Band-Pass Response Properties of Rat SI Neurons

2003 ◽  
Vol 90 (3) ◽  
pp. 1379-1391 ◽  
Author(s):  
Catherine E. Garabedian ◽  
Stephanie R. Jones ◽  
Michael M. Merzenich ◽  
Anders Dale ◽  
Christopher I. Moore

Rats typically employ 4- to 12-Hz “whisking” movements of their vibrissae during tactile exploration. The intentional sampling of signals in this frequency range suggests that neural processing of tactile information may be differentially engaged in this bandwidth. We examined action potential responses in rat primary somatosensory cortex (SI) to a range of frequencies of vibrissa motion. Single vibrissae were mechanically deflected with 5-s pulse trains at rates ≤40 Hz. As previously reported, vibrissa deflection evoked robust neural responses that consistently adapted to stimulus rates ≥3 Hz. In contrast with this low-pass feature of the response, several other characteristics of the response revealed bandpass response properties. While average evoked response amplitudes measured 0–35 ms after stimulus onset typically decreased with increasing frequency, the later components of the response (>15 ms post stimulus) were augmented at frequencies between 3 and 10 Hz. Further, during the steady state, both rate and temporal measures of neural activity, measured as total spike rate or vector strength (a measure of temporal fidelity of spike timing across cycles), showed peak signal values at 5–10 Hz. A minimal biophysical network model of SI layer IV, consisting of an excitatory and inhibitory neuron and thalamocortical input, captured the spike rate and vector strength band-pass characteristics. Further analyses in which specific elements were selectively removed from the model suggest that slow inhibitory influences give rise to the band-pass peak in temporal precision, while thalamocortical adaptation can account for the band-pass peak in spike rate. The presence of these band-pass characteristics may be a general property of thalamocortical circuits that lead rodents to target this frequency range with their whisking behavior.

2015 ◽  
Vol 114 (4) ◽  
pp. 2204-2219 ◽  
Author(s):  
Clifford H. Keller ◽  
Terry T. Takahashi

Spike rate adaptation (SRA) is a continuing change of responsiveness to ongoing stimuli, which is ubiquitous across species and levels of sensory systems. Under SRA, auditory responses to constant stimuli change over time, relaxing toward a long-term rate often over multiple timescales. With more variable stimuli, SRA causes the dependence of spike rate on sound pressure level to shift toward the mean level of recent stimulus history. A model based on subtractive adaptation (Benda J, Hennig RM. J Comput Neurosci 24: 113–136, 2008) shows that changes in spike rate and level dependence are mechanistically linked. Space-specific neurons in the barn owl's midbrain, when recorded under ketamine-diazepam anesthesia, showed these classical characteristics of SRA, while at the same time exhibiting changes in spike timing precision. Abrupt level increases of sinusoidally amplitude-modulated (SAM) noise initially led to spiking at higher rates with lower temporal precision. Spike rate and precision relaxed toward their long-term values with a time course similar to SRA, results that were also replicated by the subtractive model. Stimuli whose amplitude modulations (AMs) were not synchronous across carrier frequency evoked spikes in response to stimulus envelopes of a particular shape, characterized by the spectrotemporal receptive field (STRF). Again, abrupt stimulus level changes initially disrupted the temporal precision of spiking, which then relaxed along with SRA. We suggest that shifts in latency associated with stimulus level changes may differ between carrier frequency bands and underlie decreased spike precision. Thus SRA is manifest not simply as a change in spike rate but also as a change in the temporal precision of spiking.


2021 ◽  
Vol 12 (3) ◽  
pp. 1-20
Author(s):  
Damodar Reddy Edla ◽  
Shubham Dodia ◽  
Annushree Bablani ◽  
Venkatanareshbabu Kuppili

Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field of entertainment to medical. In this article, a novel Deceit Identification Test is proposed based on the Electroencephalogram signals to identify and analyze the human behavior. Deceit identification test is based on P300 signals, which have a positive peak from 300 ms to 1,000 ms of the stimulus onset. The aim of the experiment is to identify and classify P300 signals with good classification accuracy. For preprocessing, a band-pass filter is used to eliminate the artifacts. The feature extraction is carried out using “symlet” Wavelet Packet Transform (WPT). Deep Neural Network (DNN) with two autoencoders having 10 hidden layers each is applied as the classifier. A novel experiment is conducted for the collection of EEG data from the subjects. EEG signals of 30 subjects (15 guilty and 15 innocent) are recorded and analyzed during the experiment. BrainVision recorder and analyzer are used for recording and analyzing EEG signals. The model is trained for 90% of the dataset and tested for 10% of the dataset and accuracy of 95% is obtained.


2008 ◽  
Vol 20 (4) ◽  
pp. 974-993 ◽  
Author(s):  
Arunava Banerjee ◽  
Peggy Seriès ◽  
Alexandre Pouget

Several recent models have proposed the use of precise timing of spikes for cortical computation. Such models rely on growing experimental evidence that neurons in the thalamus as well as many primary sensory cortical areas respond to stimuli with remarkable temporal precision. Models of computation based on spike timing, where the output of the network is a function not only of the input but also of an independently initializable internal state of the network, must, however, satisfy a critical constraint: the dynamics of the network should not be sensitive to initial conditions. We have previously developed an abstract dynamical system for networks of spiking neurons that has allowed us to identify the criterion for the stationary dynamics of a network to be sensitive to initial conditions. Guided by this criterion, we analyzed the dynamics of several recurrent cortical architectures, including one from the orientation selectivity literature. Based on the results, we conclude that under conditions of sustained, Poisson-like, weakly correlated, low to moderate levels of internal activity as found in the cortex, it is unlikely that recurrent cortical networks can robustly generate precise spike trajectories, that is, spatiotemporal patterns of spikes precise to the millisecond timescale.


1999 ◽  
Vol 26-27 ◽  
pp. 293-298 ◽  
Author(s):  
Akaysha C Tang ◽  
Jonathan Wolfe ◽  
Andreas M Bartels

2015 ◽  
Vol 8 (2) ◽  
pp. 179-184 ◽  
Author(s):  
Valeria Nocella ◽  
Luca Pelliccia ◽  
Paola Farinelli ◽  
Roberto Sorrentino ◽  
Mario Costa ◽  
...  

A robust and tuneless micromachined waveguide diplexer operating in the frequency range 71–86 GHz is here presented. The diplexer is based on multiple coupled cavities and it is manufactured using micromachining technology on two staked silicon layers. The diplexer consists of two filters combined to a common waveguide port via an E-plane T-junction. The two eight-order band-pass filters are centered at 73.5 and 83.5 GHz. The fractional bandwidths for two bands are 8.8 and 7.8% at higher- and lower-band, respectively. The measured insertion loss is below 0.7 dB for both the filters and the diplexer isolation is better than 55 dB, as required. The proposed technology allows for a very compact device (<20 × 20 × 1.5 mm) and the first prototypes were proved to be very robust to manufacturing tolerances and environmental tests, thus leading to an excellent tuneless manufacturing yield in future production. The diplexer will be employed in next generation terrestrial radio-link communications front-ends.


1998 ◽  
Vol 10 (7) ◽  
pp. 1679-1703 ◽  
Author(s):  
Elad Schneidman ◽  
Barry Freedman ◽  
Idan Segev

The firing reliability and precision of an isopotential membrane patch consisting of a realistically large number of ion channels is investigated using a stochastic Hodgkin-Huxley (HH) model. In sharp contrast to the deterministic HH model, the biophysically inspired stochastic model reproduces qualitatively the different reliability and precision characteristics of spike firing in response to DC and fluctuating current input in neocortical neurons, as reported by Mainen & Sejnowski (1995). For DC inputs, spike timing is highly unreliable; the reliability and precision are significantly increased for fluctuating current input. This behavior is critically determined by the relatively small number of excitable channels that are opened near threshold for spike firing rather than by the total number of channels that exist in the membrane patch. Channel fluctuations, together with the inherent bistability in the HH equations, give rise to three additional experimentally observed phenomena: subthreshold oscillations in the membrane voltage for DC input, “spontaneous” spikes for subthreshold inputs, and “missing” spikes for suprathreshold inputs. We suggest that the noise inherent in the operation of ion channels enables neurons to act as “smart” encoders. Slowly varying, uncorrelated inputs are coded with low reliability and accuracy and, hence, the information about such inputs is encoded almost exclusively by the spike rate. On the other hand, correlated presynaptic activity produces sharp fluctuations in the input to the postsynaptic cell, which are then encoded with high reliability and accuracy. In this case, information about the input exists in the exact timing of the spikes. We conclude that channel stochasticity should be considered in realistic models of neurons.


2011 ◽  
Vol 105 (4) ◽  
pp. 1889-1896 ◽  
Author(s):  
Andrew M. Rosen ◽  
Jonathan D. Victor ◽  
Patricia M. Di Lorenzo

Recent studies have provided evidence that temporal coding contributes significantly to encoding taste stimuli at the first central relay for taste, the nucleus of the solitary tract (NTS). However, it is not known whether this coding mechanism is also used at the next synapse in the central taste pathway, the parabrachial nucleus of the pons (PbN). In the present study, electrophysiological responses to taste stimuli (sucrose, NaCl, HCl, and quinine) were recorded from 44 cells in the PbN of anesthetized rats. In 29 cells, the contribution of the temporal characteristics of the response to the discrimination of various taste qualities was assessed. A family of metrics that quantifies the similarity of two spike trains in terms of spike count and spike timing was used. Results showed that spike timing in 14 PbN cells (48%) conveyed a significant amount of information about taste quality, beyond what could be conveyed by spike count alone. In another 14 cells (48%), the rate envelope (time course) of the response contributed significantly more information than spike count alone. Across cells there was a significant correlation ( r = 0.51; P < 0.01) between breadth of tuning and the proportion of information conveyed by temporal dynamics. Comparison with previous data from the NTS (Di Lorenzo PM and Victor JD. J Neurophysiol 90: 1418–31, 2003 and J Neurophysiol 97: 1857–1861, 2007) showed that temporal coding in the NTS occurred in a similar proportion of cells and contributed a similar fraction of the total information at the same average level of temporal precision, even though trial-to-trial variability was higher in the PbN than in the NTS. These data suggest that information about taste quality conveyed by the temporal characteristics of evoked responses is transmitted with high fidelity from the NTS to the PbN.


2017 ◽  
Vol 29 (12) ◽  
pp. 3260-3289 ◽  
Author(s):  
Alison I. Weber ◽  
Jonathan W. Pillow

A key problem in computational neuroscience is to find simple, tractable models that are nevertheless flexible enough to capture the response properties of real neurons. Here we examine the capabilities of recurrent point process models known as Poisson generalized linear models (GLMs). These models are defined by a set of linear filters and a point nonlinearity and are conditionally Poisson spiking. They have desirable statistical properties for fitting and have been widely used to analyze spike trains from electrophysiological recordings. However, the dynamical repertoire of GLMs has not been systematically compared to that of real neurons. Here we show that GLMs can reproduce a comprehensive suite of canonical neural response behaviors, including tonic and phasic spiking, bursting, spike rate adaptation, type I and type II excitation, and two forms of bistability. GLMs can also capture stimulus-dependent changes in spike timing precision and reliability that mimic those observed in real neurons, and can exhibit varying degrees of stochasticity, from virtually deterministic responses to greater-than-Poisson variability. These results show that Poisson GLMs can exhibit a wide range of dynamic spiking behaviors found in real neurons, making them well suited for qualitative dynamical as well as quantitative statistical studies of single-neuron and population response properties.


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