Faculty Opinions recommendation of Columnar interactions determine horizontal propagation of recurrent network activity in neocortex.

AbstractComplex patterns of neural activity appear during up-states in the neocortex and sharp waves in the hippocampus, including sequences that resemble those during prior behavioral experience. The mechanisms underlying this replay are not well understood. How can small synaptic footprints engraved by experience control large-scale network activity during memory retrieval and consolidation? We hypothesize that sparse and weak synaptic connectivity between Hebbian assemblies are boosted by pre-existing recurrent connectivity within them. To investigate this idea, we connect sequences of assemblies in randomly connected spiking neuronal networks with a balance of excitation and inhibition. Simulations and analytical calculations show that recurrent connections within assemblies allow for a fast amplification of signals that indeed reduces the required number of inter-assembly connections. Replay can be evoked by small sensory-like cues or emerge spontaneously by activity fluctuations. Global—potentially neuromodulatory—alterations of neuronal excitability can switch between network states that favor retrieval and consolidation.Author SummarySynaptic plasticity is the basis for learning and memory, and many experiments indicate that memories are imprinted in synaptic connections. However, basic mechanisms of how such memories are retrieved and consolidated remain unclear. In particular, how can one-shot learning of a sequence of events achieve a sufficiently strong synaptic footprint to retrieve or replay this sequence? Using both numerical simulations of spiking neural networks and an analytic approach, we provide a biologically plausible model for understanding how minute synaptic changes in a recurrent network can nevertheless be retrieved by small cues or even manifest themselves as activity patterns that emerge spontaneously. We show how the retrieval of exceedingly small changes in the connections across assemblies is robustly facilitated by recurrent connectivity within assemblies. This interaction between recurrent amplification within an assembly and the feed-forward propagation of activity across the network establishes a basis for the retrieval of memories.

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Sustained Activation of PV+ Interneurons in Core Auditory Cortex Enables Robust Divisive Gain Control for Complex and Naturalistic Stimuli

10.1101/832642 ◽

2019 ◽

Author(s):

Tina Gothner ◽

Pedro J. Gonçalves ◽

Maneesh Sahani ◽

Jennifer F. Linden ◽

K. Jannis Hildebrandt

Keyword(s):

Auditory Cortex ◽

Gain Control ◽

Recurrent Network ◽

Cortical Inhibition ◽

Network Activity ◽

Inhibitory Interneurons ◽

Simple Complex ◽

Internal States ◽

Sensory Cortices ◽

Sustained Activation

ABSTRACTSensory cortices must flexibly adapt their operations to internal states and external requirements. Modulation of specific inhibitory interneurons may provide a network-level mechanism for adjustments on behaviourally relevant timescales. Understanding of the computational roles of such modulation has mostly been restricted to phasic optogenetic activation and short, transient stimuli. Here, we aimed to extend the understanding of modulation of cortical inhibition by using sustained, network-wide optogenetic activation of parvalbumin-positive interneurons in core auditory cortex to study modulation of responses to transient, sustained, and naturalistic stimuli. We found highly conserved spectral and temporal tuning, despite profoundly reduced overall network activity. This reduction was predominantly divisive, and consistent across simple, complex, and naturalistic stimuli. A recurrent network model with power-law input-output functions replicated our results. We conclude that modulation of parvalbumin-positive interneurons on timescales typical of more sustained neuromodulation may provide a means for robust divisive gain control conserving stimulus representations.

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Random recurrent networks near criticality capture the broadband power distribution of human ECoG dynamics

10.1101/036228 ◽

2016 ◽

Cited By ~ 3

Author(s):

Rishidev Chaudhuri ◽

Biyu He ◽

Xiao-Jing Wang

Keyword(s):

Power Spectrum ◽

Power Distribution ◽

Random Network ◽

Field Potential ◽

Power Spectra ◽

Low Frequency ◽

Recurrent Network ◽

Network Activity ◽

Brain Diseases ◽

Low Frequency Power

The power spectrum of brain electric field potential recordings is dominated by an arrhythmic broadband signal but a mechanistic account of its underlying neural network dynamics is lacking. Here we show how the broadband power spectrum of field potential recordings can be explained by a simple random network of nodes near criticality. Such a recurrent network produces activity with a combination of a fast and a slow autocorrelation time constant, with the fast mode corresponding to local dynamics and the slow mode resulting from recurrent excitatory connections across the network. These modes are combined to produce a power spectrum similar to that observed in human intracranial EEG (i.e., electrocorticography, ECoG) recordings. Moreover, such a network naturally converts input correlations across nodes into temporal autocorrelation of the network activity. Consequently, increased independence between nodes results in a reduction in low-frequency power, which offers a possible explanation for observed changes in ECoG power spectra during task performance. Lastly, changes in network coupling produce changes in network activity power spectra reminiscent of those seen in human ECoG recordings across different arousal states. This model thus links macroscopic features of the empirical ECoG power spectrum to a parsimonious underlying network structure and proposes potential mechanisms for changes in ECoG power spectra observed across behavioral and arousal states. This provides a computational framework within which to generate and test hypotheses about the cellular and network mechanisms underlying whole brain electrical dynamics, their variations across behavioral states as well as abnormalities associated with brain diseases.

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Searching for Principles of Brain Computation

10.1101/094102 ◽

2016 ◽

Author(s):

Wolfgang Maass

Keyword(s):

Dynamic Properties ◽

Activity Patterns ◽

Recurrent Network ◽

Network Activity ◽

Multielectrode Arrays ◽

Short Article ◽

Large Numbers ◽

Rapid Pace ◽

Network Plasticity ◽

Spatio Temporal

Experimental methods in neuroscience, such as calcium-imaging and recordings with multielectrode arrays, are advancing at a rapid pace. They produce insight into the simultaneous activity of large numbers of neurons, and into plasticity processes in the brains of awake and behaving animals. These new data constrain models for neural computation and network plasticity that underlie perception, cognition, behavior, and learning. I will discuss in this short article four such constraints: Inherent recurrent network activity and heterogeneous dynamic properties of neurons and synapses, stereotypical spatio-temporal activity patterns in networks of neurons, high trial-to-trial variability of network responses, and functional stability in spite of permanently ongoing changes in the network. I am proposing that these constraints provide hints to underlying principles of brain computation and learning.

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Cortical microcircuit determination through global perturbation and sparse sampling in grid cells

10.1101/019224 ◽

2015 ◽

Cited By ~ 3

Author(s):

John Widloski ◽

Ila R Fiete

Keyword(s):

Grid Cell ◽

Recurrent Network ◽

Cell System ◽

Network Activity ◽

Orientation Tuning ◽

Connectivity Matrix ◽

Grid Cells ◽

Neural Recordings ◽

Global Circuit ◽

Full Knowledge

Under modern interrogation, famously well-studied neural circuits such as that for orientation tuning in V1 are steadily giving up their secrets, but quite basic questions about connectivity and dynamics, including whether most computation is done by lateral processing or by selective feedforward summation, remain unresolved. We show here that grid cells offer a particularly rich opportunity for dissecting the mechanistic underpinnings of a cortical circuit, through a strategy based on global circuit perturbation combined with sparse neural recordings. The strategy is based on the theoretical insight that small perturbations of circuit activity will result in characteristic quantal shifts in the spatial tuning relationships between grid cells, which should be observable from multi-single unit recordings of a small subsample of the population. The predicted shifts differ qualitatively across candidate recurrent network mechanisms, and also distinguish between recurrent versus feedforward mechanisms. More generally, the proposed strategy demonstrates how sparse neu- ral recordings coupled with global perturbation in the grid cell system can reveal much more about circuit mechanism as it relates to function than can full knowledge of network activity or of the synaptic connectivity matrix.

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Arousal regulates frequency tuning in primary auditory cortex

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1911383116 ◽

2019 ◽

Vol 116 (50) ◽

pp. 25304-25310 ◽

Cited By ~ 7

Author(s):

Pei-Ann Lin ◽

Samuel K. Asinof ◽

Nicholas J. Edwards ◽

Jeffry S. Isaacson

Keyword(s):

Auditory Cortex ◽

Frequency Tuning ◽

Primary Auditory Cortex ◽

Pyramidal Cells ◽

Frequency Discrimination ◽

Recurrent Network ◽

Excitatory Input ◽

Network Activity ◽

Shape Tuning

Changes in arousal influence cortical sensory representations, but the synaptic mechanisms underlying arousal-dependent modulation of cortical processing are unclear. Here, we use 2-photon Ca2+ imaging in the auditory cortex of awake mice to show that heightened arousal, as indexed by pupil diameter, broadens frequency-tuned activity of layer 2/3 (L2/3) pyramidal cells. Sensory representations are less sparse, and the tuning of nearby cells more similar when arousal increases. Despite the reduction in selectivity, frequency discrimination by cell ensembles improves due to a decrease in shared trial-to-trial variability. In vivo whole-cell recordings reveal that mechanisms contributing to the effects of arousal on sensory representations include state-dependent modulation of membrane potential dynamics, spontaneous firing, and tone-evoked synaptic potentials. Surprisingly, changes in short-latency tone-evoked excitatory input cannot explain the effects of arousal on the broadness of frequency-tuned output. However, we show that arousal strongly modulates a slow tone-evoked suppression of recurrent excitation underlying lateral inhibition [H. K. Kato, S. K. Asinof, J. S. Isaacson, Neuron, 95, 412–423, (2017)]. This arousal-dependent “network suppression” gates the duration of tone-evoked responses and regulates the broadness of frequency tuning. Thus, arousal can shape tuning via modulation of indirect changes in recurrent network activity.

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A population of gap junction-coupled neurons drives recurrent network activity in a developing visual circuit

Journal of Neurophysiology ◽

10.1152/jn.01046.2015 ◽

2016 ◽

Vol 115 (3) ◽

pp. 1477-1486 ◽

Cited By ~ 7

Author(s):

Zhenyu Liu ◽

Christopher M. Ciarleglio ◽

Ali S. Hamodi ◽

Carlos D. Aizenman ◽

Kara G. Pratt

Keyword(s):

Temporal Processing ◽

Optic Tectum ◽

Deep Layer ◽

Recurrent Network ◽

Network Activity ◽

Distinct Population ◽

Vertebrate Brain ◽

Cell Recording ◽

Recurrent Activity ◽

Afferent Inputs

In many regions of the vertebrate brain, microcircuits generate local recurrent activity that aids in the processing and encoding of incoming afferent inputs. Local recurrent activity can amplify, filter, and temporally and spatially parse out incoming input. Determining how these microcircuits function is of great interest because it provides glimpses into fundamental processes underlying brain computation. Within the Xenopus tadpole optic tectum, deep layer neurons display robust recurrent activity. Although the development and plasticity of this local recurrent activity has been well described, the underlying microcircuitry is not well understood. Here, using a whole brain preparation that allows for whole cell recording from neurons of the superficial tectal layers, we identified a physiologically distinct population of excitatory neurons that are gap junctionally coupled and through this coupling gate local recurrent network activity. Our findings provide a novel role for neuronal coupling among excitatory interneurons in the temporal processing of visual stimuli.

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