scholarly journals Phase-tuned neuronal firing encodes human contextual representations for navigational goals

2017 ◽  
Author(s):  
Andrew J Watrous ◽  
Jonathan Miller ◽  
Salman E Qasim ◽  
Itzhak Fried ◽  
Joshua Jacobs
Keyword(s):  
Firing Rate ◽  
Medial Temporal Lobe ◽  
Single Neuron ◽  
Detection Algorithm ◽  
Neuronal Firing ◽  
Neural Firing ◽  
Categorical Information ◽  
High Frequency Activity ◽  
Oscillation Detection ◽  
Intracranial Recordings

AbstractWe previously demonstrated that the phase of oscillations modulates neural activity representing categorical information using human intracranial recordings and high-frequency activity from local field potentials (Watrous et al., 2015b). We extend these findings here using human single-neuron recordings during a navigation task. We identify neurons in the medial temporal lobe with firing-rate modulations for specific navigational goals, as well as for navigational planning and goal arrival. Going beyond this work, using a novel oscillation detection algorithm, we identify phase-locked neural firing that encodes information about a person’s prospective navigational goal in the absence of firing rate changes. These results provide evidence for navigational planning and contextual accounts of human MTL function at the single-neuron level. More generally, our findings identify phase-coded neuronal firing as a component of the human neural code.

scholarly journals Phase-tuned neuronal firing encodes human contextual representations for navigational goals

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Andrew J Watrous ◽  
Jonathan Miller ◽  
Salman E Qasim ◽  
Itzhak Fried ◽  
Joshua Jacobs
Keyword(s):  
Firing Rate ◽  
Medial Temporal Lobe ◽  
Single Neuron ◽  
Detection Algorithm ◽  
Neuronal Firing ◽  
Neural Firing ◽  
Categorical Information ◽  
High Frequency Activity ◽  
Oscillation Detection ◽  
Intracranial Recordings

We previously demonstrated that the phase of oscillations modulates neural activity representing categorical information using human intracranial recordings and high-frequency activity from local field potentials (Watrous et al., 2015b). We extend these findings here using human single-neuron recordings during a virtual navigation task. We identify neurons in the medial temporal lobe with firing-rate modulations for specific navigational goals, as well as for navigational planning and goal arrival. Going beyond this work, using a novel oscillation detection algorithm, we identify phase-locked neural firing that encodes information about a person’s prospective navigational goal in the absence of firing rate changes. These results provide evidence for navigational planning and contextual accounts of human MTL function at the single-neuron level. More generally, our findings identify phase-coded neuronal firing as a component of the human neural code.

Global attractor alphabet of neural firing modes

2013 ◽  
Vol 110 (4) ◽  
pp. 907-915 ◽  
Author(s):  
Yoram Baram
Keyword(s):  
Firing Rate ◽  
Global Attractor ◽  
Single Neuron ◽  
Global Attractors ◽  
Neuronal Firing ◽  
Passive State ◽  
Rate Model ◽  
Neural Firing ◽  
Neural Interaction ◽  
Parameter Dependent

The elementary set, or alphabet, of neural firing modes is derived from the widely accepted conductance-based rectified firing-rate model. The firing dynamics of interacting neurons are shown to be governed by a multidimensional bilinear threshold discrete iteration map. The parameter-dependent global attractors of the map morph into 12 attractor types. Consistent with the dynamic modes observed in biological neuronal firing, the global attractor alphabet is highly visual and intuitive in the scalar, single-neuron case. As synapse permeability varies from high depression to high potentiation, the global attractor type varies from chaotic to multiplexed, oscillatory, fixed, and saturated. As membrane permeability decreases, the global attractor transforms from active to passive state. Under the same activation, learning and retrieval end at the same global attractor. The bilinear threshold structure of the multidimensional map associated with interacting neurons generalizes the global attractor alphabet of neuronal firing modes to multineuron systems. Selective positive or negative activation and neural interaction yield combinatorial revelation and concealment of stored neuronal global attractors.

scholarly journals Persistent hippocampal neural firing and hippocampal-cortical coupling predict verbal working memory load

2019 ◽  
Vol 5 (3) ◽  
pp. eaav3687 ◽  
Author(s):  
Ece Boran ◽  
Tommaso Fedele ◽  
Peter Klaver ◽  
Peter Hilfiker ◽  
Lennart Stieglitz ◽  
...  
Keyword(s):  
Working Memory ◽  
Neural Activity ◽  
Medial Temporal Lobe ◽  
Memory Load ◽  
Human Subjects ◽  
Verbal Working Memory ◽  
Neuronal Firing ◽  
Neural Firing ◽  
Scalp Eeg ◽  
Oscillatory Coupling

The maintenance of items in working memory relies on persistent neural activity in a widespread network of brain areas. To investigate the influence of load on working memory, we asked human subjects to maintain sets of letters in memory while we recorded single neurons and intracranial encephalography (EEG) in the medial temporal lobe and scalp EEG. Along the periods of a trial, hippocampal neural firing differentiated between success and error trials during stimulus encoding, predicted workload during memory maintenance, and predicted the subjects’ behavior during retrieval. During maintenance, neuronal firing was synchronized with intracranial hippocampal EEG. On the network level, synchronization between hippocampal and scalp EEG in the theta-alpha frequency range showed workload dependent oscillatory coupling between hippocampus and cortex. Thus, we found that persistent neural activity in the hippocampus participated in working memory processing that is specific to memory maintenance, load sensitive and synchronized to the cortex.

scholarly journals Dissociation of broadband high-frequency activity and neuronal firing in the neocortex

2019 ◽  
Author(s):  
Marcin Leszczynski ◽  
Annamaria Barczak ◽  
Yoshinao Kajikawa ◽  
Istvan Ulbert ◽  
Arnaud Falchier ◽  
...  
Keyword(s):  
High Frequency ◽  
Field Potential ◽  
Bimodal Distribution ◽  
Neuronal Population ◽  
Superficial Layer ◽  
Blood Oxygenation ◽  
Neuronal Firing ◽  
Neural Firing ◽  
Action Potential Firing ◽  
High Frequency Activity

Broadband High-frequency Activity (BHA; 70-150 Hz), also known as "high gamma," a key analytic signal in human intracranial recordings is often assumed to reflect local neural firing (multiunit activity; MUA). Accordingly, BHA has been used to study neuronal population responses in auditory (1,2), visual (3,4), language (5), mnemonic processes (6-9) and cognitive control (10,11). BHA is arguably the electrophysiological measure best correlated with the Blood Oxygenation Level Dependent (BOLD) signal in fMRI (12-13). However, beyond the fact that BHA correlates with neuronal spiking (12, 14-16), the neuronal populations and physiological processes generating BHA are not precisely defined. Although critical for interpreting intracranial signals in human and non-human primates, the precise physiology of BHA remains unknown. Here, we show that BHA dissociates from MUA in primary visual and auditory cortex. Using laminar multielectrode data in monkeys, we found a bimodal distribution of stimulus-evoked BHA across depth of a cortical column: an early-deep, followed by a later-superficial layer response. Only, the early-deep layer BHA had a clear local MUA correlate, while the more prominent superficial layer BHA had a weak or undetectable MUA correlate. In many cases, particularly in V1 (70%), supragranular sites showed strong BHA in lieu of any detectable increase in MUA. Due to volume conduction, BHA from both the early-deep and the later-supragranular generators contribute to the field potential at the pial surface, though the contribution may be weighted towards the late-supragranular BHA. Our results demonstrate that the strongest generators of BHA are in the superficial cortical layers and show that the origins of BHA include a mixture of the neuronal action potential firing and dendritic processes separable from this firing. It is likely that the typically-recorded BHA signal emphasizes the latter processes to a greater extent than previously recognized.

Multiple Sites of Action Potential Initiation Increase Neuronal Firing Rate

2001 ◽  
Vol 86 (3) ◽  
pp. 1226-1236 ◽  
Author(s):  
Stephen A. Baccus ◽  
Christie L. Sahley ◽  
Kenneth J. Muller
Keyword(s):  
Firing Rate ◽  
Sensory Input ◽  
Single Neuron ◽  
Neuronal Firing ◽  
Initiation Increase ◽  
Mechanosensory Neurons ◽  
Neuronal Firing Rate ◽  
Action Potential Initiation ◽  
Sites Of Action ◽  
Potential Initiation

Sensory input to an individual interneuron or motoneuron typically evokes activity at a single site, the initial segment, so that firing rate reflects the balance of excitation and inhibition there. In a network of cells that are electrically coupled, a sensory input produced by appropriate, localized stimulation can cause impulses to be initiated in several places. An example in the leech is the chain of S cells, which are critical for sensitization of reflex responses to mechanosensory stimulation. S cells, one per segment, form an electrically coupled chain extending the entire length of the CNS. Each S cell receives input from mechanosensory neurons in that segment. Because impulses can arise in any S cell and can reliably propagate throughout the chain, all the S cells behave like a single neuron with multiple initiation sites. In the present experiments, well-defined stimuli applied to a small area of skin evoked mechanosensory action potentials that propagated centrally to several segments, producing S cell impulses in those segments. Following pressure to the skin, impulses arose first in the S cell of the same segment as the stimulus, followed by impulses in S cells in other segments. Often four or five separate initiation sites were observed. This timing of impulse initiation played an important role in increasing the frequency of firing. Impulses arising at different sites did not usually collide but added to the total firing rate of the chain. A computational model is presented to illustrate how mechanosensory neurons distribute the effects of a single sensory stimulus into spatially and temporally separated synaptic input. The model predicts that changes in impulse propagation in mechanosensory neurons can alter S cell frequency of firing by changing the number of initiation sites.

scholarly journals Neural basis of functional ultrasound signals

2021 ◽  
Author(s):  
Anwar O. Nunez-Elizalde ◽  
Michael Krumin ◽  
Charu Bai Reddy ◽  
Gabriel Montaldo ◽  
Alan Urban ◽  
...  
Keyword(s):  
Firing Rate ◽  
Neural Activity ◽  
Brain Regions ◽  
Neuronal Firing ◽  
Linear Filter ◽  
Neural Firing ◽  
Hemodynamic Response Function ◽  
Neural Basis ◽  
Neural Recordings ◽  
Highly Correlated

SummaryFunctional ultrasound imaging (fUSI) is a popular method for studying brain function, but it remains unclear to what degree its signals reflect neural activity on a trial-by-trial basis. Here, we answer this question with simultaneous fUSI and neural recordings with Neuropixels probes in awake mice. fUSI signals strongly correlated with the slow (<0.3 Hz) fluctuations in firing rate measured in the same location and were closely predicted by convolving the firing rate with a 2.9 s wide linear filter. This filter matched the hemodynamic response function of awake mouse and was invariant across mice, stimulus conditions, and brain regions. fUSI signals matched neural firing also spatially: recordings with two probes revealed that firing rates were as highly correlated across hemispheres as fUSI signals. We conclude that fUSI signals bear a simple linear relationship to neuronal firing and accurately reflect neural activity both in time and in space.

scholarly journals Phase-amplitude coupling supports phase coding in human ECoG

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Andrew J Watrous ◽  
Lorena Deuker ◽  
Juergen Fell ◽  
Nikolai Axmacher
Keyword(s):  
Human Brain ◽  
High Frequency ◽  
Low Frequency ◽  
Phase Coding ◽  
Neural Representations ◽  
Simultaneous Acquisition ◽  
Categorical Information ◽  
High Frequency Activity ◽  
Phase Amplitude ◽  
Intracranial Recordings

Prior studies have shown that high-frequency activity (HFA) is modulated by the phase of low-frequency activity. This phenomenon of phase-amplitude coupling (PAC) is often interpreted as reflecting phase coding of neural representations, although evidence for this link is still lacking in humans. Here, we show that PAC indeed supports phase-dependent stimulus representations for categories. Six patients with medication-resistant epilepsy viewed images of faces, tools, houses, and scenes during simultaneous acquisition of intracranial recordings. Analyzing 167 electrodes, we observed PAC at 43% of electrodes. Further inspection of PAC revealed that category specific HFA modulations occurred at different phases and frequencies of the underlying low-frequency rhythm, permitting decoding of categorical information using the phase at which HFA events occurred. These results provide evidence for categorical phase-coded neural representations and are the first to show that PAC coincides with phase-dependent coding in the human brain.

scholarly journals Cortical ripples provide the conditions for consolidation during NREM sleep in humans

2021 ◽  
Author(s):  
Charles W Dickey ◽  
Ilya A Verzhbinsky ◽  
Xi Jiang ◽  
Burke Q Rosen ◽  
Sophie Kajfez ◽  
...  
Keyword(s):  
Memory Consolidation ◽  
High Frequency ◽  
Activity Patterns ◽  
Nrem Sleep ◽  
Spike Timing ◽  
Neuronal Firing ◽  
Human Cortex ◽  
Firing Patterns ◽  
High Frequency Activity ◽  
Intracranial Recordings

Hippocampal ripples index the reconstruction of spatiotemporal neuronal firing patterns essential for the consolidation of memories in the cortex during non-rapid eye movement (NREM) sleep. However, it is not known whether ripples are generated in the human cortex during sleep. Here, using human intracranial recordings, we show that ~70ms long ~80Hz ripples are ubiquitous in all regions of the cortex during NREM sleep as well as waking. During waking, cortical ripples occur on local high frequency activity peaks. During sleep, cortical ripples occur during spindles on the down-to-upstate transition, with unit-firing patterns consistent with generation by pyramidal-interneuron feedback. Cortical ripples mark the recurrence of spatiotemporal activity patterns from preceding waking, and they group co-firing within the window of spike-timing-dependent plasticity. Thus, cortical ripples guided by sequential sleep waves may facilitate memory consolidation during NREM sleep in humans.

scholarly journals Dissociation of broadband high-frequency activity and neuronal firing in the neocortex

2020 ◽  
Vol 6 (33) ◽  
pp. eabb0977 ◽  
Author(s):  
Marcin Leszczyński ◽  
Annamaria Barczak ◽  
Yoshinao Kajikawa ◽  
Istvan Ulbert ◽  
Arnaud Y. Falchier ◽  
...  
Keyword(s):  
High Frequency ◽  
Analytic Signal ◽  
Neuronal Firing ◽  
Neural Firing ◽  
Cortical Layers ◽  
Pial Surface ◽  
High Gamma ◽  
High Frequency Activity ◽  
Physiological Substrates ◽  
Temporal Overlap

Broadband high-frequency activity (BHA; 70 to 150 Hz), also known as “high gamma,” a key analytic signal in human intracranial (electrocorticographic) recordings, is often assumed to reflect local neural firing [multiunit activity (MUA)]. As the precise physiological substrates of BHA are unknown, this assumption remains controversial. Our analysis of laminar multielectrode data from V1 and A1 in monkeys outlines two components of stimulus-evoked BHA distributed across the cortical layers: an “early-deep” and “late-superficial” response. Early-deep BHA has a clear spatial and temporal overlap with MUA. Late-superficial BHA was more prominent and accounted for more of the BHA signal measured near the cortical pial surface. However, its association with local MUA is weak and often undetectable, consistent with the view that it reflects dendritic processes separable from local neuronal firing.

scholarly journals Synaptic and intrinsic homeostasis cooperate to optimize single neuron response properties and tune integrator circuits

2016 ◽  
Vol 116 (5) ◽  
pp. 2004-2022 ◽  
Author(s):  
Jonathan Cannon ◽  
Paul Miller
Keyword(s):  
Firing Rate ◽  
Single Neuron ◽  
Neuronal Firing ◽  
Model Parameters ◽  
Normal Brain ◽  
Synaptic Homeostasis ◽  
Neuron Response ◽  
Normal Brain Function ◽  
Long Time ◽  
Mean And Variance

Homeostatic processes that provide negative feedback to regulate neuronal firing rate are essential for normal brain function, and observations suggest that multiple such processes may operate simultaneously in the same network. We pose two questions: why might a diversity of homeostatic pathways be necessary, and how can they operate in concert without opposing and undermining each other? To address these questions, we perform a computational and analytical study of cell-intrinsic homeostasis and synaptic homeostasis in single-neuron and recurrent circuit models. We demonstrate analytically and in simulation that when two such mechanisms are controlled on a long time scale by firing rate via simple and general feedback rules, they can robustly operate in tandem to tune the mean and variance of single neuron's firing rate to desired goals. This property allows the system to recover desired behavior after chronic changes in input statistics. We illustrate the power of this homeostatic tuning scheme by using it to regain high mutual information between neuronal input and output after major changes in input statistics. We then show that such dual homeostasis can be applied to tune the behavior of a neural integrator, a system that is notoriously sensitive to variation in parameters. These results are robust to variation in goals and model parameters. We argue that a set of homeostatic processes that appear to redundantly regulate mean firing rate may work together to control firing rate mean and variance and thus maintain performance in a parameter-sensitive task such as integration.

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