scholarly journals Necessary Conditions for Reliable Representation of Asynchronous Spikes Through a Single-layered Feedforward Network

2019 ◽  
Author(s):  
Milad Lankarany
Keyword(s):  
Firing Rate ◽  
Network Model ◽  
Mean Squared Error ◽  
Time Windows ◽  
Model Neuron ◽  
Network Size ◽  
Feedforward Network ◽  
Synaptic Noise ◽  
Reliable Representation ◽  
Short Time

AbstractReliable propagation of firing rate – specifically slow modulation of asynchronous spikes in fairly short time windows [20-500]ms across multiple layers of a feedforward network (FFN) receiving background synaptic noise has proven difficult to capture in spiking models. We, in this paper, explore how information of asynchronous spikes disrupted in the first layer of a typical FFN, and which factors can enable reliable information representation. Our rationale is that the reliable propagation of information across layers of a FFN is likely if that information can be preserved in the first layer of the FFN. In a typical FFN, each layer comprises a certain number (network size) of excitatory neurons – leaky integrate and fire (LIF) model neuron in this paper – receiving correlated input (common stimulus from the upstream layer) plus independent background synaptic noise. We develop a reduced network model of FFN which captures main features of a conventional all-to-all connected FFN. Exploiting the reduced network model, synaptic weights are calculated using a closed-form optimization framework that minimizes the mean squared error between reconstructed stimulus (by spikes of the first layer of FFN) and the original common stimulus. We further explore how representation of asynchronous spikes in a FFN changes with respect to other factors like the network size and the level of background synaptic noise while synaptic weights are optimized for each scenario. We show that not only synaptic weights but also the network size and the level of background synaptic noise are crucial to preserve a reliable representation of asynchronous spikes in the first layer of a FFN. This work sheds light in better understanding of how information of slowly time-varying fluctuations of the firing rate can be transmitted in multi-layered FFNs.

Misfire Diagnosis of Diesel Engine Based on Short-Time Vibration Characters

2010 ◽  
Vol 34-35 ◽  
pp. 301-305
Author(s):  
Zhao Qian Zhu ◽  
Jue Yang ◽  
Xiao Ming Zhang ◽  
Xiao Lei Li
Keyword(s):  
Neural Network ◽  
Diesel Engine ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Monitoring Test ◽  
Bp Neural Network Model ◽  
Short Time

This paper studied misfire diagnosis of diesel engine based on short-time vibration characters. Misfire of diesel engine was simulated by the vibration monitoring test. Cylinder vibration signal and top center signal were collected under different states. The short-time vibration signal of each cylinder was intercepted according to the diesel combustion sequence, effective value was calculated, and BP Neural Network model built with this character was used to diagnose diesel misfire. The result shows that this method can locate the misfire cylinder effectively, and it is meaningful for guiding the detection and repair of vehicles.

scholarly journals Peripheral Administration of RF9 Does Not Affect Hypothalamic-PituitaryGonadal Axis in Normal Fed Adult Male Macaque

2020 ◽  
pp. 1-5
Author(s):  
Aalia Batool ◽  
Madiha Wazir ◽  
Rahim Ullah ◽  
Aalia Batool ◽  
Rabia Naz ◽  
...  
Keyword(s):  
Adult Male ◽  
Normal Saline ◽  
Time Windows ◽  
Treated Group ◽  
Plasma Testosterone ◽  
Control Group ◽  
Hpg Axis ◽  
Testosterone Levels ◽  
Physiological Conditions ◽  
Short Time

Stress represses hypothalamic-pituitary-gonadal axis (HPG-axis) but RF9, a synthetic peptide, rescues such repression. To assess the role of RF9 in regulating HPG-axis under normal physiological conditions in higher primates, RF9 was administered to intact adult male rhesus monkeys and response of the HPG-axis was examined by measuring plasma testosterone as an end parameter of the axis. Control group (n=4) received normal saline whereas the treated group (n=4) received RF9. On the first day of experiment, four bolus injections of normal saline (1ml/animal) were administered intravenously at 2-hr interval to the control monkeys. Similarly, on the second day of experiment, treated group received four iv bolus injections of RF9 (0.1mg/kg BW) at 2-hr interval. Serial blood samples were collected at 20 min interval during a 6-hr period which started just after first saline/RF9 injection. Plasma testosterone levels were measured by using a specific EIA. Overall means of plasma testosterone levels and plasma testosterone area under curve (AUC) and overall mean testosterone and mean testosterone AUC in short time windows following each injection of RF9 and saline were comparable between the groups. Our results demonstrate that RF9 has no role in regulating HPG-axis under normal physiological conditions in adult male monkeys.

Multiple modes of activity in a model neuron suggest a novel mechanism for the effects of neuromodulators

1994 ◽  
Vol 72 (2) ◽  
pp. 872-882 ◽  
Author(s):  
C. C. Canavier ◽  
D. A. Baxter ◽  
J. W. Clark ◽  
J. H. Byrne
Keyword(s):  
Steady State ◽  
Model Neuron ◽  
State Level ◽  
High Amplitude ◽  
Steady State Level ◽  
Molluscan Neuron ◽  
Dynamic Activity ◽  
Mode Shift ◽  
Synaptic Inputs ◽  
Synaptic Noise

1. Previous examination of the phase space of a mathematical model of a bursting molluscan neuron has demonstrated the existence of multiple stable oscillatory modes. The present study examined the extent to which multistability could be regulated by known modulatory agents, the consequences of that regulation on the response of the neuron to synaptic inputs, the effects of noise, and the potential of multistability to enrich the repertoire of neuromodulatory effects. 2. Coexisting stable attractors may appear when a change is made in a voltage-dependent conductance in a manner that simulates the application of a neuromodulator. A small transient perturbation can shift the model neuron between stable modes, greatly amplifying the original perturbation. Thus the model becomes more sensitive to conventional synaptic inputs. These mode shifts are robust in the presence of low-amplitude synaptic noise. 3. In response to random high-amplitude synaptic noise, a model neuron rendered multistable by a simulated application of a neuromodulator produces apparently random activity, whereas in response to the same synaptic noise, a monostable model neuron produces barely perturbed regular activity. Thus an increase in the number of attractors enhances sensitivity to both conventional synaptic inputs and noise. Conversely, a decrease is associated with a reduction in sensitivity. 4. The response of a neuron to a subsequent transient perturbation in the level of neuromodulator depends on the steady-state level of the neuromodulator. For example, if the steady-state level is associated with a multistable neuron, a mode shift produced by such a transient change in the level of neuromodulator (manifested in our model as a conductance change) can persist after the conductance is returned gradually to its original value. Thus multistable dynamic activity permits the effects of a neuromodulator to persist when the neuromodulator is no longer present. 5. The mechanism of mode shifting between coexisting stable oscillatory modes introduces a number of novel possibilities with potentially profound implications for information processing and storage in a single neuron.

scholarly journals Synaptic mechanisms for motor variability in a feedforward network

2020 ◽  
Vol 6 (25) ◽  
pp. eaba4856
Author(s):  
Guo Zhang ◽  
Ke Yu ◽  
Tao Wang ◽  
Ting-Ting Chen ◽  
Wang-Ding Yuan ◽  
...  
Keyword(s):  
State Transition ◽  
Behavioral Variability ◽  
Noise Levels ◽  
Feedforward Network ◽  
Rhythmic Motor ◽  
System A ◽  
Synaptic Noise ◽  
Variable Activity ◽  
Transition Points ◽  
Synaptic Mechanisms

Behavioral variability often arises from variable activity in the behavior-generating neural network. The synaptic mechanisms underlying this variability are poorly understood. We show that synaptic noise, in conjunction with weak feedforward excitation, generates variable motor output in the Aplysia feeding system. A command-like neuron (CBI-10) triggers rhythmic motor programs more variable than programs triggered by CBI-2. CBI-10 weakly excites a pivotal pattern-generating interneuron (B34) strongly activated by CBI-2. The activation properties of B34 substantially account for the degree of program variability. CBI-10– and CBI-2–induced EPSPs in B34 vary in amplitude across trials, suggesting that there is synaptic noise. Computational studies show that synaptic noise is required for program variability. Further, at network state transition points when synaptic conductance is low, maximum program variability is promoted by moderate noise levels. Thus, synaptic strength and noise act together in a nonlinear manner to determine the degree of variability within a feedforward network.

scholarly journals Estimation of diameter at breast height from mobile laser scanning data collected under a heavy forest canopy

2017 ◽  
Vol 63 (No. 9) ◽  
pp. 433-441 ◽  
Author(s):  
Čerňava Juraj ◽  
Tuček Ján ◽  
Koreň Milan ◽  
Mokroš Martin
Keyword(s):  
Laser Scanning ◽  
Forest Canopy ◽  
Mean Squared Error ◽  
Satellite System ◽  
Tree Level ◽  
Efficient Technology ◽  
Mobile Mapping ◽  
Mobile Mapping System ◽  
Global Navigation Satellite ◽  
Short Time

Mobile laser scanning (MLS) is time-efficient technology of geospatial data collection that proved its ability to provide accurate measurements in many fields. Mobile innovation of the terrestrial laser scanning has a potential to collect forest inventory data on a tree level from large plots in a short time. Valuable data, collected using mobile mapping system (MMS), becomes very difficult to process when Global Navigation Satellite System (GNSS) outages become too long. A heavy forest canopy blocking the GNSS signal and limited accessibility can make mobile mapping very difficult. This paper presents processing of data collected by MMS under a heavy forest canopy. DBH was estimated from MLS point cloud using three different methods. Root mean squared error varied between 2.65 and 5.57 cm. Our research resulted in verification of the influence of MLS coverage of tree stem on the accuracy of DBH data.

Cellular and Network Mechanisms of Slow Oscillatory Activity (<1 Hz) and Wave Propagations in a Cortical Network Model

2003 ◽  
Vol 89 (5) ◽  
pp. 2707-2725 ◽  
Author(s):  
Albert Compte ◽  
Maria V. Sanchez-Vives ◽  
David A. McCormick ◽  
Xiao-Jing Wang
Keyword(s):  
Network Model ◽  
Slow Wave Sleep ◽  
Model Neuron ◽  
Input Resistance ◽  
Oscillatory Activity ◽  
Recurrent Excitation ◽  
Slow Adaptation ◽  
Pharmacological Manipulations

Slow oscillatory activity (<1 Hz) is observed in vivo in the cortex during slow-wave sleep or under anesthesia and in vitro when the bath solution is chosen to more closely mimic cerebrospinal fluid. Here we present a biophysical network model for the slow oscillations observed in vitro that reproduces the single neuron behaviors and collective network firing patterns in control as well as under pharmacological manipulations. The membrane potential of a neuron oscillates slowly (at <1 Hz) between a down state and an up state; the up state is maintained by strong recurrent excitation balanced by inhibition, and the transition to the down state is due to a slow adaptation current (Na+-dependent K+ current). Consistent with in vivo data, the input resistance of a model neuron, on average, is the largest at the end of the down state and the smallest during the initial phase of the up state. An activity wave is initiated by spontaneous spike discharges in a minority of neurons, and propagates across the network at a speed of 3–8 mm/s in control and 20–50 mm/s with inhibition block. Our work suggests that long-range excitatory patchy connections contribute significantly to this wave propagation. Finally, we show with this model that various known physiological effects of neuromodulation can switch the network to tonic firing, thus simulating a transition to the waking state.

Parameters of Spike Trains Observed in a Short Time Window

2008 ◽  
Vol 20 (5) ◽  
pp. 1325-1343 ◽  
Author(s):  
Zbyněk Pawlas ◽  
Lev B. Klebanov ◽  
Martin Prokop ◽  
Petr Lansky
Keyword(s):  
Coefficient Of Variation ◽  
Point Processes ◽  
Interspike Interval ◽  
Time Window ◽  
Time Windows ◽  
Time Interval ◽  
Short Time Interval ◽  
Target Neuron ◽  
Short Time Window ◽  
Short Time

We study the estimation of statistical moments of interspike intervals based on observation of spike counts in many independent short time windows. This scenario corresponds to the situation in which a target neuron occurs. It receives information from many neurons and has to respond within a short time interval. The precision of the estimation procedures is examined. As the model for neuronal activity, two examples of stationary point processes are considered: renewal process and doubly stochastic Poisson process. Both moment and maximum likelihood estimators are investigated. Not only the mean but also the coefficient of variation is estimated. In accordance with our expectations, numerical studies confirm that the estimation of mean interspike interval is more reliable than the estimation of coefficient of variation. The error of estimation increases with increasing mean interspike interval, which is equivalent to decreasing the size of window (less events are observed in a window) and with decreasing the number of neurons (lower number of windows).

scholarly journals Stimulus Competition by Inhibitory Interference

2005 ◽  
Vol 17 (11) ◽  
pp. 2421-2453 ◽  
Author(s):  
Paul H. E. Tiesinga
Keyword(s):  
Firing Rate ◽  
Arrival Time ◽  
Model Neuron ◽  
Field Potential ◽  
Relative Timing ◽  
Local Interneuron ◽  
General Applicability ◽  
The Poor ◽  
V4 Neurons ◽  
Preferred Stimulus

When two stimuli are present in the receptive field of a V4 neuron, the firing rate response is between the weakest and strongest response elicited by each of the stimuli when presented alone (Reynolds, Chelazzi, & Desimone, 1999). When attention is directed toward the stimulus eliciting the strongest response (the preferred stimulus), the response to the pair is increased, whereas the response decreases when attention is directed to the other stimulus (the poor stimulus). When attention is directed to either of the two stimuli presented alone, the firing rate remains the same or increases slightly, but the coherence between the neuron's spike train and the local field potential can increase (Fries, Reynolds, Rorie, & Desimone, 2001). These experimental results were reproduced in a model of a V4 neuron under the assumption that attention modulates the activity of local interneuron networks. The V4 model neuron received stimulus-specific excitation from V2 and synchronous inhibitory inputs from two local interneuron networks in V4. Each interneuron network was driven by stimulus-specific excitatory inputs from V2 and was modulated by the activity of the frontal eye fields. Stimulus competition was present because of a delay in arrival time of synchronous volleys from each interneuron network. For small delays, the firing rate was close to the rate elicited by the preferred stimulus alone, whereas for larger delays, it approached the firing rate of the poor stimulus. When either stimulus was presented alone, the neuron's response was not altered by the change in delay, but could change due to modulation of the degree of synchrony of the corresponding interneuron network. The model suggests that top-down attention biases the competition between V2 columns for control of V4 neurons primarily by changing the relative timing of inhibition, whereas changes in the degree of synchrony of interneuron networks modulate the response to a single stimulus. The new mechanism proposed here for attentional modulation of firing rate, gain modulation by inhibitory interference, is likely to have more general applicability to cortical information processing.

scholarly journals Architecture-Independent Approximation of Functions

2001 ◽  
Vol 13 (5) ◽  
pp. 1119-1135 ◽  
Author(s):  
Vicente Ruiz de Angulo ◽  
Carme Torras
Keyword(s):  
Weight Distribution ◽  
Network Size ◽  
Approximation Of Functions ◽  
Feedforward Network

We show that minimizing the expected error of a feedforward network over a distribution of weights results in an approximation that tends to be independent of network size as the number of hidden units grows. This minimization can be easily performed, and the complexity of the resulting function implemented by the network is regulated by the variance of the weight distribution. For a fixed variance, there is a number of hidden units above which either the implemented function does not change or the change is slight and tends to zero as the size of the network grows. In sum, the control of the complexity depends on only the variance, not the architecture, provided it is large enough.

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