Phase-Resetting Analysis of Gamma-Frequency Synchronization of Cortical Fast-Spiking Interneurons Using Synaptic-like Conductance Injection

2011 ◽  
pp. 489-509 ◽  
Author(s):  
Hugo Zeberg ◽  
Nathan W. Gouwens ◽  
Kunichika Tsumoto ◽  
Takashi Tateno ◽  
Kazuyuki Aihara ◽  
...  
Keyword(s):  
Frequency Synchronization ◽  
Phase Resetting ◽  
Gamma Frequency ◽  
Fast Spiking

scholarly journals Frequency-dependent entrainment of striatal fast-spiking interneurons

2019 ◽  
Vol 122 (3) ◽  
pp. 1060-1072 ◽  
Author(s):  
Matthew H. Higgs ◽  
Charles J. Wilson
Keyword(s):  
Action Potentials ◽  
Frequency Tuning ◽  
Brain Slices ◽  
Stimulus Frequency ◽  
Broadband Noise ◽  
Constant Current ◽  
Phase Resetting ◽  
Gamma Frequency ◽  
Frequency Components ◽  
Fast Spiking

Striatal fast-spiking interneurons (FSIs) fire in variable-length runs of action potentials at 20–200 spikes/s separated by pauses. In vivo, or with fluctuating applied current, both runs and pauses become briefer and more variable. During runs, spikes are entrained specifically to gamma-frequency components of the input fluctuations. We stimulated parvalbumin-expressing striatal FSIs in mouse brain slices with broadband noise currents added to direct current steps and measured spike entrainment across all frequencies. As the constant current level was increased, FSIs produced longer runs and showed sharper frequency tuning, with best entrainment at the stimulus frequency matching their intrarun firing rate. We separated the contributions of previous spikes from that of the fluctuating stimulus, revealing a strong contribution of previous action potentials to gamma-frequency entrainment. In contrast, after subtraction of the effect inherited from the previous spike, the remaining stimulus contribution to spike generation was less sharply tuned, showing a larger contribution of lower frequencies. The frequency specificity of entrainment within a run was reproduced with a phase resetting model based on experimentally measured phase resetting curves of the same FSIs. In the model, broadly tuned phase entrainment for the first spike in a run evolved into sharply tuned gamma entrainment over the next few spikes. The data and modeling results indicate that for FSIs firing in brief runs and pauses firing within runs is entrained by gamma-frequency components of the input, whereas the onset timing of runs may be sensitive to a wider range of stimulus frequency components. NEW & NOTEWORTHY Specific types of neurons entrain their spikes to particular oscillation frequencies in their synaptic input. This entrainment is commonly understood in terms of the subthreshold voltage response, but how this translates to spiking is not clear. We show that in striatal fast-spiking interneurons, entrainment to gamma-frequency input depends on rhythmic spike runs and is explained by the phase resetting curve, whereas run initiation can be triggered by a broad range of input frequencies.

scholarly journals Synchronization of Firing in Cortical Fast-Spiking Interneurons at Gamma Frequencies: A Phase-Resetting Analysis

2010 ◽  
Vol 6 (9) ◽  
pp. e1000951 ◽  
Author(s):  
Nathan W. Gouwens ◽  
Hugo Zeberg ◽  
Kunichika Tsumoto ◽  
Takashi Tateno ◽  
Kazuyuki Aihara ◽  
...  
Keyword(s):  
Phase Resetting ◽  
Fast Spiking ◽  
Gamma Frequencies

scholarly journals The ionic mechanism of gamma resonance in rat striatal fast-spiking neurons

2011 ◽  
Vol 106 (6) ◽  
pp. 2936-2949 ◽  
Author(s):  
Giuseppe Sciamanna ◽  
Charles J. Wilson
Keyword(s):  
Firing Rate ◽  
Firing Pattern ◽  
Field Potential ◽  
Gamma Oscillations ◽  
Repetitive Firing ◽  
Spike Threshold ◽  
Gamma Frequency ◽  
Subthreshold Oscillations ◽  
Fast Spiking

Striatal fast-spiking (FS) cells in slices fire in the gamma frequency range and in vivo are often phase-locked to gamma oscillations in the field potential. We studied the firing patterns of these cells in slices from rats ages 16–23 days to determine the mechanism of their gamma resonance. The resonance of striatal FS cells was manifested as a minimum frequency for repetitive firing. At rheobase, cells fired a doublet of action potentials or doublets separated by pauses, with an instantaneous firing rate averaging 44 spikes/s. The minimum rate for sustained firing was also responsible for the stuttering firing pattern. Firing rate adapted during each episode of firing, and bursts were terminated when firing was reduced to the minimum sustainable rate. Resonance and stuttering continued after blockade of Kv3 current using tetraethylammonium (0.1–1 mM). Both gamma resonance and stuttering were strongly dependent on Kv1 current. Blockade of Kv1 channels with dendrotoxin-I (100 nM) completely abolished the stuttering firing pattern, greatly lowered the minimum firing rate, abolished gamma-band subthreshold oscillations, and slowed spike frequency adaptation. The loss of resonance could be accounted for by a reduction in potassium current near spike threshold and the emergence of a fixed spike threshold. Inactivation of the Kv1 channel combined with the minimum firing rate could account for the stuttering firing pattern. The resonant properties conferred by this channel were shown to be adequate to account for their phase-locking to gamma-frequency inputs as seen in vivo.

Gamma frequency synchronization in a local cortical network model

2004 ◽  
Vol 58-60 ◽  
pp. 173-178 ◽  
Author(s):  
Masaki Nomura ◽  
Toshio Aoyagi ◽  
Tomoki Fukai
Keyword(s):  
Network Model ◽  
Cortical Network ◽  
Frequency Synchronization ◽  
Gamma Frequency

Increased γ- and Decreased δ-Oscillations in a Mouse Deficient for a Potassium Channel Expressed in Fast-Spiking Interneurons

1999 ◽  
Vol 82 (4) ◽  
pp. 1855-1864 ◽  
Author(s):  
Rolf H. Joho ◽  
Chi Shun Ho ◽  
Gerald A. Marks
Keyword(s):  
Mutant Mouse ◽  
Spectral Power ◽  
Thalamic Reticular Nucleus ◽  
Reticular Nucleus ◽  
High Threshold ◽  
Cortical Function ◽  
Fourfold Increase ◽  
Gamma Frequency ◽  
Fast Spiking ◽  
Delta Oscillations

Kv3.1 is a voltage-gated, fast activating/deactivating potassium (K+) channel with a high-threshold of activation and a large unit conductance. Kv3.1 K+ channels are expressed in fast-spiking, parvalbumin-containing interneurons in cortex, hippocampus, striatum, the thalamic reticular nucleus (TRN), and in several nuclei of the brain stem. A high density of Kv3.1 channels contributes to short-duration action potentials, fast afterhyperpolarizations, and brief refractory periods enhancing the capability in these neurons for high-frequency firing. Kv3.1 K+ channel expression in the TRN and cortex also suggests a role in thalamocortical and cortical function. Here we show that fast gamma and slow delta oscillations recorded from the somatomotor cortex are altered in the freely behaving Kv3.1 mutant mouse. Electroencephalographic (EEG) recordings from homozygous Kv3.1−/− mice show a three- to fourfold increase in both absolute and relative spectral power in the gamma frequency range (20–60 Hz). In contrast, Kv3.1-deficient mice have a 20–50% reduction of power in the slow delta range (2–3 Hz). The increase in gamma power is most prominent during waking in the 40- to 55-Hz range, whereas the decrease in delta power occurs equally across all states of arousal. Our findings suggest that Kv3.1-expressing neurons are involved in the generation and maintenance of cortical fast gamma and slow delta oscillations. Hence the Kv3.1-mutant mouse could serve as a model to study the generation and maintenance of fast gamma and slow delta rhythms and their involvement in behavior and cognition.

scholarly journals Author response: Serotonin enhances excitability and gamma frequency temporal integration in mouse prefrontal fast-spiking interneurons

2017 ◽  
Author(s):  
Jegath C Athilingam ◽  
Roy Ben-Shalom ◽  
Caroline M Keeshen ◽  
Vikaas S Sohal ◽  
Kevin J Bender
Keyword(s):  
Temporal Integration ◽  
Author Response ◽  
Gamma Frequency ◽  
Fast Spiking

scholarly journals Influence of NMDA and GABA synaptic dysfunction on the evoked gamma oscillations in a computational model of schizophrenia

Biologija ◽  
2017 ◽  
Vol 63 (2) ◽  
Author(s):  
Rokas Jackevičius ◽  
Bruce P. Graham ◽  
Aušra Saudargienė
Keyword(s):  
Neural Network ◽  
Computational Modeling ◽  
Computational Model ◽  
Time Constant ◽  
Gamma Oscillations ◽  
Synaptic Dysfunction ◽  
Spiking Neural Network ◽  
Gamma Frequency ◽  
Gated Channel ◽  
Fast Spiking

Background. Schizophrenia is a psychiatric disorder which is characterized by delusions and hallucinations, and affects thoughts, behaviour and emotions. Major neuronal degeneration is not observed in schizophrenic patients, but abnormalities in cortical circuits are present. These abnormalities are reflected in impaired EEG gamma frequency (30–80 Hz), being crucial for many processes including sensation, perception, working memory, and attention. NMDA and GABA synaptic dysfunction is proposed as one of the possible mechanisms underlying the gamma oscillatory deficits in schizophrenia. Materials and Methods. We used a computational modeling approach to investigate the joint influence of NMDA and GABA synaptic dysfunction on gamma oscillations in cortex. We employed a computational model of a spiking neural network composed of 800 pyramidal neurons, 150 regular-spiking interneurons, and 50 fast-spiking interneurons. All cells were randomly interconnected. Network neurons received independent Poisson noise input at 4 Hz and 40 Hz drive excitatory stimulation. Fast-spiking interneuron GABA receptor-gated channel time constant was increased and NMDA receptor-gated channel synaptic conductance was decreased to represent synaptic dysfunction in schizophrenia. Results. Reducing NMDA conductance enhanced gamma power, and increasing decay time constant of GABA receptorgated channel attenuated gamma generation in a network. The effect of synaptic GABA alteration was more profound. Conclusions. NMDA and GABA synaptic dysfunction leads to the impaired gamma frequency oscillations in a spiking neural network of cortex. Computational modeling approach is a powerful tool to understand complex non-linear dynamical systems and intrinsic mechanisms of neuronal network activity in healthy and diseased brain.

scholarly journals Striatal fast-spiking interneurons drive habitual behavior

2017 ◽  
Author(s):  
Justin K. O’Hare ◽  
Haofang Li ◽  
Namsoo Kim ◽  
Erin Gaidis ◽  
Kristen K. Ade ◽  
...  
Keyword(s):  
Habit Formation ◽  
Behavioral Adaptation ◽  
Single Class ◽  
Gamma Frequency ◽  
Dependent Behavior ◽  
Selective Modulation ◽  
Lever Pressing ◽  
Fast Spiking ◽  
Habitual Behavior

AbstractHabit formation is a behavioral adaptation that automates routine actions. Habitual behavior correlates with broad reconfigurations of dorsolateral striatal (DLS) circuit properties that increase gain and shift pathway timing. The mechanism(s) for these circuit adaptations are unknown and could be responsible for habitual behavior. Here we find that a single class of interneuron, fast-spiking interneurons (FSIs), modulates all of these habit-predictive properties. Consistent with a role in habits, FSIs are more excitable in habitual mice compared to goal-directed and acute chemogenetic inhibition of FSIs in DLS prevents the expression of habitual lever pressing. In vivo recordings further reveal a previously unappreciated selective modulation of SPNs based on their firing patterns; FSIs inhibit most SPNs but paradoxically promote the activity of a subset displaying high fractions of gamma-frequency spiking. These results establish a microcircuit mechanism for habits and provide a new example of how interneurons mediate experience-dependent behavior.

scholarly journals Striatal fast-spiking interneurons selectively modulate circuit output and are required for habitual behavior

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Justin K O'Hare ◽  
Haofang Li ◽  
Namsoo Kim ◽  
Erin Gaidis ◽  
Kristen Ade ◽  
...  
Keyword(s):  
Behavioral Adaptation ◽  
Single Class ◽  
Gamma Frequency ◽  
Dependent Behavior ◽  
Selective Modulation ◽  
Lever Pressing ◽  
Fast Spiking ◽  
Habitual Behavior ◽  
Circuit Output

Habit formation is a behavioral adaptation that automates routine actions. Habitual behavior correlates with broad reconfigurations of dorsolateral striatal (DLS) circuit properties that increase gain and shift pathway timing. The mechanism(s) for these circuit adaptations are unknown and could be responsible for habitual behavior. Here we find that a single class of interneuron, fast-spiking interneurons (FSIs), modulates all of these habit-predictive properties. Consistent with a role in habits, FSIs are more excitable in habitual mice compared to goal-directed and acute chemogenetic inhibition of FSIs in DLS prevents the expression of habitual lever pressing. In vivo recordings further reveal a previously unappreciated selective modulation of SPNs based on their firing patterns; FSIs inhibit most SPNs but paradoxically promote the activity of a subset displaying high fractions of gamma-frequency spiking. These results establish a microcircuit mechanism for habits and provide a new example of how interneurons mediate experience-dependent behavior.

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