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.

Measurement and nature of firing rate adaptation in turtle spinal neurons

2005 ◽  
Vol 191 (7) ◽  
pp. 583-603 ◽  
Author(s):  
R. B. Gorman ◽  
J. C. McDonagh ◽  
T. G. Hornby ◽  
R. M. Reinking ◽  
D. G. Stuart
Keyword(s):  
Firing Rate ◽  
Rate Adaptation ◽  
Spinal Neurons ◽  
Firing Rate Adaptation

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.

Firing Rate Adaptation without Losing Sensitivity to Input Fluctuations

2002 ◽  
pp. 180-185 ◽  
Author(s):  
Giancarlo La Camera ◽  
Alexander Rauch ◽  
Walter Senn ◽  
Hans-R. Lüscher ◽  
Stefano Fusi
Keyword(s):  
Firing Rate ◽  
Rate Adaptation ◽  
Firing Rate Adaptation

scholarly journals Midbrain adaptation may set the stage for the perception of musical beat

2017 ◽  
Vol 284 (1866) ◽  
pp. 20171455 ◽  
Author(s):  
Vani G. Rajendran ◽  
Nicol S. Harper ◽  
Jose A. Garcia-Lazaro ◽  
Nicholas A. Lesica ◽  
Jan W. H. Schnupp
Keyword(s):  
Firing Rate ◽  
Cognitive Processes ◽  
Auditory Processing ◽  
Temporal Context ◽  
Present Evidence ◽  
Firing Rates ◽  
Sensory Motor ◽  
Specific Manner ◽  
Musical Rhythms ◽  
Firing Rate Adaptation

The ability to spontaneously feel a beat in music is a phenomenon widely believed to be unique to humans. Though beat perception involves the coordinated engagement of sensory, motor and cognitive processes in humans, the contribution of low-level auditory processing to the activation of these networks in a beat-specific manner is poorly understood. Here, we present evidence from a rodent model that midbrain preprocessing of sounds may already be shaping where the beat is ultimately felt. For the tested set of musical rhythms, on-beat sounds on average evoked higher firing rates than off-beat sounds, and this difference was a defining feature of the set of beat interpretations most commonly perceived by human listeners over others. Basic firing rate adaptation provided a sufficient explanation for these results. Our findings suggest that midbrain adaptation, by encoding the temporal context of sounds, creates points of neural emphasis that may influence the perceptual emergence of a beat.

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 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.

Adaptation and Inhibition Underlie Responses to Time-Varying Interaural Phase Cues in a Model of Inferior Colliculus Neurons

2002 ◽  
Vol 88 (4) ◽  
pp. 2134-2146 ◽  
Author(s):  
Alla Borisyuk ◽  
Malcolm N. Semple ◽  
John Rinzel
Keyword(s):  
Firing Rate ◽  
Inferior Colliculus ◽  
Rate Adaptation ◽  
Phase Shifts ◽  
Hysteresis Curve ◽  
Medial Superior Olive ◽  
Binaural Beats ◽  
Dynamic Tuning ◽  
Interaural Phase ◽  
Firing Rate Adaptation

A mathematical model was developed for exploring the sensitivity of low-frequency inferior colliculus (IC) neurons to interaural phase disparity (IPD). The formulation involves a firing-rate-type model that does not include spikes per se. The model IC neuron receives IPD-tuned excitatory and inhibitory inputs (viewed as the output of a collection of cells in the medial superior olive). The model cell possesses cellular properties of firing rate adaptation and postinhibitory rebound (PIR). The descriptions of these mechanisms are biophysically reasonable, but only semi-quantitative. We seek to explain within a minimal model the experimentally observed mismatch between responses to IPD stimuli delivered dynamically and those delivered statically ( McAlpine et al. 2000 ; Spitzer and Semple 1993 ). The model reproduces many features of the responses to static IPD presentations, binaural beat, and partial range sweep stimuli. These features include differences in responses to a stimulus presented in static or dynamic context: sharper tuning and phase shifts in response to binaural beats, and hysteresis and “rise-from-nowhere” in response to partial range sweeps. Our results suggest that dynamic response features are due to the structure of inputs and the presence of firing rate adaptation and PIR mechanism in IC cells, but do not depend on a specific biophysical mechanism. We demonstrate how the model's various components contribute to shaping the observed phenomena. For example, adaptation, PIR, and transmission delay shape phase advances and delays in responses to binaural beats, adaptation and PIR shape hysteresis in different ranges of IPD, and tuned inhibition underlies asymmetry in dynamic tuning properties. We also suggest experiments to test our modeling predictions: in vitro simulation of the binaural beat (phase advance at low beat frequencies, its dependence on firing rate), in vivo partial range sweep experiments (dependence of the hysteresis curve on parameters), and inhibition blocking experiments (to study inhibitory tuning properties by observation of phase shifts).

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.

scholarly journals Adaptive coding for dynamic sensory inference

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Wiktor F Młynarski ◽  
Ann M Hermundstad
Keyword(s):  
Firing Rate ◽  
Rate Adaptation ◽  
Sensory Systems ◽  
Neural Representation ◽  
High Fidelity ◽  
Neural Responses ◽  
Adaptive Coding ◽  
Optimal Coding ◽  
Adaptive Encoding ◽  
Firing Rate Adaptation

Behavior relies on the ability of sensory systems to infer properties of the environment from incoming stimuli. The accuracy of inference depends on the fidelity with which behaviorally relevant properties of stimuli are encoded in neural responses. High-fidelity encodings can be metabolically costly, but low-fidelity encodings can cause errors in inference. Here, we discuss general principles that underlie the tradeoff between encoding cost and inference error. We then derive adaptive encoding schemes that dynamically navigate this tradeoff. These optimal encodings tend to increase the fidelity of the neural representation following a change in the stimulus distribution, and reduce fidelity for stimuli that originate from a known distribution. We predict dynamical signatures of such encoding schemes and demonstrate how known phenomena, such as burst coding and firing rate adaptation, can be understood as hallmarks of optimal coding for accurate inference.

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