect on EBI of CMP.

scholarly journals Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 500 ◽  
Author(s):  
Sergey A. Lobov ◽  
Andrey V. Chernyshov ◽  
Nadia P. Krilova ◽  
Maxim O. Shamshin ◽  
Victor B. Kazantsev
Keyword(s):  
Supervised Learning ◽  
Hebbian Learning ◽  
Back Propagation ◽  
Spike Timing ◽  
Temporal Coding ◽  
Competitive Learning ◽  
Neuron Activity ◽  
Synaptic Competition ◽  
Rate Coding ◽  
Emg Patterns

One of the modern trends in the design of human–machine interfaces (HMI) is to involve the so called spiking neuron networks (SNNs) in signal processing. The SNNs can be trained by simple and efficient biologically inspired algorithms. In particular, we have shown that sensory neurons in the input layer of SNNs can simultaneously encode the input signal based both on the spiking frequency rate and on varying the latency in generating spikes. In the case of such mixed temporal-rate coding, the SNN should implement learning working properly for both types of coding. Based on this, we investigate how a single neuron can be trained with pure rate and temporal patterns, and then build a universal SNN that is trained using mixed coding. In particular, we study Hebbian and competitive learning in SNN in the context of temporal and rate coding problems. We show that the use of Hebbian learning through pair-based and triplet-based spike timing-dependent plasticity (STDP) rule is accomplishable for temporal coding, but not for rate coding. Synaptic competition inducing depression of poorly used synapses is required to ensure a neural selectivity in the rate coding. This kind of competition can be implemented by the so-called forgetting function that is dependent on neuron activity. We show that coherent use of the triplet-based STDP and synaptic competition with the forgetting function is sufficient for the rate coding. Next, we propose a SNN capable of classifying electromyographical (EMG) patterns using an unsupervised learning procedure. The neuron competition achieved via lateral inhibition ensures the “winner takes all” principle among classifier neurons. The SNN also provides gradual output response dependent on muscular contraction strength. Furthermore, we modify the SNN to implement a supervised learning method based on stimulation of the target classifier neuron synchronously with the network input. In a problem of discrimination of three EMG patterns, the SNN with supervised learning shows median accuracy 99.5% that is close to the result demonstrated by multi-layer perceptron learned by back propagation of an error algorithm.

scholarly journals Sequence memory in recurrent neuronal network can develop without structured input

2020 ◽  
Author(s):  
Matthias Loidolt ◽  
Lucas Rudelt ◽  
Viola Priesemann
Keyword(s):  
Sensory Input ◽  
Building Blocks ◽  
Spike Timing ◽  
Synaptic Competition ◽  
Structured Input ◽  
Recurrent Neuronal Network ◽  
Sequence Memory ◽  
The Brain ◽  
Specific Sequences ◽  
Intrinsic Feature

AbstractHow does spontaneous activity during development prepare cortico-cortical connections for sensory input? We here analyse the development of sequence memory, an intrinsic feature of recurrent networks that supports temporal perception. We use a recurrent neural network model with homeostatic and spike-timing-dependent plasticity (STDP). This model has been shown to learn specific sequences from structured input. We show that development even under unstructured input increases unspecific sequence memory. Moreover, networks “pre-shaped” by such unstructured input subsequently learn specific sequences faster. The key structural substrate is the emergence of strong and directed synapses due to STDP and synaptic competition. These construct self-amplifying preferential paths of activity, which can quickly encode new input sequences. Our results suggest that memory traces are not printed on a tabula rasa, but instead harness building blocks already present in the brain.

Spike-timing-dependent synaptic modification induced by natural spike trains

Nature ◽  
2002 ◽  
Vol 416 (6879) ◽  
pp. 433-438 ◽  
Author(s):  
Robert C. Froemke ◽  
Yang Dan
Keyword(s):  
Spike Trains ◽  
Spike Timing ◽  
Synaptic Modification

scholarly journals Reconciling the STDP and BCM Models of Synaptic Plasticity in a Spiking Recurrent Neural Network

2010 ◽  
Vol 22 (8) ◽  
pp. 2059-2085 ◽  
Author(s):  
Daniel Bush ◽  
Andrew Philippides ◽  
Phil Husbands ◽  
Michael O'Shea
Keyword(s):  
Neural Network ◽  
Synaptic Plasticity ◽  
Recurrent Neural Network ◽  
Hebbian Learning ◽  
Spike Timing ◽  
Synaptic Competition ◽  
Firing Rates ◽  
Plasticity Rule ◽  
Asymmetric Learning

Rate-coded Hebbian learning, as characterized by the BCM formulation, is an established computational model of synaptic plasticity. Recently it has been demonstrated that changes in the strength of synapses in vivo can also depend explicitly on the relative timing of pre- and postsynaptic firing. Computational modeling of this spike-timing-dependent plasticity (STDP) has demonstrated that it can provide inherent stability or competition based on local synaptic variables. However, it has also been demonstrated that these properties rely on synaptic weights being either depressed or unchanged by an increase in mean stochastic firing rates, which directly contradicts empirical data. Several analytical studies have addressed this apparent dichotomy and identified conditions under which distinct and disparate STDP rules can be reconciled with rate-coded Hebbian learning. The aim of this research is to verify, unify, and expand on these previous findings by manipulating each element of a standard computational STDP model in turn. This allows us to identify the conditions under which this plasticity rule can replicate experimental data obtained using both rate and temporal stimulation protocols in a spiking recurrent neural network. Our results describe how the relative scale of mean synaptic weights and their dependence on stochastic pre- or postsynaptic firing rates can be manipulated by adjusting the exact profile of the asymmetric learning window and temporal restrictions on spike pair interactions respectively. These findings imply that previously disparate models of rate-coded autoassociative learning and temporally coded heteroassociative learning, mediated by symmetric and asymmetric connections respectively, can be implemented in a single network using a single plasticity rule. However, we also demonstrate that forms of STDP that can be reconciled with rate-coded Hebbian learning do not generate inherent synaptic competition, and thus some additional mechanism is required to guarantee long-term input-output selectivity.

Intermittent stimulus presentation stabilizes neuronal responses in macaque area MT

2012 ◽  
Vol 108 (8) ◽  
pp. 2101-2114 ◽  
Author(s):  
P. Christiaan Klink ◽  
Anna Oleksiak ◽  
Martin J. M. Lankheet ◽  
Richard J. A. van Wezel
Keyword(s):  
Neuronal Response ◽  
Stimulus Presentation ◽  
Sensory Stimulation ◽  
Response Variability ◽  
Spike Timing ◽  
Response Magnitude ◽  
General Effect ◽  
Neuronal Responses ◽  
Area Mt ◽  
Response Characteristics

Repeated stimulation impacts neuronal responses. Here we show how response characteristics of sensory neurons in macaque visual cortex are influenced by the duration of the interruptions during intermittent stimulus presentation. Besides effects on response magnitude consistent with neuronal adaptation, the response variability was also systematically influenced. Spike rate variability in motion-sensitive area MT decreased when interruption durations were systematically increased from 250 to 2,000 ms. Activity fluctuations between subsequent trials and Fano factors over full response sequences were both lower with longer interruptions, while spike timing patterns became more regular. These variability changes partially depended on the response magnitude, but another significant effect that was uncorrelated with adaptation-induced changes in response magnitude was also present. Reduced response variability was furthermore accompanied by changes in spike-field coherence, pointing to the possibility that reduced spiking variability results from interactions in the local cortical network. While neuronal response stabilization may be a general effect of repeated sensory stimulation, we discuss its potential link with the phenomenon of perceptual stabilization of ambiguous stimuli as a result of interrupted presentation.

A Calcium-Based Simple Model of Multiple Spike Interactions in Spike-Timing-Dependent Plasticity

2013 ◽  
Vol 25 (7) ◽  
pp. 1853-1869 ◽  
Author(s):  
Takumi Uramoto ◽  
Hiroyuki Torikai
Keyword(s):  
Synaptic Weight ◽  
Spike Timing ◽  
State Variables ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Synaptic Modification ◽  
Proposed Model ◽  
Layer 2 ◽  
Visual Cortical ◽  
Cortical Slices

Spike-timing-dependent plasticity (STDP) is a form of synaptic modification that depends on the relative timings of presynaptic and postsynaptic spikes. In this letter, we proposed a calcium-based simple STDP model, described by an ordinary differential equation having only three state variables: one represents the density of intracellular calcium, one represents a fraction of open state NMDARs, and one represents the synaptic weight. We shown that in spite of its simplicity, the model can reproduce the properties of the plasticity that have been experimentally measured in various brain areas (e.g., layer 2/3 and 5 visual cortical slices, hippocampal cultures, and layer 2/3 somatosensory cortical slices) with respect to various patterns of presynaptic and postsynaptic spikes. In addition, comparisons with other STDP models are made, and the significance and advantages of the proposed model are discussed.

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