scholarly journals On the structure of population activity under fluctuations in attentional state

2015 ◽  
Author(s):  
Alexander S Ecker ◽  
George H Denfield ◽  
Matthias Bethge ◽  
Andreas S Tolias
Keyword(s):  
Simple Model ◽  
Limited Range ◽  
Neural Response ◽  
Response Modulation ◽  
Attentional State ◽  
Population Activity ◽  
Neuronal Populations ◽  
Correlated Activity ◽  
State Affect ◽  
Structure Of Population

Attention is commonly thought to improve behavioral performance by increasing response gain and suppressing shared variability in neuronal populations. However, both the focus and the strength of attention are likely to vary from one experimental trial to the next, thereby inducing response variability unknown to the experimenter. Here we study analytically how fluctuations in attentional state affect the structure of population responses in a simple model of spatial and feature attention. In our model, attention acts on the neural response exclusively by modulating each neuron's gain. Neurons are conditionally independent given the stimulus and the attentional gain, and correlated activity arises only from trial-to-trial fluctuations of the attentional state, which are unknown to the experimenter. We find that this simple model can readily explain many aspects of neural response modulation under attention, such as increased response gain, reduced individual and shared variability, increased correlations with firing rates, limited range correlations, and differential correlations. We therefore suggest that attention may act primarily by increasing response gain of individual neurons without affecting their correlation structure. The experimentally observed reduction in correlations may instead result from reduced variability of the attentional gain when a stimulus is attended. Moreover, we show that attentional gain fluctuations – even if unknown to a downstream readout – do not impair the readout accuracy despite inducing limited-range correlations.

scholarly journals Phase-Amplitude Markers of Synchrony and Noise: A Resting-State and TMS-EEG Study of Schizophrenia

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Dominik Freche ◽  
Jodie Naim-Feil ◽  
Shmuel Hess ◽  
Avraham Peled ◽  
Alexander Grinshpoon ◽  
...  
Keyword(s):  
Resting State ◽  
Narrow Band ◽  
Signal To Noise Ratio ◽  
Phase Locking ◽  
Activity Patterns ◽  
Neuronal Populations ◽  
Correlated Activity ◽  
Amplitude Fluctuations ◽  
Phase Amplitude ◽  
External Stimuli

Abstract The electroencephalogram (EEG) of schizophrenia patients is known to exhibit a reduction of signal-to-noise ratio and of phase locking, as well as a facilitation of excitability, in response to a variety of external stimuli. Here, we demonstrate these effects in transcranial magnetic stimulation (TMS)-evoked potentials and in the resting-state EEG. To ensure veracity, we used 3 weekly sessions and analyzed both resting-state and TMS-EEG data. For the TMS responses, our analysis verifies known results. For the resting state, we introduce the methodology of mean-normalized variation to the EEG analysis (quartile-based coefficient of variation), which allows for a comparison of narrow-band EEG amplitude fluctuations to narrow-band Gaussian noise. This reveals that amplitude fluctuations in the delta, alpha, and beta bands of healthy controls are different from those in schizophrenia patients, on time scales of tens of seconds. We conclude that the EEG-measured cortical activity patterns of schizophrenia patients are more similar to noise, both in alpha- and beta-resting state and in TMS responses. Our results suggest that the ability of neuronal populations to form stable, locally, and temporally correlated activity is reduced in schizophrenia, a conclusion, that is, in accord with previous experiments on TMS-EEG and on resting-state EEG.

Spatial Overlap of on and off Subregions and Its Relation to Response Modulation Ratio in Macaque Primary Visual Cortex

2005 ◽  
Vol 93 (2) ◽  
pp. 919-928 ◽  
Author(s):  
Mario L. Mata ◽  
Dario L. Ringach
Keyword(s):  
Visual Cortex ◽  
Simple Model ◽  
Primary Visual Cortex ◽  
Response Modulation ◽  
Noise Pattern ◽  
Unimodal Distribution ◽  
Spatial Overlap ◽  
The Relationship ◽  
Spatial Maps ◽  
Modulation Ratio

We studied the spatial overlap of on and off subregions in macaque primary visual cortex and its relation to the response modulation ratio (the F1/ F0 ratio). Spatial maps of on and off subregions were obtained by reverse correlation with a dynamic noise pattern of bright and dark spots. Two spatial maps, on and off, were produced by cross-correlating the spike train with the location of bright and dark spots in the stimulus respectively. Several measures were used to assess the degree of overlap between subregions. In a subset of neurons, we also computed the F1/ F0 ratio in response to drifting sinusoidal gratings. Significant correlations were found among all the overlap measures and the F1/ F0 ratio. Most overlap indices considered, and the F1/ F0 measure, had bimodal distributions. In contrast, the distance between on and off subregions normalized by their size was unimodal. Surprisingly, a simple model that additively combines on and off subregions with spatial separations drawn from a unimodal distribution, can readily explain the data. These analyses clarify the relationship between subregion overlap and the F1/ F0 ratio in macaque primary visual cortex, and a simple model provides a parsimonious explanation for the co-existence of bimodal distributions of overlap indices and a unimodal distribution of the normalized distance.

scholarly journals Microscale physiological events on the human cortical surface

2019 ◽  
Author(s):  
Angelique C. Paulk ◽  
Jimmy C. Yang ◽  
Daniel R. Cleary ◽  
Daniel J. Soper ◽  
Milan Halgren ◽  
...  
Keyword(s):  
Rhythmic Activity ◽  
Human Cognition ◽  
Discrete Events ◽  
Population Activity ◽  
Neuronal Populations ◽  
Ultrahigh Density ◽  
Human Participants ◽  
Electrical Stimuli ◽  
Aggregate Population ◽  
Somatic Action Potentials

AbstractDespite ongoing advancements in our understanding of the local single-cellular and network-level activity of neuronal populations in the human brain, extraordinarily little is known about their ‘intermediate’ microscale local circuit dynamics. Here, we utilized ultrahigh density microelectrode arrays and a rare opportunity to perform intracranial recordings across multiple cortical areas in human participants to discover three distinct classes of cortical activity that are not locked to ongoing natural brain rhythmic activity. The first included fast waveforms similar to extracellular single unit activity. The other two types were discrete events with slower waveform dynamics and were found preferentially in upper layers of the grey matter. They were also observed in rodents, non-human primates, and semi-chronic recordings in humans via laminar and Utah array microelectrodes. The rates of all three events were selectively modulated by auditory and electrical stimuli, pharmacological manipulation, and cold saline application and had small causal co-occurrences. These results suggest that with the proper combination of high resolution microelectrodes and analytic techniques it is possible to capture neuronal dynamics that lay between somatic action potentials and aggregate population activity and that understanding these intermediate microscale dynamics may reveal important details of the full circuit behavior in human cognition.

scholarly journals Primary motor cortex activity traces distinct trajectories of population dynamics during spontaneous facial motor sequences in mice

2021 ◽  
Author(s):  
Juan Carlos Boffi ◽  
Tristan Wiessalla ◽  
Robert Prevedel
Keyword(s):  
Population Dynamics ◽  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Neuronal Population ◽  
Future Research ◽  
Sequencing Analysis ◽  
Population Activity ◽  
Neuronal Populations ◽  
Custom Made ◽  
Facial Area

AbstractWe explore the link between on-going neuronal activity at primary motor cortex (M1) and face movement in awake mice. By combining custom-made behavioral sequencing analysis and fast volumetric Ca2+-imaging, we simultaneously tracked M1 population activity during many different facial motor sequences. We show that a facial area of M1 displays distinct trajectories of neuronal population dynamics across different spontaneous facial motor sequences, suggesting an underlying population dynamics code.Significance statementHow our brain controls a seemingly limitless diversity of body movements remains largely unknown. Recent research brings new light into this subject by showing that neuronal populations at the primary motor cortex display different dynamics during forelimb reaching movements versus grasping, which suggests that different motor sequences could be associated with distinct motor cortex population dynamics. To explore this possibility, we designed an experimental paradigm for simultaneously tracking the activity of neuronal populations in motor cortex across many different motor sequences. Our results support the concept that distinct population dynamics encode different motor sequences, bringing new insight into the role of motor cortex in sculpting behavior while opening new avenues for future research.

scholarly journals Conditioning sharpens the spatial representation of rewarded stimuli in mouse primary visual cortex

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Pieter M Goltstein ◽  
Guido T Meijer ◽  
Cyriel MA Pennartz
Keyword(s):  
Visual Space ◽  
Spatial Separation ◽  
Time Lapse ◽  
Optical Signal ◽  
Population Coding ◽  
Cortical Representation ◽  
Population Activity ◽  
Two Photon ◽  
Structure Of Population

Reward is often employed as reinforcement in behavioral paradigms but it is unclear how the visuospatial aspect of a stimulus-reward association affects the cortical representation of visual space. Using a head-fixed paradigm, we conditioned mice to associate the same visual pattern in adjacent retinotopic regions with availability and absence of reward. Time-lapse intrinsic optical signal imaging under anesthesia showed that conditioning increased the spatial separation of mesoscale cortical representations of reward predicting- and non-reward predicting stimuli. Subsequent in vivo two-photon calcium imaging revealed that this improved separation correlated with enhanced population coding for retinotopic location, specifically for the trained orientation and spatially confined to the V1 region where rewarded and non-rewarded stimulus representations bordered. These results are corroborated by conditioning-induced differences in the correlation structure of population activity. Thus, the cortical representation of visual space is sharpened as consequence of associative stimulus-reward learning while the overall retinotopic map remains unaltered.

scholarly journals Bidirectional synaptic plasticity rapidly modifies hippocampal representations independent of correlated activity

2020 ◽  
Author(s):  
Aaron D. Milstein ◽  
Yiding Li ◽  
Katie C. Bittner ◽  
Christine Grienberger ◽  
Ivan Soltesz ◽  
...  
Keyword(s):  
Synaptic Plasticity ◽  
Network Modeling ◽  
Place Cells ◽  
Synaptic Activity ◽  
Associative Memories ◽  
Population Activity ◽  
Correlated Activity ◽  
Dependent Model ◽  
Standard Models

AbstractAccording to standard models of synaptic plasticity, correlated activity between connected neurons drives changes in synaptic strengths to store associative memories. Here we tested this hypothesis in vivo by manipulating the activity of hippocampal place cells and measuring the resulting changes in spatial selectivity. We found that the spatial tuning of place cells was rapidly reshaped via bidirectional synaptic plasticity. To account for the magnitude and direction of plasticity, we evaluated two models – a standard model that depended on synchronous pre- and post-synaptic activity, and an alternative model that depended instead on whether active synaptic inputs had previously been potentiated. While both models accounted equally well for the data, they predicted opposite outcomes of a perturbation experiment, which ruled out the standard correlation-dependent model. Finally, network modeling suggested that this form of bidirectional synaptic plasticity enables population activity, rather than pairwise neuronal correlations, to drive plasticity in response to changes in the environment.

scholarly journals A Statistical Model of Shared Variability in the Songbird Auditory System

2017 ◽  
Author(s):  
Lars Buesing ◽  
Ana Calabrese ◽  
John P. Cunningham ◽  
Sarah M. N. Woolley ◽  
Liam Paninski
Keyword(s):  
Simple Model ◽  
Simple Structure ◽  
Vocal Communication ◽  
Factor Model ◽  
Primary Auditory Cortex ◽  
Single Neurons ◽  
Correlated Activity ◽  
Neural Populations ◽  
Cortical Regions ◽  
Simultaneous Activity

AbstractVocal communication evokes robust responses in primary auditory cortex (A1) of songbirds, and single neurons from superficial and deep regions of A1 have been shown to respond selectively to songs over complex, synthetic sounds. However, little is known about how this song selectivity arises and manifests itself on the level of networks of neurons in songbird A1. Here, we examined the network-level coding of song and synthetic sounds in A1 by simultaneously recording the responses of multiple neurons in unanesthetized zebra finches. We developed a latent factor model of the joint simultaneous activity of these neural populations, and found that the shared variability in the activity has a surprisingly simple structure; it is dominated by an unobserved latent source with one degree-of-freedom. This simple model captures the structure of the correlated activity in these populations in both spontaneous and stimulus-driven conditions, and given both song and synthetic stimuli. The inferred latent variability is strongly suppressed under stimulation, consistent with similar observations in a range of mammalian cortical regions.

scholarly journals Conserved structures of neural activity in sensorimotor cortex of freely moving rats allow cross-subject decoding

2021 ◽  
Author(s):  
Svenja Melbaum ◽  
David Eriksson ◽  
Thomas Brox ◽  
Ilka Diester
Keyword(s):  
Sensorimotor Cortex ◽  
Activity Patterns ◽  
Population Activity ◽  
Electrophysiological Recordings ◽  
Primary Sensory Cortex ◽  
Behavioral Paradigm ◽  
Structure Of Population ◽  
Freely Moving ◽  
Low Dimensional ◽  
Neuronal Code

Our knowledge about neuronal activity in the sensorimotor cortex relies primarily on stereotyped movements which are strictly controlled via the experimental settings. It remains unclear how results can be carried over to less constrained behavior, i.e. freely moving subjects. Towards this goal, we developed a self-paced behavioral paradigm which encouraged rats to conduct different types of movements. Via bilateral electrophysiological recordings across the entire sensorimotor cortex and simultaneous paw tracking, we identified behavioral coupling of neurons with lateralization and an anterior-posterior gradient from premotor to primary sensory cortex. The structure of population activity patterns was conserved across animals, in spite of severe undersampling of the total number of neurons and variations of electrode positions across individuals. Via alignments of low-dimensional neural manifolds, we demonstrate cross-subject and cross-session generalization in a decoding task arguing for a conserved neuronal code.

Minor Contribution of Principal Excitatory Pathways to Hippocampal LFPs in the Anesthetized Rat: A Combined Independent Component and Current Source Density Study

2010 ◽  
Vol 104 (1) ◽  
pp. 484-497 ◽  
Author(s):  
A. Korovaichuk ◽  
J. Makarova ◽  
V. A. Makarov ◽  
N. Benito ◽  
O. Herreras
Keyword(s):  
Time Course ◽  
Specific Activity ◽  
Current Source ◽  
Current Source Density ◽  
Independent Component ◽  
Synaptic Activity ◽  
Population Activity ◽  
Neuronal Populations ◽  
Source Density ◽  
Sparse Population

Analysis of local field potentials (LFPs) helps understand the function of the converging neuronal populations that produce the mixed synaptic activity in principal cells. Recently, using independent component analysis (ICA), we resolved ongoing hippocampal activity into several major contributions of stratified LFP-generators. Here, using pathway-specific LFP reconstruction, we isolated LFP-generators that describe the activity of Schaffer-CA1 and Perforant-Dentate excitatory inputs in the anesthetized rat. First, we applied ICA and current source density analysis to LFPs evoked by electrical subthreshold stimulation of the pathways. The results showed that pathway specific activity is selectively captured by individual components or LFP-generators. Each generator matches the known distribution of axonal terminal fields in the hippocampus and recovers the specific time course of their activation. Second, we use sparse weak electrical stimulation to prime ongoing LFPs with activity of a known origin. Decomposition of ongoing LFPs yields a few significant LFP-generators with distinct spatiotemporal characteristics for the Schaffer and Perforant inputs. Both pathways convey an irregular temporal pattern in bouts of population activity of varying amplitude. Importantly, the contribution of Schaffer and Perforant inputs to the power of raw LFPs in the hippocampus is minor (7 and 5%, respectively). The results support the hypothesis on a sparse population code used by excitatory populations in the entorhino-hippocampal system, and they validate the separation of LFP-generators as a powerful tool to explore the computational function of neuronal circuits in real time.

Coding of Sensory Signals by Neuronal Populations: The Role of Correlated Activity

2003 ◽  
Vol 9 (3) ◽  
pp. 175-180 ◽  
Author(s):  
Stefano Panzeri ◽  
Gianni Pola ◽  
Rasmus S. Petersen
Keyword(s):  
Sensory Signals ◽  
Neuronal Populations ◽  
Correlated Activity
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