scholarly journals The Effect of Correlated Variability on the Accuracy of a Population Code

1999 ◽  
Vol 11 (1) ◽  
pp. 91-101 ◽  
Author(s):  
L. F. Abbott ◽  
Peter Dayan
Keyword(s):  
Firing Rate ◽  
Neuronal Firing ◽  
Population Code ◽  
Firing Rates ◽  
Large Populations ◽  
Neuronal Firing Rate ◽  
General Limit ◽  
Widespread Belief ◽  
The Impact

We study the impact of correlated neuronal firing rate variability on the accuracy with which an encoded quantity can be extracted from a population of neurons. Contrary to widespread belief, correlations in the variabilities of neuronal firing rates do not, in general, limit the increase in coding accuracy provided by using large populations of encoding neurons. Furthermore, in some cases, but not all, correlations improve the accuracy of a population code.

scholarly journals A deep convolutional visual encoding model of neuronal responses in the LGN

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Eslam Mounier ◽  
Bassem Abdullah ◽  
Hani Mahdi ◽  
Seif Eldawlatly
Keyword(s):  
Firing Rate ◽  
Visual Information ◽  
Visual Stimulation ◽  
Neuronal Population ◽  
Neuronal Firing ◽  
Electrode Arrays ◽  
Deep Convolutional Neural Networks ◽  
Firing Rates ◽  
Spatiotemporal Representation

AbstractThe Lateral Geniculate Nucleus (LGN) represents one of the major processing sites along the visual pathway. Despite its crucial role in processing visual information and its utility as one target for recently developed visual prostheses, it is much less studied compared to the retina and the visual cortex. In this paper, we introduce a deep learning encoder to predict LGN neuronal firing in response to different visual stimulation patterns. The encoder comprises a deep Convolutional Neural Network (CNN) that incorporates visual stimulus spatiotemporal representation in addition to LGN neuronal firing history to predict the response of LGN neurons. Extracellular activity was recorded in vivo using multi-electrode arrays from single units in the LGN in 12 anesthetized rats with a total neuronal population of 150 units. Neural activity was recorded in response to single-pixel, checkerboard and geometrical shapes visual stimulation patterns. Extracted firing rates and the corresponding stimulation patterns were used to train the model. The performance of the model was assessed using different testing data sets and different firing rate windows. An overall mean correlation coefficient between the actual and the predicted firing rates of 0.57 and 0.7 was achieved for the 10 ms and the 50 ms firing rate windows, respectively. Results demonstrate that the model is robust to variability in the spatiotemporal properties of the recorded neurons outperforming other examined models including the state-of-the-art Generalized Linear Model (GLM). The results indicate the potential of deep convolutional neural networks as viable models of LGN firing.

scholarly journals Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS)

2017 ◽  
Author(s):  
Nur Ahmadi ◽  
Timothy G. Constandinou ◽  
Christos-Savvas Bouganis
Keyword(s):  
Firing Rate ◽  
Spike Train ◽  
Prior Distribution ◽  
Kernel Smoothing ◽  
Neuronal Firing ◽  
Gaussian Kernel ◽  
Single Trial ◽  
Adaptive Kernel ◽  
Neuronal Firing Rate ◽  
Spike Train Data

AbstractNeurons use sequences of action potentials (spikes) to convey information across neuronal networks. In neurophysiology experiments, information about external stimuli or behavioral tasks has been frequently characterized in term of neuronal firing rate. The firing rate is conventionally estimated by averaging spiking responses across multiple similar experiments (or trials). However, there exist a number of applications in neuroscience research that require firing rate to be estimated on a single trial basis. Estimating firing rate from a single trial is a challenging problem and current state-of-the-art methods do not perform well. To address this issue, we develop a new method for estimating firing rate based on kernel smoothing technique that considers the bandwidth as a random variable with prior distribution that is adaptively updated under a Bayesian framework. By carefully selecting the prior distribution together with Gaussian kernel function, an analytical expression can be achieved for the kernel bandwidth. We refer to the proposed method as Bayesian Adaptive Kernel Smoother (BAKS). We evaluate the performance of BAKS using synthetic spike train data generated by biologically plausible models: inhomogeneous Gamma (IG) and inhomogeneous inverse Gaussian (IIG). We also apply BAKS to real spike train data from non-human primate (NHP) motor and visual cortex. We benchmark the proposed method against the established and previously reported methods. These include: optimized kernel smoother (OKS), variable kernel smoother (VKS), local polynomial fit (Locfit), and Bayesian adaptive regression splines (BARS). Results using both synthetic and real data demonstrate that the proposed method achieves better performance compared to competing methods. This suggests that the proposed method could be useful for understanding the encoding mechanism of neurons in cognitive-related tasks. The proposed method could also potentially improve the performance of brain-machine interface (BMI) decoder that relies on estimated firing rate as the input.

scholarly journals Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS)

PLoS ONE ◽  
2018 ◽  
Vol 13 (11) ◽  
pp. e0206794 ◽  
Author(s):  
Nur Ahmadi ◽  
Timothy G. Constandinou ◽  
Christos-Savvas Bouganis
Keyword(s):  
Firing Rate ◽  
Neuronal Firing ◽  
Adaptive Kernel ◽  
Neuronal Firing Rate

Chronotropic Effect of Angiotensin II via Type 2 Receptors in Rat Brain Neurons

2001 ◽  
Vol 85 (5) ◽  
pp. 2177-2183 ◽  
Author(s):  
Mingyan Zhu ◽  
Colin Sumners ◽  
Craig H. Gelband ◽  
Philip Posner
Keyword(s):  
Angiotensin Ii ◽  
Firing Rate ◽  
Brain Stem ◽  
Neuronal Firing ◽  
Delayed Rectifier ◽  
Neuronal Cultures ◽  
Ang Ii ◽  
Chronotropic Effect ◽  
Neuronal Firing Rate

Previously, we determined that angiotensin II (Ang II) elicits an Ang II type 2 (AT2) receptor–mediated increase of neuronal delayed rectifier K+( I KV) current in neuronal cultures from newborn rat hypothalamus and brain stem. This requires generation of lipoxygenase (LO) metabolites of arachidonic acid (AA) and activation of serine/threonine phosphatase type 2A (PP-2A). Enhancement of I KV results in a decrease in net inward current during the action potential (AP) upstroke as well as shortening of the refractory period, which may lead to alterations in neuronal firing rate. Thus, in the present study, we used whole-cell current clamp recording methods to investigate the AT2 receptor–mediated effects of Ang II on the firing rate of cultured neurons from the hypothalamus and brain stem. At room temperature, these neurons exhibited spontaneous APs with an amplitude of 77.72 ± 2.7 mV ( n = 20) and they fired at a frequency of 0.8 ± 0.1 Hz ( n = 11). Most cells had a prolonged early after-depolarization that followed an initial fully developed AP. Superfusion of Ang II (100 nM) plus losartan (LOS, 1 μM) to block Ang II type 1 receptors elicited a significant chronotropic effect that was reversed by the AT2 receptor inhibitor PD 123,319 (1 μM). LOS alone had no effect on any of the parameters measured. The chronotropic effect of Ang II was reversed by the general LO inhibitor 5,8,11,14-eicosatetraynoic acid (10 μM) or by the selective PP-2A inhibitor okadaic acid (1 nM) and was mimicked by the 12-LO metabolite of AA 12-(S)-hydroxy-(5Z, 8Z, 10E, 14Z)-eicosatetraynoic acid. These data indicate that Ang II elicits an AT2 receptor–mediated increase in neuronal firing rate, an effect that involves generation of LO metabolites of AA and activation of PP-2A.

Internal Pallidal Neuronal Activity During Mild Drug-Related Dyskinesias in Parkinson's Disease: Decreased Firing Rates and Altered Firing Patterns

2007 ◽  
Vol 97 (4) ◽  
pp. 2627-2641 ◽  
Author(s):  
J. I. Lee ◽  
L. Verhagen Metman ◽  
S. Ohara ◽  
P. M. Dougherty ◽  
J. H. Kim ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Firing Rate ◽  
Spike Trains ◽  
Neuronal Firing ◽  
Low Doses ◽  
Significant Group ◽  
Firing Patterns ◽  
Firing Rates ◽  
Before And After

The neuronal basis of hyperkinetic movement disorders has long been unclear. We now test the hypothesis that changes in the firing pattern of neurons in the globus pallidus internus (GPi) are related to dyskinesias induced by low doses of apomorphine in patients with advanced Parkinson's disease (PD). During pallidotomy for advanced PD, the activity of single neurons was studied both before and after administration of apomorphine at doses just adequate to induce dyskinesias (21 neurons, 17 patients). After the apomorphine injection, these spike trains demonstrated an initial fall in firing from baseline. In nine neurons, the onset of on was simultaneous with that of dyskinesias. In these spike trains, the initial fall in firing rate preceded and was larger than the fall at the onset of on with dyskinesias. Among the three neurons in which the onset of on occurred before that of dyskinesias, the firing rate did not change at the time of onset of dyskinesias. After injection of apomorphine, dyskinesias during on with dyskinesias often fluctuated between transient periods with dyskinesias and those without. Average firing rates were not different between these two types of transient periods. Transient periods with dyskinesias were characterized by interspike interval (ISI) independence, stationary spike trains, and higher variability of ISIs. A small but significant group of neurons demonstrated recurring ISI patterns during transient periods of on with dyskinesias. These results suggest that mild dyskinesias resulting from low doses of apomorphine are related to both low GPi neuronal firing rates and altered firing patterns.

Sign in / Sign up

Export Citation Format

Share Document