Structure in Neural Activity during Observed and Executed Movements Is Shared at the Neural Population Level, Not in Single Neurons

Competing accounts propose that working memory (WM) is subserved either by persistent activity in single neurons, or by time-varying activity across a neural population, or by activity-silent mechanisms carried out by hidden internal states of the neural population. While WM is traditionally regarded to originate exclusively from neuronal interactions, cortical networks also include astrocytes that can modulate neural activity. We propose that different mechanisms of WM can be brought forth by astrocyte-mediated modulations of synaptic transmitter release. In this account, the emergence of different mechanisms depends on the network's spontaneous activity and the geometry of the connections between synapses and astrocytes.

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Behavioral Triggers of Skin Conductance Responses and Their Neural Correlates in the Primate Amygdala

Journal of Neurophysiology ◽

10.1152/jn.91110.2008 ◽

2009 ◽

Vol 101 (4) ◽

pp. 1749-1754 ◽

Cited By ~ 48

Author(s):

Christopher M. Laine ◽

Kevin M. Spitler ◽

Clayton P. Mosher ◽

Katalin M. Gothard

Keyword(s):

Skin Conductance ◽

Single Unit ◽

Population Level ◽

Emotional Reaction ◽

Neural Population ◽

Experimental Conditions ◽

Indirect Measure ◽

Skin Conductance Responses ◽

The Right ◽

Sympathetic Arousal

The amygdala plays a crucial role in evaluating the emotional significance of stimuli and in transforming the results of this evaluation into appropriate autonomic responses. Lesion and stimulation studies suggest involvement of the amygdala in the generation of the skin conductance response (SCR), which is an indirect measure of autonomic activity that has been associated with both emotion and attention. It is unclear if this involvement marks an emotional reaction to an external stimulus or sympathetic arousal regardless of its origin. We recorded skin conductance in parallel with single-unit activity from the right amygdala of two rhesus monkeys during a rewarded image viewing task and while the monkeys sat alone in a dimly lit room, drifting in and out of sleep. In both experimental conditions, we found similar SCR-related modulation of activity at the single-unit and neural population level. This suggests that the amygdala contributes to the production or modulation of SCRs regardless of the source of sympathetic arousal.

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Latent factors and dynamics in motor cortex and their application to brain-machine interfaces

10.7287/peerj.preprints.27217 ◽

2018 ◽

Author(s):

Chethan Pandarinath ◽

K. Cora Ames ◽

Abigail A Russo ◽

Ali Farshchian ◽

Lee E Miller ◽

...

Keyword(s):

Motor Cortex ◽

Movement Planning ◽

Neural Population ◽

Great Effort ◽

Single Neurons ◽

Latent Factors ◽

Brain Machine Interfaces ◽

Population Activity ◽

Systems Perspective ◽

Behaving Monkeys

In the fifty years since Evarts first recorded single neurons in motor cortex of behaving monkeys, great effort has been devoted to understanding their relation to movement. Yet these single neurons exist within a vast network, the nature of which has been largely inaccessible. With advances in recording technologies, algorithms, and computational power, the ability to study network-level phenomena is increasing exponentially. Recent experimental results suggest that the dynamical properties of these networks are critical to movement planning and execution. Here we discuss this dynamical systems perspective, and how it is reshaping our understanding of the motor cortices. Following an overview of key studies in motor cortex, we discuss techniques to uncover the “latent factors” underlying observed neural population activity. Finally, we discuss efforts to leverage these factors to improve the performance of brain-machine interfaces, promising to make these findings broadly relevant to neuroengineering as well as systems neuroscience.

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Multi-shanks SiNAPS Active Pixel Sensor CMOS probe: 1024 simultaneously recording channels for high-density intracortical brain mapping

10.1101/749911 ◽

2019 ◽

Author(s):

Fabio Boi ◽

Nikolas Perentos ◽

Aziliz Lecomte ◽

Gerrit Schwesig ◽

Stefano Zordan ◽

...

Keyword(s):

Neural Activity ◽

Brain Activity ◽

Brain Area ◽

Large Population ◽

Brain Structures ◽

High Density ◽

Single Neurons ◽

Electrode Arrays ◽

Unit Recording ◽

Recording Channels

AbstractThe advent of implantable active dense CMOS neural probes opened a new era for electrophysiology in neuroscience. These single shank electrode arrays, and the emerging tailored analysis tools, provide for the first time to neuroscientists the neurotechnology means to spatiotemporally resolve the activity of hundreds of different single-neurons in multiple vertically aligned brain structures. However, while these unprecedented experimental capabilities to study columnar brain properties are a big leap forward in neuroscience, there is the need to spatially distribute electrodes also horizontally. Closely spacing and consistently placing in well-defined geometrical arrangement multiple isolated single-shank probes is methodologically and economically impractical. Here, we present the first high-density CMOS neural probe with multiple shanks integrating thousand’s of closely spaced and simultaneously recording microelectrodes to map neural activity across 2D lattice. Taking advantage from the high-modularity of our electrode-pixels-based SiNAPS technology, we realized a four shanks active dense probe with 256 electrode-pixels/shank and a pitch of 28 µm, for a total of 1024 simultaneously recording channels. The achieved performances allow for full-band, whole-array read-outs at 25 kHz/channel, show a measured input referred noise in the action potential band (300-7000 Hz) of 6.5 ± 2.1µVRMS, and a power consumption <6 µW/electrode-pixel. Preliminary recordings in awake behaving mice demonstrated the capability of multi-shanks SiNAPS probes to simultaneously record neural activity (both LFPs and spikes) from a brain area >6 mm2, spanning cortical, hippocampal and thalamic regions. High-density 2D array enables combining large population unit recording across distributed networks with precise intra- and interlaminar/nuclear mapping of the oscillatory dynamics. These results pave the way to a new generation of high-density and extremely compact multi-shanks CMOS-probes with tunable layouts for electrophysiological mapping of brain activity at the single-neurons resolution.

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Stable task information from an unstable neural population

10.1101/796334 ◽

2019 ◽

Author(s):

Michael E. Rule ◽

Adrianna R. Loback ◽

Dhruva V. Raman ◽

Laura Driscoll ◽

Christopher D. Harvey ◽

...

Keyword(s):

Neural Activity ◽

Sensorimotor Cortex ◽

Learning Rule ◽

Neural Population ◽

Local Learning ◽

Significant Component ◽

Obvious Change ◽

Consistent Information

AbstractOver days and weeks, neural activity representing an animal’s position and movement in sensorimotor cortex has been found to continually reconfigure or ‘drift’ during repeated trials of learned tasks, with no obvious change in behavior. This challenges classical theories which assume stable engrams underlie stable behavior. However, it is not known whether this drift occurs systematically, allowing downstream circuits to extract consistent information. We show that drift is systematically constrained far above chance, facilitating a linear weighted readout of behavioural variables. However, a significant component of drift continually degrades a fixed readout, implying that drift is not confined to a null coding space. We calculate the amount of plasticity required to compensate drift independently of any learning rule, and find that this is within physiologically achievable bounds. We demonstrate that a simple, biologically plausible local learning rule can achieve these bounds, accurately decoding behavior over many days.

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Stimulus Feature-Specific Information Flow Along the Columnar Cortical Microcircuit Revealed by Multivariate Laminar Spiking Analysis

Frontiers in Systems Neuroscience ◽

10.3389/fnsys.2020.600601 ◽

2020 ◽

Vol 14 ◽

Author(s):

David A. Tovar ◽

Jacob A. Westerberg ◽

Michele A. Cox ◽

Kacie Dougherty ◽

Thomas A. Carlson ◽

...

Keyword(s):

Neural Activity ◽

Visual Stimulation ◽

Brain Activity ◽

Contextual Information ◽

Univariate Analysis ◽

Stimulus Presentation ◽

Population Level ◽

Granular Cell ◽

Stimulus Orientation ◽

Specific Information

Most of the mammalian neocortex is comprised of a highly similar anatomical structure, consisting of a granular cell layer between superficial and deep layers. Even so, different cortical areas process different information. Taken together, this suggests that cortex features a canonical functional microcircuit that supports region-specific information processing. For example, the primate primary visual cortex (V1) combines the two eyes' signals, extracts stimulus orientation, and integrates contextual information such as visual stimulation history. These processes co-occur during the same laminar stimulation sequence that is triggered by the onset of visual stimuli. Yet, we still know little regarding the laminar processing differences that are specific to each of these types of stimulus information. Univariate analysis techniques have provided great insight by examining one electrode at a time or by studying average responses across multiple electrodes. Here we focus on multivariate statistics to examine response patterns across electrodes instead. Specifically, we applied multivariate pattern analysis (MVPA) to linear multielectrode array recordings of laminar spiking responses to decode information regarding the eye-of-origin, stimulus orientation, and stimulus repetition. MVPA differs from conventional univariate approaches in that it examines patterns of neural activity across simultaneously recorded electrode sites. We were curious whether this added dimensionality could reveal neural processes on the population level that are challenging to detect when measuring brain activity without the context of neighboring recording sites. We found that eye-of-origin information was decodable for the entire duration of stimulus presentation, but diminished in the deepest layers of V1. Conversely, orientation information was transient and equally pronounced along all layers. More importantly, using time-resolved MVPA, we were able to evaluate laminar response properties beyond those yielded by univariate analyses. Specifically, we performed a time generalization analysis by training a classifier at one point of the neural response and testing its performance throughout the remaining period of stimulation. Using this technique, we demonstrate repeating (reverberating) patterns of neural activity that have not previously been observed using standard univariate approaches.

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Neural populations in human posteromedial cortex display opposing responses during memory and numerical processing

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1206580109 ◽

2012 ◽

Vol 109 (38) ◽

pp. 15514-15519 ◽

Cited By ~ 47

Author(s):

Brett L. Foster ◽

Mohammad Dastjerdi ◽

Josef Parvizi

Keyword(s):

Autobiographical Memory ◽

Default Mode Network ◽

Memory Retrieval ◽

Late Onset ◽

Population Level ◽

Neural Population ◽

Signal Trial ◽

Default Mode ◽

Numerical Processing ◽

Neuroimaging Data

Our understanding of the human default mode network derives primarily from neuroimaging data but its electrophysiological correlates remain largely unexplored. To address this limitation, we recorded intracranially from the human posteromedial cortex (PMC), a core structure of the default mode network, during various conditions of internally directed (e.g., autobiographical memory) as opposed to externally directed focus (e.g., arithmetic calculation). We observed late-onset (>400 ms) increases in broad high γ-power (70–180 Hz) within PMC subregions during memory retrieval. High γ-power was significantly reduced or absent when subjects retrieved self-referential semantic memories or responded to self-judgment statements, respectively. Conversely, a significant deactivation of high γ-power was observed during arithmetic calculation, the duration of which correlated with reaction time at the signal-trial level. Strikingly, at each recording site, the magnitude of activation during episodic autobiographical memory retrieval predicted the degree of suppression during arithmetic calculation. These findings provide important anatomical and temporal details—at the neural population level—of PMC engagement during autobiographical memory retrieval and address how the same populations are actively suppressed during tasks, such as numerical processing, which require externally directed attention.

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Comparison of Shape Encoding in Primate Dorsal and Ventral Visual Pathways

Journal of Neurophysiology ◽

10.1152/jn.00168.2006 ◽

2007 ◽

Vol 97 (1) ◽

pp. 307-319 ◽

Cited By ~ 100

Author(s):

Sidney R. Lehky ◽

Anne B. Sereno

Keyword(s):

Population Level ◽

Shape Selectivity ◽

Visual Pathways ◽

Neural Population ◽

Inferotemporal Cortex ◽

Lateral Intraparietal Cortex ◽

Dorsal Pathway ◽

Repetition Suppression ◽

Shape Encoding ◽

High Level

Ventral and dorsal visual pathways perform fundamentally different functions. The former is involved in object recognition, whereas the latter carries out spatial localization of stimuli and visual guidance of motor actions. Despite the association of the dorsal pathway with spatial vision, recent studies have reported shape selectivity in the dorsal stream. We compared shape encoding in anterior inferotemporal cortex (AIT), a high-level ventral area, with that in lateral intraparietal cortex (LIP), a high-level dorsal area, during a fixation task. We found shape selectivities of individual neurons to be greater in anterior inferotemporal cortex than in lateral intraparietal cortex. At the neural population level, responses to different shapes were more dissimilar in AIT than LIP. Both observations suggest a greater capacity in AIT for making finer shape distinctions. Multivariate analyses of AIT data grouped together similar shapes based on neural population responses, whereas such grouping was indistinct in LIP. Thus in a first comparison of shape response properties in late stages of the two visual pathways, we report that AIT exhibits greater capability than LIP for both object discrimination and generalization. These differences in the two visual pathways provide the first neurophysiological evidence that shape encoding in the dorsal pathway is distinct from and not a mere duplication of that formed in the ventral pathway. In addition to shape selectivity, we observed stimulus-driven cognitive effects in both areas. Stimulus repetition suppression in LIP was similar to the well-known repetition suppression in AIT and may be associated with the “inhibition of return” memory effect observed during reflexive attention.

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Noise-Robust Modes of the Retinal Population Code Have the Geometry of “Ridges” and Correspond to Neuronal Communities

Neural Computation ◽

10.1162/neco_a_01011 ◽

2017 ◽

Vol 29 (12) ◽

pp. 3119-3180 ◽

Cited By ~ 6

Author(s):

Adrianna Loback ◽

Jason Prentice ◽

Mark Ioffe ◽

Michael Berry II

Keyword(s):

Neural Activity ◽

Ganglion Cells ◽

Activity Patterns ◽

Local Maximum ◽

Spike Count ◽

Neural Population ◽

Local Maxima ◽

Local Probability ◽

Multiple Data Sets ◽

Noise Robust

An appealing new principle for neural population codes is that correlations among neurons organize neural activity patterns into a discrete set of clusters, which can each be viewed as a noise-robust population codeword. Previous studies assumed that these codewords corresponded geometrically with local peaks in the probability landscape of neural population responses. Here, we analyze multiple data sets of the responses of approximately 150 retinal ganglion cells and show that local probability peaks are absent under broad, nonrepeated stimulus ensembles, which are characteristic of natural behavior. However, we find that neural activity still forms noise-robust clusters in this regime, albeit clusters with a different geometry. We start by defining a soft local maximum, which is a local probability maximum when constrained to a fixed spike count. Next, we show that soft local maxima are robustly present and can, moreover, be linked across different spike count levels in the probability landscape to form a ridge. We found that these ridges comprise combinations of spiking and silence in the neural population such that all of the spiking neurons are members of the same neuronal community, a notion from network theory. We argue that a neuronal community shares many of the properties of Donald Hebb's classic cell assembly and show that a simple, biologically plausible decoding algorithm can recognize the presence of a specific neuronal community.

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