scholarly journals Firing rate homeostasis occurs in the absence of neuronal activity-regulated transcription

2019 ◽  
Author(s):  
Kelsey M. Tyssowski ◽  
Katherine C. Letai ◽  
Samuel D. Rendall ◽  
Anastasia Nizhnik ◽  
Jesse M. Gray
Keyword(s):  
Firing Rate ◽  
Neuronal Activity ◽  
Cortical Neurons ◽  
Molecular Mechanisms ◽  
Neuronal Circuits ◽  
Electrode Arrays ◽  
Knock Out ◽  
Firing Rates ◽  
The Face ◽  
Homeostatic Mechanisms

ABSTRACTDespite dynamic inputs, neuronal circuits maintain relatively stable firing rates over long periods. This maintenance of firing rate, or firing rate homeostasis, is likely mediated by homeostatic mechanisms such as synaptic scaling and regulation of intrinsic excitability. Because some of these homeostatic mechanisms depend on transcription of activity-regulated genes, including Arc and Homer1a, we hypothesized that activity-regulated transcription would be required for firing rate homeostasis. Surprisingly, however, we found that cultured mouse cortical neurons grown on multi-electrode arrays homeostatically adapt their firing rates to persistent pharmacological stimulation even when activity-regulated transcription is disrupted. Specifically, we observed firing rate homeostasis Arc knock-out neurons, as well as knock-out neurons lacking activity-regulated transcription factors, AP1 and SRF. Firing rate homeostasis also occurred normally during acute pharmacological blockade of transcription. Thus, firing rate homeostasis in response to increased neuronal activity can occur in the absence of neuronal-activity-regulated transcription.SIGNIFICANCE STATEMENTNeuronal circuits maintain relatively stable firing rates even in the face of dynamic circuit inputs. Understanding the molecular mechanisms that enable this firing rate homeostasis could potentially provide insight into neuronal diseases that present with an imbalance of excitation and inhibition. However, the molecular mechanisms underlying firing rate homeostasis are largely unknown.It has long been hypothesized that firing rate homeostasis relies upon neuronal activity-regulated transcription. For example, a 2012 review (PMID 22685679) proposed it, and a 2014 modeling approach established that transcription could theoretically both measure and control firing rate (PMID 24853940). Surprisingly, despite this prediction, we found that cortical neurons undergo firing rate homeostasis in the absence of activity-regulated transcription, indicating that firing rate homeostasis is controlled by non-transcriptional mechanisms.

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.

Quantitative Analysis of Abducens Neuron Discharge Dynamics During Saccadic and Slow Eye Movements

1999 ◽  
Vol 82 (5) ◽  
pp. 2612-2632 ◽  
Author(s):  
Pierre A. Sylvestre ◽  
Kathleen E. Cullen
Keyword(s):  
Eye Movements ◽  
Firing Rate ◽  
Eye Movement ◽  
Neuronal Activity ◽  
Smooth Pursuit ◽  
Slow Phase ◽  
Vestibular Nystagmus ◽  
Firing Rates ◽  
Rapid Eye Movements ◽  
Eye Velocity

The mechanics of the eyeball and its surrounding tissues, which together form the oculomotor plant, have been shown to be the same for smooth pursuit and saccadic eye movements. Hence it was postulated that similar signals would be carried by motoneurons during slow and rapid eye movements. In the present study, we directly addressed this proposal by determining which eye movement–based models best describe the discharge dynamics of primate abducens neurons during a variety of eye movement behaviors. We first characterized abducens neuron spike trains, as has been classically done, during fixation and sinusoidal smooth pursuit. We then systematically analyzed the discharge dynamics of abducens neurons during and following saccades, during step-ramp pursuit and during high velocity slow-phase vestibular nystagmus. We found that the commonly utilized first-order description of abducens neuron firing rates (FR = b + kE + rE˙, where FR is firing rate, E and E˙ are eye position and velocity, respectively, and b, k, and r are constants) provided an adequate model of neuronal activity during saccades, smooth pursuit, and slow phase vestibular nystagmus. However, the use of a second-order model, which included an exponentially decaying term or “slide” (FR = b + kE + rE˙ + uË − c[Formula: see text]), notably improved our ability to describe neuronal activity when the eye was moving and also enabled us to model abducens neuron discharges during the postsaccadic interval. We also found that, for a given model, a single set of parameters could not be used to describe neuronal firing rates during both slow and rapid eye movements. Specifically, the eye velocity and position coefficients ( r and k in the above models, respectively) consistently decreased as a function of the mean (and peak) eye velocity that was generated. In contrast, the bias ( b, firing rate when looking straight ahead) invariably increased with eye velocity. Although these trends are likely to reflect, in part, nonlinearities that are intrinsic to the extraocular muscles, we propose that these results can also be explained by considering the time-varying resistance to movement that is generated by the antagonist muscle. We conclude that to create realistic and meaningful models of the neural control of horizontal eye movements, it is essential to consider the activation of the antagonist, as well as agonist motoneuron pools.

Neural Responses to Facial Expression and Face Identity in the Monkey Amygdala

2007 ◽  
Vol 97 (2) ◽  
pp. 1671-1683 ◽  
Author(s):  
K. M. Gothard ◽  
F. P. Battaglia ◽  
C. A. Erickson ◽  
K. M. Spitler ◽  
D. G. Amaral
Keyword(s):  
Facial Expression ◽  
Firing Rate ◽  
Face Processing ◽  
Neural Responses ◽  
Firing Rates ◽  
Face Identity ◽  
Relative Contribution ◽  
The Face ◽  
Face Stimuli ◽  
Human Faces

The amygdala is purported to play an important role in face processing, yet the specificity of its activation to face stimuli and the relative contribution of identity and expression to its activation are unknown. In the current study, neural activity in the amygdala was recorded as monkeys passively viewed images of monkey faces, human faces, and objects on a computer monitor. Comparable proportions of neurons responded selectively to images from each category. Neural responses to monkey faces were further examined to determine whether face identity or facial expression drove the face-selective responses. The majority of these neurons (64%) responded both to identity and facial expression, suggesting that these parameters are processed jointly in the amygdala. Large fractions of neurons, however, showed pure identity-selective or expression-selective responses. Neurons were selective for a particular facial expression by either increasing or decreasing their firing rate compared with the firing rates elicited by the other expressions. Responses to appeasing faces were often marked by significant decreases of firing rates, whereas responses to threatening faces were strongly associated with increased firing rate. Thus global activation in the amygdala might be larger to threatening faces than to neutral or appeasing faces.

scholarly journals Computing and Stability in Cortical Networks

2004 ◽  
Vol 16 (7) ◽  
pp. 1385-1412 ◽  
Author(s):  
Peter E. Latham ◽  
Sheila Nirenberg
Keyword(s):  
Cortical Neurons ◽  
Unstable State ◽  
Stable States ◽  
Attractor Networks ◽  
Firing Rates ◽  
The Face ◽  
Multiple Stable States ◽  
Background State ◽  
Do So

Cortical neurons are predominantly excitatory and highly interconnected. In spite of this, the cortex is remarkably stable: normal brains do not exhibit the kind of runaway excitation one might expect of such a system. How does the cortex maintain stability in the face of this massive excitatory feedback? More importantly, how does it do so during computations, which necessarily involve elevated firing rates? Here we address these questions in the context of attractor networks—networks that exhibit multiple stable states, or memories. We find that such networks can be stabilized at the relatively low firing rates observed in vivo if two conditions are met: (1) the background state, where all neurons are firing at low rates, is inhibition dominated, and (2) the fraction of neurons involved in a memory is above some threshold, so that there is sufficient coupling between the memory neurons and the background. This allows “dynamical stabilization” of the attractors, meaning feedback from the pool of background neurons stabilizes what would otherwise be an unstable state. We suggest that dynamical stabilization may be a strategy used for a broad range of computations, not just those involving attractors.

scholarly journals Neuronal firing rates diverge during REM and homogenize during non-REM

2016 ◽  
Author(s):  
Hiroyuki Miyawaki ◽  
Brendon Watson ◽  
Kamran Diba
Keyword(s):  
Firing Rate ◽  
Frontal Cortex ◽  
Rem Sleep ◽  
Cortical Neurons ◽  
Nrem Sleep ◽  
Regression To The Mean ◽  
Firing Rates ◽  
The Mean ◽  
High Firing ◽  
Interneuron Activity

AbstractNeurons fire at highly variable innate rates and recent evidence suggests that low and high firing rate neurons display different plasticity and dynamics. Furthermore, recent publications imply possibly differing rate-dependent effects in hippocampus versus neocortex, but those analyses were carried out separately and with possibly important differences. To more effectively synthesize these questions, we analyzed the firing rate dynamics of populations of neurons in both hippocampal CA1 and frontal cortex under one framework that avoids pitfalls of previous analyses and accounts for regression-to-the-mean. We observed remarkably consistent effects across these regions. While rapid eye movement (REM) sleep was marked by decreased hippocampal firing and increased neocortical firing, in both regions firing rates distributions widened during REM due to differential changes in high-firing versus low-firing cells in parallel with increased interneuron activity. In contrast, upon non-REM (NREM) sleep, firing rate distributions narrowed while interneuron firing decreased. Interestingly, hippocampal interneuron activity closely followed the patterns observed in neocortical principal cells rather than the hippocampal principal cells, suggestive of long-range interactions. Following these undulations in variance, the net effect of sleep was a decrease in firing rates. These decreases were greater in lower-firing hippocampal neurons but higher-firing frontal cortical neurons, suggestive of greater plasticity in these cell groups. Our results across two different regions and with statistical corrections indicate that the hippocampus and neocortex show a mixture of differences and similarities as they cycle between sleep states with a unifying characteristic of homogenization of firing during NREM and diversification during REM.Significance StatementMiyawaki and colleagues analyze firing patterns across low-firing and high-firing neurons in the hippocampus and the frontal cortex throughout sleep in a framework that accounts for regression-to-the-mean. They find that in both regions REM sleep activity is relatively dominated by high-firing neurons and increased inhibition, resulting in a wider distribution of firing rates. On the other hand, NREM sleep produces lower inhibition, and results in a more homogenous distribution of firing rates. Integration of these changes across sleep results in net decrease of firing rates with largest drops in low-firing hippocampal pyramidal neurons and high-firing neocortical principal neurons. These findings provide insights into the effects and functions of different sleep stages on cortical neurons.

scholarly journals Firing rate distributions in spiking networks with heterogeneous connectivity

2019 ◽  
Author(s):  
Marina Vegué ◽  
Alex Roxin
Keyword(s):  
Experimental Data ◽  
Firing Rate ◽  
Neuronal Activity ◽  
Diffusion Approximation ◽  
Large Amplitude ◽  
Spiking Neurons ◽  
Arbitrary Degree ◽  
Current Form ◽  
Firing Rates ◽  
Spiking Networks

AbstractMeanfield theory for networks of spiking neurons based on the so-called diffusion approximation has been used to calculate certain measures of neuronal activity which can be compared with experimental data. This includes the distribution of firing rates across the network. However, the theory in its current form applies only to networks in which there is relatively little heterogeneity in the number of incoming and outgoing connections per neuron. Here we extend this theory to include networks with arbitrary degree distributions. Furthermore, the theory takes into account correlations in the in-degree and out-degree of neurons, which would arise e.g. in the case of networks with hub-like neurons. Finally, we show that networks with broad and postively correlated degrees can generate a large-amplitude sustained response to transient stimuli which does not occur in more homogeneous networks.

scholarly journals Separating Spike Count Correlation from Firing Rate Correlation

2016 ◽  
Vol 28 (5) ◽  
pp. 849-881 ◽  
Author(s):  
Giuseppe Vinci ◽  
Valérie Ventura ◽  
Matthew A. Smith ◽  
Robert E. Kass
Keyword(s):  
Firing Rate ◽  
Cortical Neurons ◽  
Experimental Manipulation ◽  
Response Variability ◽  
Spike Count ◽  
Firing Rates ◽  
Area V4 ◽  
Rate Correlation ◽  
Over Time

Populations of cortical neurons exhibit shared fluctuations in spiking activity over time. When measured for a pair of neurons over multiple repetitions of an identical stimulus, this phenomenon emerges as correlated trial-to-trial response variability via spike count correlation (SCC). However, spike counts can be viewed as noisy versions of firing rates, which can vary from trial to trial. From this perspective, the SCC for a pair of neurons becomes a noisy version of the corresponding firing rate correlation (FRC). Furthermore, the magnitude of the SCC is generally smaller than that of the FRC and is likely to be less sensitive to experimental manipulation. We provide statistical methods for disambiguating time-averaged drive from within-trial noise, thereby separating FRC from SCC. We study these methods to document their reliability, and we apply them to neurons recorded in vivo from area V4 in an alert animal. We show how the various effects we describe are reflected in the data: within-trial effects are largely negligible, while attenuation due to trial-to-trial variation dominates and frequently produces comparisons in SCC that, because of noise, do not accurately reflect those based on the underlying FRC.

scholarly journals Dynamic Gain Changes During Attentional Modulation

2006 ◽  
Vol 18 (8) ◽  
pp. 1847-1867 ◽  
Author(s):  
Arun P. Sripati ◽  
Kenneth O. Johnson
Keyword(s):  
Firing Rate ◽  
Cortical Neurons ◽  
Model Neuron ◽  
Rate Adaptation ◽  
Recognition Task ◽  
Fano Factor ◽  
Attentional Modulation ◽  
Firing Rates ◽  
Spike Threshold ◽  
Firing Rate Adaptation

Attention causes a multiplicative effect on firing rates of cortical neurons without affecting their selectivity (Motter, 1993; McAdams & Maunsell, 1999a) or the relationship between the spike count mean and variance (McAdams & Maunsell, 1999b). We analyzed attentional modulation of the firing rates of 144 neurons in the secondary somatosensory cortex (SII) of two monkeys trained to switch their attention between a tactile pattern recognition task and a visual task. We found that neurons in SII cortex also undergo a predominantly multiplicative modulation in firing rates without affecting the ratio of variance to mean firing rate (i.e., the Fano factor). Furthermore, both additive and multiplicative components of attentional modulation varied dynamically during the stimulus presentation. We then used a standard conductance-based integrate-and-fire model neuron to ascertain which mechanisms might account for a multiplicative increase in firing rate without affecting the Fano factor. Six mechanisms were identified as biophysically plausible ways that attention could modify the firing rate: spike threshold, firing rate adaptation, excitatory input synchrony, synchrony between all inputs, membrane leak resistance, and reset potential. Of these, only a change in spike threshold or in firing rate adaptation affected model firing rates in a manner compatible with the observed neural data. The results indicate that only a limited number of biophysical mechanisms can account for observed attentional modulation.

Different aspects of the face are represented in the dynamic firing rate patterns of macaque temporal cortical neurons

1998 ◽  
Vol 31 ◽  
pp. S184
Author(s):  
Yasuko Sugase ◽  
Sigeru Yamane ◽  
Shoogo Ueno ◽  
Kenji Kawano
Keyword(s):  
Firing Rate ◽  
Cortical Neurons ◽  
The Face

scholarly journals Attractor cortical neurodynamics, schizophrenia, and depression

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Edmund T. Rolls
Keyword(s):  
Nmda Receptor ◽  
Functional Connectivity ◽  
Firing Rate ◽  
Orbitofrontal Cortex ◽  
Cortical Neurons ◽  
Receptor Function ◽  
Firing Rates ◽  
High Firing Rate ◽  
Nmda Receptor Function ◽  
High Firing

AbstractThe local recurrent collateral connections between cortical neurons provide a basis for attractor neural networks for memory, attention, decision-making, and thereby for many aspects of human behavior. In schizophrenia, a reduction of the firing rates of cortical neurons, caused for example by reduced NMDA receptor function or reduced spines on neurons, can lead to instability of the high firing rate attractor states that normally implement short-term memory and attention in the prefrontal cortex, contributing to the cognitive symptoms. Reduced NMDA receptor function in the orbitofrontal cortex by reducing firing rates may produce negative symptoms, by reducing reward, motivation, and emotion. Reduced functional connectivity between some brain regions increases the temporal variability of the functional connectivity, contributing to the reduced stability and more loosely associative thoughts. Further, the forward projections have decreased functional connectivity relative to the back projections in schizophrenia, and this may reduce the effects of external bottom-up inputs from the world relative to internal top-down thought processes. Reduced cortical inhibition, caused by a reduction of GABA neurotransmission, can lead to instability of the spontaneous firing states of cortical networks, leading to a noise-induced jump to a high firing rate attractor state even in the absence of external inputs, contributing to the positive symptoms of schizophrenia. In depression, the lateral orbitofrontal cortex non-reward attractor network system is over-connected and has increased sensitivity to non-reward, providing a new approach to understanding depression. This is complemented by under-sensitivity and under-connectedness of the medial orbitofrontal cortex reward system in depression.

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