scholarly journals Breakdown of spatial coding and neural synchronization in epilepsy

2018 ◽  
Author(s):  
Tristan Shuman ◽  
Daniel Aharoni ◽  
Denise J. Cai ◽  
Christopher R. Lee ◽  
Spyridon Chavlis ◽  
...  
Keyword(s):  
Dentate Gyrus ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Population Level ◽  
Place Cells ◽  
Population Coding ◽  
Spatial Processing ◽  
Spatial Representations ◽  
Spatial Coding

AbstractTemporal lobe epilepsy causes significant cognitive deficits in both human patients and rodent models, yet the specific circuit mechanisms that alter cognitive processes remain unknown. There is dramatic and selective interneuron death and axonal reorganization within the hippocampus of both humans and animal models, but the functional consequences of these changes on information processing at the neuronal population level have not been well characterized. To examine spatial representations of epileptic and control mice, we developed a novel wire-free miniature microscope to allow for unconstrained behavior during in vivo calcium imaging of neuronal activity. We found that epileptic mice running on a linear track had severely impaired spatial processing in CA1 within a single session, as place cells were less precise and less stable, and population coding was impaired. Long-term stability of place cells was also compromised as place cells in epileptic mice were highly unstable across short time intervals and completely remapped across a week. Because of the large-scale reorganization of inhibitory circuits in epilepsy, we hypothesized that degraded spatial representations were caused by dysfunctional inhibition. To test this hypothesis, we examined the temporal dynamics of hippocampal interneurons using silicon probes to simultaneously record from CA1 and dentate gyrus during head-fixed virtual navigation. We found that epileptic mice had a profound reduction in theta coherence between the dentate gyrus and CA1 regions and altered interneuron synchronization. In particular, dentate interneurons of epileptic mice had altered phase preferences to ongoing theta oscillations, which decorrelated inhibitory population firing between CA1 and dentate gyrus. To assess the specific contribution of desynchronization on spatial coding, we built a CA1 network model to simulate hippocampal desynchronization. Critically, we found that desynchronized inputs reduced the information content and stability of CA1 neurons, consistent with the experimental data. Together, these results demonstrate that temporally precise intra-hippocampal communication is critical for forming the spatial code and that desynchronized firing of hippocampal neuronal populations contributes to poor spatial processing in epileptic mice.

scholarly journals Nonlinear visuoauditory integration in the mouse superior colliculus

2021 ◽  
Author(s):  
Shinya Ito ◽  
Yufei Si ◽  
Alan M. Litke ◽  
David A. Feldheim
Keyword(s):  
Superior Colliculus ◽  
Sensory Integration ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Critical Role ◽  
Sensory Information ◽  
Population Level ◽  
Object Identification ◽  
Response Properties ◽  
The Individual

AbstractSensory information from different modalities is processed in parallel, and then integrated in associative brain areas to improve object identification and the interpretation of sensory experiences. The Superior Colliculus (SC) is a midbrain structure that plays a critical role in integrating visual, auditory, and somatosensory input to assess saliency and promote action. Although the response properties of the individual SC neurons to visuoauditory stimuli have been characterized, little is known about the spatial and temporal dynamics of the integration at the population level. Here we recorded the response properties of SC neurons to spatially restricted visual and auditory stimuli using large-scale electrophysiology. We then created a general, population-level model that explains the spatial, temporal, and intensity requirements of stimuli needed for sensory integration. We found that the mouse SC contains topographically organized visual and auditory neurons that exhibit nonlinear multisensory integration. We show that nonlinear integration depends on properties of auditory but not visual stimuli. We also find that a heuristically derived nonlinear modulation function reveals conditions required for sensory integration that are consistent with previously proposed models of sensory integration such as spatial matching and the principle of inverse effectiveness.

scholarly journals Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex

2016 ◽  
Vol 114 (2) ◽  
pp. 394-399 ◽  
Author(s):  
John D. Murray ◽  
Alberto Bernacchia ◽  
Nicholas A. Roy ◽  
Christos Constantinidis ◽  
Ranulfo Romo ◽  
...  
Keyword(s):  
Working Memory ◽  
Prefrontal Cortex ◽  
Neural Circuit ◽  
Temporal Dynamics ◽  
Population Level ◽  
Population Coding ◽  
Neural Dynamics ◽  
Stable Population ◽  
Neuron Activity ◽  
Delayed Response

Working memory (WM) is a cognitive function for temporary maintenance and manipulation of information, which requires conversion of stimulus-driven signals into internal representations that are maintained across seconds-long mnemonic delays. Within primate prefrontal cortex (PFC), a critical node of the brain’s WM network, neurons show stimulus-selective persistent activity during WM, but many of them exhibit strong temporal dynamics and heterogeneity, raising the questions of whether, and how, neuronal populations in PFC maintain stable mnemonic representations of stimuli during WM. Here we show that despite complex and heterogeneous temporal dynamics in single-neuron activity, PFC activity is endowed with a population-level coding of the mnemonic stimulus that is stable and robust throughout WM maintenance. We applied population-level analyses to hundreds of recorded single neurons from lateral PFC of monkeys performing two seminal tasks that demand parametric WM: oculomotor delayed response and vibrotactile delayed discrimination. We found that the high-dimensional state space of PFC population activity contains a low-dimensional subspace in which stimulus representations are stable across time during the cue and delay epochs, enabling robust and generalizable decoding compared with time-optimized subspaces. To explore potential mechanisms, we applied these same population-level analyses to theoretical neural circuit models of WM activity. Three previously proposed models failed to capture the key population-level features observed empirically. We propose network connectivity properties, implemented in a linear network model, which can underlie these features. This work uncovers stable population-level WM representations in PFC, despite strong temporal neural dynamics, thereby providing insights into neural circuit mechanisms supporting WM.

scholarly journals Place cell map genesis via competitive learning and conjunctive coding in the dentate gyrus

2019 ◽  
Author(s):  
Soyoun Kim ◽  
Dajung Jung ◽  
Sébastien Royer
Keyword(s):  
Dentate Gyrus ◽  
Spatial Information ◽  
Grid Cell ◽  
Place Cells ◽  
Competitive Learning ◽  
Spatial Representations ◽  
Object Locations ◽  
Network Pattern ◽  
Treadmill Belt ◽  
The Continuum

AbstractPlace cells exhibit spatially selective firing fields and collectively map the continuum of positions in environments; how such network pattern develops with experience remains unclear. Here, we recorded putative granule (GC) and mossy (MC) cells from the dentate gyrus (DG) over 27 days as mice repetitively ran through a sequence of objects fixed onto a treadmill belt. We observed a progressive transformation of GC spatial representations, from a sparse encoding of object locations and periodic spatial intervals to increasingly more single, evenly dispersed place fields, while MCs showed little transformation and preferentially encoded object locations. A competitive learning model of the DG reproduced GC transformations via the progressive integration of landmark-vector cells and grid cell inputs and required MC-mediated feedforward inhibition to evenly distribute GC representations, suggesting that GCs progressively encode conjunctions of objects and spatial information via competitive learning, while MCs help homogenize GC spatial representations.

scholarly journals A Geometric Characterization of Population Coding in the Prefrontal Cortex and Hippocampus during a Paired-Associate Learning Task

2020 ◽  
Vol 32 (8) ◽  
pp. 1455-1465
Author(s):  
Yue Liu ◽  
Scott L. Brincat ◽  
Earl K. Miller ◽  
Michael E. Hasselmo
Keyword(s):  
Prefrontal Cortex ◽  
Large Scale ◽  
Paired Associate ◽  
Population Level ◽  
Population Coding ◽  
Task Structure ◽  
Population Activity ◽  
Associate Learning ◽  
Task Variables ◽  
Monkey Prefrontal Cortex

Large-scale neuronal recording techniques have enabled discoveries of population-level mechanisms for neural computation. However, it is not clear how these mechanisms form by trial-and-error learning. In this article, we present an initial effort to characterize the population activity in monkey prefrontal cortex (PFC) and hippocampus (HPC) during the learning phase of a paired-associate task. To analyze the population data, we introduce the normalized distance, a dimensionless metric that describes the encoding of cognitive variables from the geometrical relationship among neural trajectories in state space. It is found that PFC exhibits a more sustained encoding of the visual stimuli, whereas HPC only transiently encodes the identity of the associate stimuli. Surprisingly, after learning, the neural activity is not reorganized to reflect the task structure, raising the possibility that learning is accompanied by some “silent” mechanism that does not explicitly change the neural representations. We did find partial evidence on the learning-dependent changes for some of the task variables. This study shows the feasibility of using normalized distance as a metric to characterize and compare population-level encoding of task variables and suggests further directions to explore learning-dependent changes in the neural circuits.

scholarly journals Nonlinear visuoauditory integration in the mouse superior colliculus

2021 ◽  
Vol 17 (11) ◽  
pp. e1009181
Author(s):  
Shinya Ito ◽  
Yufei Si ◽  
Alan M. Litke ◽  
David A. Feldheim
Keyword(s):  
Superior Colliculus ◽  
Sensory Integration ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Critical Role ◽  
Sensory Information ◽  
Population Level ◽  
Object Identification ◽  
Response Properties ◽  
The Individual

Sensory information from different modalities is processed in parallel, and then integrated in associative brain areas to improve object identification and the interpretation of sensory experiences. The Superior Colliculus (SC) is a midbrain structure that plays a critical role in integrating visual, auditory, and somatosensory input to assess saliency and promote action. Although the response properties of the individual SC neurons to visuoauditory stimuli have been characterized, little is known about the spatial and temporal dynamics of the integration at the population level. Here we recorded the response properties of SC neurons to spatially restricted visual and auditory stimuli using large-scale electrophysiology. We then created a general, population-level model that explains the spatial, temporal, and intensity requirements of stimuli needed for sensory integration. We found that the mouse SC contains topographically organized visual and auditory neurons that exhibit nonlinear multisensory integration. We show that nonlinear integration depends on properties of auditory but not visual stimuli. We also find that a heuristically derived nonlinear modulation function reveals conditions required for sensory integration that are consistent with previously proposed models of sensory integration such as spatial matching and the principle of inverse effectiveness.

scholarly journals Place cell maps slowly develop via competitive learning and conjunctive coding in the dentate gyrus

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Soyoun Kim ◽  
Dajung Jung ◽  
Sébastien Royer
Keyword(s):  
Dentate Gyrus ◽  
Spatial Information ◽  
Granule Cells ◽  
Place Cells ◽  
Competitive Learning ◽  
Spatial Representations ◽  
Mossy Cells ◽  
Object Locations ◽  
Treadmill Belt ◽  
The Continuum

Abstract Place cells exhibit spatially selective firing fields that collectively map the continuum of positions in environments; how such activity pattern develops with experience is largely unknown. Here, we record putative granule cells (GCs) and mossy cells (MCs) from the dentate gyrus (DG) over 27 days as mice repetitively run through a sequence of objects fixed onto a treadmill belt. We observe a progressive transformation of GC spatial representations, from a sparse encoding of object locations and spatial patterns to increasingly more single, evenly dispersed place fields, while MCs show little transformation and preferentially encode object locations. A competitive learning model of the DG reproduces GC transformations via the progressive integration of landmark-vector cells and spatial inputs and requires MC-mediated feedforward inhibition to evenly distribute GC representations, suggesting that GCs slowly encode conjunctions of objects and spatial information via competitive learning, while MCs help homogenize GC spatial representations.

scholarly journals Toward an Aggregate, Implicit, and Dynamic Model of Norm Formation: Capturing Large-Scale Media Representations of Dynamic Descriptive Norms Through Automated and Crowdsourced Content Analysis

2019 ◽  
Vol 69 (6) ◽  
pp. 563-588 ◽  
Author(s):  
Jiaying Liu ◽  
Leeann Siegel ◽  
Laura A Gibson ◽  
Yoonsang Kim ◽  
Steven Binns ◽  
...  
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Population Level ◽  
Media Representations ◽  
Supervised Machine Learning ◽  
Media Content ◽  
Population Norms ◽  
Long Form ◽  
Incidental Exposure ◽  
Individual Use

Abstract Media content can shape people’s descriptive norm perceptions by presenting either population-level prevalence information or descriptions of individuals’ behaviors. Supervised machine learning and crowdsourcing can be combined to answer new, theoretical questions about the ways in which normative perceptions form and evolve through repeated, incidental exposure to normative mentions emanating from the media environment. Applying these methods, this study describes tobacco and e-cigarette norm prevalence and trends over 37 months through an examination of a census of 135,764 long-form media texts, 12,262 popular YouTube videos, and 75,322,911 tweets. Long-form texts mentioned tobacco population norms (4–5%) proportionately less often than e-cigarette population norms (20%). Individual use norms were common across sources, particularly YouTube (tobacco long-form: 34%; Twitter: 33%; YouTube: 88%; e-cigarette long form: 17%; Twitter: 16%; YouTube: 96%). The capacity to capture aggregated prevalence and temporal dynamics of normative media content permits asking population-level media effects questions that would otherwise be infeasible to address.

scholarly journals A geometric characterization of population coding in the prefrontal cortex and hippocampus during a paired-associate learning task

2019 ◽  
Author(s):  
Yue Liu ◽  
Scott L Brincat ◽  
Earl K Miller ◽  
Michael E Hasselmo
Keyword(s):  
Prefrontal Cortex ◽  
Large Scale ◽  
Paired Associate ◽  
Population Data ◽  
Population Level ◽  
Population Coding ◽  
Population Activity ◽  
Associate Learning ◽  
Task Variables ◽  
Monkey Prefrontal Cortex

Large-scale neuronal recording techniques have enabled discoveries of population-level mechanisms for neural computation. However it is not clear how these mechanisms form by trial and error learning. In this paper we present an initial effort to characterize the population activity in monkey prefrontal cortex (PFC) and hippocampus (HPC) during the learning phase of a paired-associate task. To analyze the population data, we introduce the normalized distance, a dimensionless metric that describes the encoding of cognitive variables from the geometrical relationship among neural trajectories in state space. It is found that PFC exhibits a more sustained encoding of task-relevant variables whereas HPC only transiently encodes the identity of the stimuli. We also found partial evidence on the learning-dependent changes for some of the task variables. This study shows the feasibility of using normalized distance as a metric to characterize and compare population level encoding of task variables, and suggests further directions to explore the learning-dependent changes in the population activity.

scholarly journals E-Cannula reveals anatomical diversity in sharp-wave ripples as a driver for the recruitment of distinct hippocampal assemblies

2021 ◽  
Author(s):  
Xin Liu ◽  
Satoshi Terada ◽  
Jeonghoon Kim ◽  
Yichen Lu ◽  
Mehrdad Ramezani ◽  
...  
Keyword(s):  
Large Scale ◽  
Critical Role ◽  
Population Level ◽  
Technical Difficulty ◽  
Brain Regions ◽  
Cell Assemblies ◽  
Sharp Wave ◽  
Wave Patterns ◽  
Scale Population

The hippocampus plays a critical role in spatial navigation and episodic memory. However, research on in vivo hippocampal activity dynamics has mostly relied on single modalities such as electrical recordings or optical imaging, with respectively limited spatial and temporal resolution. This technical difficulty greatly impedes multi-level investigations into network state-related changes in cellular activity. To overcome these limitations, we developed the E-Cannula integrating fully transparent graphene microelectrodes with imaging-cannula. The E-Cannula enables the simultaneous electrical recording and two-photon calcium imaging from the exact same population of neurons across an anatomically extended region of the mouse hippocampal CA1 stably across several days. These large-scale simultaneous optical and electrical recordings showed that local hippocampal sharp wave ripples (SWRs) are associated with synchronous calcium events involving large neural populations in CA1. We show that SWRs exhibit spatiotemporal wave patterns along multiple axes in 2D space with different spatial extents (local or global) and temporal propagation modes (stationary or travelling). Notably, distinct SWR wave patterns were associated with, and decoded from, the selective recruitment of orthogonal CA1 cell assemblies. These results suggest that the diversity in the anatomical progression of SWRs may serve as a mechanism for the selective activation of the unique hippocampal cell assemblies extensively implicated in the encoding of distinct memories. Through these results we demonstrate the utility of the E-Cannula as a versatile neurotechnology with the potential for future integration with other optical components such as green lenses, fibers or prisms enabling the multi-modal investigation of cross-time scale population-level neural dynamics across brain regions.

scholarly journals Theory of Neuronal Perturbome: Linking Connectivity to Coding via Perturbations

2020 ◽  
Author(s):  
Sadra Sadeh ◽  
Claudia Clopath
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Neuronal Networks ◽  
Single Neuron ◽  
Population Level ◽  
Prima Facie ◽  
The Road ◽  
Experimental Findings ◽  
Mouse Visual Cortex

SummaryTo unravel the functional properties of the brain, we need to untangle how neurons interact with each other and coordinate in large-scale recurrent networks. One way to address this question is to measure the functional influence of individual neurons on each other by perturbing them in vivo. Application of such single-neuron perturbations in mouse visual cortex has recently revealed feature-specific suppression between excitatory neurons, despite the presence of highly specific excitatory connectivity, which was deemed to underlie feature-specific amplification. Here, we studied which connectivity profiles are consistent with these seemingly contradictory observations, by modelling the effect of single-neuron perturbations in large-scale neuronal networks. Our numerical simulations and mathematical analysis revealed that, contrary to the prima facie assumption, neither inhibition-dominance nor broad inhibition alone were sufficient to explain the experimental findings; instead, strong and functionally specific excitatory-inhibitory connectivity was necessary, consistent with recent findings in the primary visual cortex of rodents. Such networks had a higher capacity to encode and decode natural images in turn, which was accompanied by the emergence of response gain nonlinearities at the population level. Our study provides a general computational framework to investigate how single-neuron perturbations are linked to cortical connectivity and sensory coding, and paves the road to map the perturbome of neuronal networks in future studies.

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