scholarly journals Acetylcholine-gated current translates wake neuronal firing rate information into a spike timing-based code in Non-REM sleep, stabilizing neural network dynamics during memory consolidation

2021 ◽  
Vol 17 (9) ◽  
pp. e1009424
Author(s):  
Quinton M. Skilling ◽  
Bolaji Eniwaye ◽  
Brittany C. Clawson ◽  
James Shaver ◽  
Nicolette Ognjanovski ◽  
...  
Keyword(s):  
Firing Rate ◽  
Memory Consolidation ◽  
Neuronal Excitability ◽  
Network Models ◽  
Nrem Sleep ◽  
Spike Timing ◽  
Neuronal Firing ◽  
Information Encoding ◽  
Firing Rates

Sleep is critical for memory consolidation, although the exact mechanisms mediating this process are unknown. Combining reduced network models and analysis of in vivo recordings, we tested the hypothesis that neuromodulatory changes in acetylcholine (ACh) levels during non-rapid eye movement (NREM) sleep mediate stabilization of network-wide firing patterns, with temporal order of neurons’ firing dependent on their mean firing rate during wake. In both reduced models and in vivo recordings from mouse hippocampus, we find that the relative order of firing among neurons during NREM sleep reflects their relative firing rates during prior wake. Our modeling results show that this remapping of wake-associated, firing frequency-based representations is based on NREM-associated changes in neuronal excitability mediated by ACh-gated potassium current. We also show that learning-dependent reordering of sequential firing during NREM sleep, together with spike timing-dependent plasticity (STDP), reconfigures neuronal firing rates across the network. This rescaling of firing rates has been reported in multiple brain circuits across periods of sleep. Our model and experimental data both suggest that this effect is amplified in neural circuits following learning. Together our data suggest that sleep may bias neural networks from firing rate-based towards phase-based information encoding to consolidate memories.

scholarly journals Non-REM sleep facilitates consolidation of contextual fear memory through temporal coding among hippocampal neurons

2020 ◽  
Author(s):  
Quinton M. Skilling ◽  
Brittany C. Clawson ◽  
Bolaji Eniwaye ◽  
James Shaver ◽  
Nicolette Ognjanovski ◽  
...  
Keyword(s):  
Memory Consolidation ◽  
Hippocampal Neurons ◽  
Critical Role ◽  
Nrem Sleep ◽  
Spike Timing ◽  
Temporal Coding ◽  
Network Activity ◽  
Prior Learning ◽  
Information Encoding

SummarySleep plays a critical role in memory consolidation, although the exact mechanisms mediating this process are unknown. Combining computational and in vivo experimental approaches, we test the hypothesis that reduced cholinergic input to the hippocampus during non-rapid eye movement (NREM) sleep generates stable spike timing relationships between neurons. We find that the order of firing among neurons during a period of NREM sleep reflects their relative firing rates during prior wake, and changes as a function of prior learning. We show that learning-dependent pattern formation (e.g. “replay”) in the hippocampus during NREM, together with spike timing dependent plasticity (STDP), restructures network activity in a manner similar to that observed in brain circuits across periods of sleep. This suggests that sleep actively promotes memory consolidation by switching the network from rate-based to firing phase-based information encoding.

scholarly journals A deep convolutional visual encoding model of neuronal responses in the LGN

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Eslam Mounier ◽  
Bassem Abdullah ◽  
Hani Mahdi ◽  
Seif Eldawlatly
Keyword(s):  
Firing Rate ◽  
Visual Information ◽  
Visual Stimulation ◽  
Neuronal Population ◽  
Neuronal Firing ◽  
Electrode Arrays ◽  
Deep Convolutional Neural Networks ◽  
Firing Rates ◽  
Spatiotemporal Representation

AbstractThe Lateral Geniculate Nucleus (LGN) represents one of the major processing sites along the visual pathway. Despite its crucial role in processing visual information and its utility as one target for recently developed visual prostheses, it is much less studied compared to the retina and the visual cortex. In this paper, we introduce a deep learning encoder to predict LGN neuronal firing in response to different visual stimulation patterns. The encoder comprises a deep Convolutional Neural Network (CNN) that incorporates visual stimulus spatiotemporal representation in addition to LGN neuronal firing history to predict the response of LGN neurons. Extracellular activity was recorded in vivo using multi-electrode arrays from single units in the LGN in 12 anesthetized rats with a total neuronal population of 150 units. Neural activity was recorded in response to single-pixel, checkerboard and geometrical shapes visual stimulation patterns. Extracted firing rates and the corresponding stimulation patterns were used to train the model. The performance of the model was assessed using different testing data sets and different firing rate windows. An overall mean correlation coefficient between the actual and the predicted firing rates of 0.57 and 0.7 was achieved for the 10 ms and the 50 ms firing rate windows, respectively. Results demonstrate that the model is robust to variability in the spatiotemporal properties of the recorded neurons outperforming other examined models including the state-of-the-art Generalized Linear Model (GLM). The results indicate the potential of deep convolutional neural networks as viable models of LGN firing.

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 Sparse bursts optimize information transmission in a multiplexed neural code

2018 ◽  
Vol 115 (27) ◽  
pp. E6329-E6338 ◽  
Author(s):  
Richard Naud ◽  
Henning Sprekeler
Keyword(s):  
Firing Rate ◽  
Cortical Neurons ◽  
Neural Code ◽  
Spike Timing ◽  
Neural Ensemble ◽  
Multiple Input ◽  
Cortical Hierarchy ◽  
Connectivity Patterns ◽  
Rate Coding

Many cortical neurons combine the information ascending and descending the cortical hierarchy. In the classical view, this information is combined nonlinearly to give rise to a single firing-rate output, which collapses all input streams into one. We analyze the extent to which neurons can simultaneously represent multiple input streams by using a code that distinguishes spike timing patterns at the level of a neural ensemble. Using computational simulations constrained by experimental data, we show that cortical neurons are well suited to generate such multiplexing. Interestingly, this neural code maximizes information for short and sparse bursts, a regime consistent with in vivo recordings. Neurons can also demultiplex this information, using specific connectivity patterns. The anatomy of the adult mammalian cortex suggests that these connectivity patterns are used by the nervous system to maintain sparse bursting and optimal multiplexing. Contrary to firing-rate coding, our findings indicate that the physiology and anatomy of the cortex may be interpreted as optimizing the transmission of multiple independent signals to different targets.

scholarly journals Neuronal spike-rate adaptation supports working memory in language processing

2020 ◽  
Vol 117 (34) ◽  
pp. 20881-20889 ◽  
Author(s):  
Hartmut Fitz ◽  
Marvin Uhlmann ◽  
Dick van den Broek ◽  
Renato Duarte ◽  
Peter Hagoort ◽  
...  
Keyword(s):  
Working Memory ◽  
Language Processing ◽  
Neuronal Excitability ◽  
Memory Span ◽  
Temporal Integration ◽  
Serial Order ◽  
Neuronal Firing ◽  
Dynamic Memory ◽  
Memory Mechanism ◽  
Firing Rates

Language processing involves the ability to store and integrate pieces of information in working memory over short periods of time. According to the dominant view, information is maintained through sustained, elevated neural activity. Other work has argued that short-term synaptic facilitation can serve as a substrate of memory. Here we propose an account where memory is supported by intrinsic plasticity that downregulates neuronal firing rates. Single neuron responses are dependent on experience, and we show through simulations that these adaptive changes in excitability provide memory on timescales ranging from milliseconds to seconds. On this account, spiking activity writes information into coupled dynamic variables that control adaptation and move at slower timescales than the membrane potential. From these variables, information is continuously read back into the active membrane state for processing. This neuronal memory mechanism does not rely on persistent activity, excitatory feedback, or synaptic plasticity for storage. Instead, information is maintained in adaptive conductances that reduce firing rates and can be accessed directly without cued retrieval. Memory span is systematically related to both the time constant of adaptation and baseline levels of neuronal excitability. Interference effects within memory arise when adaptation is long lasting. We demonstrate that this mechanism is sensitive to context and serial order which makes it suitable for temporal integration in sequence processing within the language domain. We also show that it enables the binding of linguistic features over time within dynamic memory registers. This work provides a step toward a computational neurobiology of language.

scholarly journals Resonance with subthreshold oscillatory drive organizes activity and optimizes learning in neural networks

2018 ◽  
Vol 115 (13) ◽  
pp. E3017-E3025 ◽  
Author(s):  
James P. Roach ◽  
Aleksandra Pidde ◽  
Eitan Katz ◽  
Jiaxing Wu ◽  
Nicolette Ognjanovski ◽  
...  
Keyword(s):  
Hippocampal Neurons ◽  
External Input ◽  
Neuronal Firing ◽  
Network Activity ◽  
Dependent Manner ◽  
Information Encoding ◽  
Subthreshold Oscillations ◽  
Neuronal Network Model ◽  
And Performance

Network oscillations across and within brain areas are critical for learning and performance of memory tasks. While a large amount of work has focused on the generation of neural oscillations, their effect on neuronal populations’ spiking activity and information encoding is less known. Here, we use computational modeling to demonstrate that a shift in resonance responses can interact with oscillating input to ensure that networks of neurons properly encode new information represented in external inputs to the weights of recurrent synaptic connections. Using a neuronal network model, we find that due to an input current-dependent shift in their resonance response, individual neurons in a network will arrange their phases of firing to represent varying strengths of their respective inputs. As networks encode information, neurons fire more synchronously, and this effect limits the extent to which further “learning” (in the form of changes in synaptic strength) can occur. We also demonstrate that sequential patterns of neuronal firing can be accurately stored in the network; these sequences are later reproduced without external input (in the context of subthreshold oscillations) in both the forward and reverse directions (as has been observed following learning in vivo). To test whether a similar mechanism could act in vivo, we show that periodic stimulation of hippocampal neurons coordinates network activity and functional connectivity in a frequency-dependent manner. We conclude that resonance with subthreshold oscillations provides a plausible network-level mechanism to accurately encode and retrieve information without overstrengthening connections between neurons.

Internal Pallidal Neuronal Activity During Mild Drug-Related Dyskinesias in Parkinson's Disease: Decreased Firing Rates and Altered Firing Patterns

2007 ◽  
Vol 97 (4) ◽  
pp. 2627-2641 ◽  
Author(s):  
J. I. Lee ◽  
L. Verhagen Metman ◽  
S. Ohara ◽  
P. M. Dougherty ◽  
J. H. Kim ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Firing Rate ◽  
Spike Trains ◽  
Neuronal Firing ◽  
Low Doses ◽  
Significant Group ◽  
Firing Patterns ◽  
Firing Rates ◽  
Before And After

The neuronal basis of hyperkinetic movement disorders has long been unclear. We now test the hypothesis that changes in the firing pattern of neurons in the globus pallidus internus (GPi) are related to dyskinesias induced by low doses of apomorphine in patients with advanced Parkinson's disease (PD). During pallidotomy for advanced PD, the activity of single neurons was studied both before and after administration of apomorphine at doses just adequate to induce dyskinesias (21 neurons, 17 patients). After the apomorphine injection, these spike trains demonstrated an initial fall in firing from baseline. In nine neurons, the onset of on was simultaneous with that of dyskinesias. In these spike trains, the initial fall in firing rate preceded and was larger than the fall at the onset of on with dyskinesias. Among the three neurons in which the onset of on occurred before that of dyskinesias, the firing rate did not change at the time of onset of dyskinesias. After injection of apomorphine, dyskinesias during on with dyskinesias often fluctuated between transient periods with dyskinesias and those without. Average firing rates were not different between these two types of transient periods. Transient periods with dyskinesias were characterized by interspike interval (ISI) independence, stationary spike trains, and higher variability of ISIs. A small but significant group of neurons demonstrated recurring ISI patterns during transient periods of on with dyskinesias. These results suggest that mild dyskinesias resulting from low doses of apomorphine are related to both low GPi neuronal firing rates and altered firing patterns.

scholarly journals Reconciling the STDP and BCM Models of Synaptic Plasticity in a Spiking Recurrent Neural Network

2010 ◽  
Vol 22 (8) ◽  
pp. 2059-2085 ◽  
Author(s):  
Daniel Bush ◽  
Andrew Philippides ◽  
Phil Husbands ◽  
Michael O'Shea
Keyword(s):  
Neural Network ◽  
Synaptic Plasticity ◽  
Recurrent Neural Network ◽  
Hebbian Learning ◽  
Spike Timing ◽  
Synaptic Competition ◽  
Firing Rates ◽  
Plasticity Rule ◽  
Asymmetric Learning

Rate-coded Hebbian learning, as characterized by the BCM formulation, is an established computational model of synaptic plasticity. Recently it has been demonstrated that changes in the strength of synapses in vivo can also depend explicitly on the relative timing of pre- and postsynaptic firing. Computational modeling of this spike-timing-dependent plasticity (STDP) has demonstrated that it can provide inherent stability or competition based on local synaptic variables. However, it has also been demonstrated that these properties rely on synaptic weights being either depressed or unchanged by an increase in mean stochastic firing rates, which directly contradicts empirical data. Several analytical studies have addressed this apparent dichotomy and identified conditions under which distinct and disparate STDP rules can be reconciled with rate-coded Hebbian learning. The aim of this research is to verify, unify, and expand on these previous findings by manipulating each element of a standard computational STDP model in turn. This allows us to identify the conditions under which this plasticity rule can replicate experimental data obtained using both rate and temporal stimulation protocols in a spiking recurrent neural network. Our results describe how the relative scale of mean synaptic weights and their dependence on stochastic pre- or postsynaptic firing rates can be manipulated by adjusting the exact profile of the asymmetric learning window and temporal restrictions on spike pair interactions respectively. These findings imply that previously disparate models of rate-coded autoassociative learning and temporally coded heteroassociative learning, mediated by symmetric and asymmetric connections respectively, can be implemented in a single network using a single plasticity rule. However, we also demonstrate that forms of STDP that can be reconciled with rate-coded Hebbian learning do not generate inherent synaptic competition, and thus some additional mechanism is required to guarantee long-term input-output selectivity.

Spike Timing-Dependent Plasticity: From Synapse to Perception

2006 ◽  
Vol 86 (3) ◽  
pp. 1033-1048 ◽  
Author(s):  
Yang Dan ◽  
Mu-Ming Poo
Keyword(s):  
Neuronal Excitability ◽  
Human Perception ◽  
Long Term Potentiation ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Functional Changes

Information in the nervous system may be carried by both the rate and timing of neuronal spikes. Recent findings of spike timing-dependent plasticity (STDP) have fueled the interest in the potential roles of spike timing in processing and storage of information in neural circuits. Induction of long-term potentiation (LTP) and long-term depression (LTD) in a variety of in vitro and in vivo systems has been shown to depend on the temporal order of pre- and postsynaptic spiking. Spike timing-dependent modification of neuronal excitability and dendritic integration was also observed. Such STDP at the synaptic and cellular level is likely to play important roles in activity-induced functional changes in neuronal receptive fields and human perception.

Changes in Granule Cell Firing Rates Precede Locally Recorded Spontaneous Seizures by Minutes in an Animal Model of Temporal Lobe Epilepsy

2008 ◽  
Vol 99 (5) ◽  
pp. 2431-2442 ◽  
Author(s):  
Mark R. Bower ◽  
Paul S. Buckmaster
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Dentate Gyrus ◽  
Firing Rate ◽  
Temporal Lobe ◽  
Granule Cells ◽  
Neuronal Firing ◽  
Seizure Onset ◽  
Electrographic Seizure ◽  
Firing Rates ◽  
Spontaneous Seizures

Although much is known about persistent molecular, cellular, and circuit changes associated with temporal lobe epilepsy, mechanisms of seizure onset remain unclear. The dentate gyrus displays many persistent epilepsy-related abnormalities and is in the mesial temporal lobe where seizures initiate in patients. However, little is known about seizure-related activity of individual neurons in the dentate gyrus. We used tetrodes to record action potentials of multiple, single granule cells before and during spontaneous seizures in epileptic pilocarpine-treated rats. Subsets of granule cells displayed four distinct activity patterns: increased firing before seizure onset, decreased firing before seizure onset, increased firing only after seizure onset, and unchanged firing rates despite electrographic seizure activity in the immediate vicinity. No cells decreased firing rate immediately after seizure onset. During baseline periods between seizures, action potential waveforms and firing rates were similar among the four subsets of granule cells in epileptic rats and in granule cells of control rats. The mean normalized firing rate of granule cells whose firing rates increased before seizure onset deviated from baseline earliest, beginning 4 min before dentate gyrus electrographic seizure onset, and increased progressively, more than doubling by seizure onset. It is generally assumed that neuronal firing rates increase abruptly and synchronously only when electrographic seizures begin. However, these findings show heterogeneous and gradually building changes in activity of individual granule cells minutes before spontaneous seizures.

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