Searching for Principles of Brain Computation

Mapping Intimacies ◽

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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Paper-based patterned 3D neural cultures as a tool to study network activity on multielectrode arrays

RSC Advances ◽

10.1039/c7ra00971b ◽

2017 ◽

Vol 7 (62) ◽

pp. 39359-39371 ◽

Cited By ~ 5

Author(s):

Harald Dermutz ◽

Greta Thompson-Steckel ◽

Csaba Forró ◽

Victoria de Lange ◽

Livie Dorwling-Carter ◽

...

Keyword(s):

High Throughput ◽

Activity Patterns ◽

Network Activity ◽

Multielectrode Arrays ◽

Arbitrary Topology ◽

Extracellular Signals ◽

Neural Cultures

High-throughput platform targeting activity patterns of 3D neural cultures with arbitrary topology, by combining network-wide intracellular and local extracellular signals.

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Memory Replay in Balanced Recurrent Networks

10.1101/069641 ◽

2016 ◽

Author(s):

Nikolay Chenkov ◽

Henning Sprekeler ◽

Richard Kempter

Keyword(s):

Large Scale ◽

Neuronal Excitability ◽

Activity Patterns ◽

Recurrent Network ◽

Network Activity ◽

Sharp Waves ◽

Sequence Of Events ◽

Up States ◽

Plausible Model ◽

Network States

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

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.715497800.791002851 ◽

2012 ◽

Author(s):

Leonard Maler

Keyword(s):

Recurrent Network ◽

Network Activity

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Predicting Human Mobility with Reinforcement-Learning-Based Long-Term Periodicity Modeling

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/3469860 ◽

2021 ◽

Vol 12 (6) ◽

pp. 1-23

Author(s):

Shuo Tao ◽

Jingang Jiang ◽

Defu Lian ◽

Kai Zheng ◽

Enhong Chen

Keyword(s):

Reinforcement Learning ◽

Human Mobility ◽

Recurrent Network ◽

Mobility Prediction ◽

Learning Framework ◽

Temporal Features ◽

Wide Range ◽

Spatio Temporal ◽

Historical Trajectory

Mobility prediction plays an important role in a wide range of location-based applications and services. However, there are three problems in the existing literature: (1) explicit high-order interactions of spatio-temporal features are not systemically modeled; (2) most existing algorithms place attention mechanisms on top of recurrent network, so they can not allow for full parallelism and are inferior to self-attention for capturing long-range dependence; (3) most literature does not make good use of long-term historical information and do not effectively model the long-term periodicity of users. To this end, we propose MoveNet and RLMoveNet. MoveNet is a self-attention-based sequential model, predicting each user’s next destination based on her most recent visits and historical trajectory. MoveNet first introduces a cross-based learning framework for modeling feature interactions. With self-attention on both the most recent visits and historical trajectory, MoveNet can use an attention mechanism to capture the user’s long-term regularity in a more efficient way. Based on MoveNet, to model long-term periodicity more effectively, we add the reinforcement learning layer and named RLMoveNet. RLMoveNet regards the human mobility prediction as a reinforcement learning problem, using the reinforcement learning layer as the regularization part to drive the model to pay attention to the behavior with periodic actions, which can help us make the algorithm more effective. We evaluate both of them with three real-world mobility datasets. MoveNet outperforms the state-of-the-art mobility predictor by around 10% in terms of accuracy, and simultaneously achieves faster convergence and over 4x training speedup. Moreover, RLMoveNet achieves higher prediction accuracy than MoveNet, which proves that modeling periodicity explicitly from the perspective of reinforcement learning is more effective.

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Epileptogenesis due to glia-mediated synaptic scaling

Journal of The Royal Society Interface ◽

10.1098/rsif.2008.0387 ◽

2008 ◽

Vol 6 (37) ◽

pp. 655-668 ◽

Cited By ~ 35

Author(s):

Cristina Savin ◽

Jochen Triesch ◽

Michael Meyer-Hermann

Keyword(s):

Immune System ◽

Neuronal Activity ◽

Activity Patterns ◽

Brain Inflammation ◽

Network Activity ◽

Homeostatic Regulation ◽

Synaptic Scaling ◽

Tnf Α ◽

Factor Α ◽

Potential Interactions

Homeostatic regulation of neuronal activity is fundamental for the stable functioning of the cerebral cortex. One form of homeostatic synaptic scaling has been recently shown to be mediated by glial cells that interact with neurons through the diffusible messenger tumour necrosis factor-α (TNF-α). Interestingly, TNF-α is also used by the immune system as a pro-inflammatory messenger, suggesting potential interactions between immune system signalling and the homeostatic regulation of neuronal activity. We present the first computational model of neuron–glia interaction in TNF-α-mediated synaptic scaling. The model shows how under normal conditions the homeostatic mechanism is effective in balancing network activity. After chronic immune activation or TNF-α overexpression by glia, however, the network develops seizure-like activity patterns. This may explain why under certain conditions brain inflammation increases the risk of seizures. Additionally, the model shows that TNF-α diffusion may be responsible for epileptogenesis after localized brain lesions.

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How the Limit Values Work

East European Journal of Physics ◽

10.26565/2312-4334-2021-1-01 ◽

2021 ◽

Keyword(s):

Cosmological Constant ◽

Gravitational Radius ◽

Limit Values ◽

Event Horizons ◽

Dirac Type ◽

Large Numbers ◽

Spatio Temporal ◽

The Universe ◽

Limiting Values ◽

Physical Principles

The efficiency of limiting quantities as a tool for describing physics at various spatio-temporal scales is shown. Due to its universality, limit values allow us to establish relationships between, at first glance, distant from each other's characteristics. The article discusses specific examples of the use of limit values to establish such relationships between quantities at different scales. Based on the principle of reaching the limiting values on the event horizons, a connection was obtained between the Planck values and the values of the Universe. The resulting relation can be attributed to relations of the Dirac type - the coincidence of large numbers that emerged from empirical observations. In the article, the relationships between large numbers of the Dirac type are established proceeding, in a certain sense, from physical principles - the existence of limiting values. It is shown that this ratio is observed throughout the evolution of the Universe. An alternative way of solving the problem of the cosmological constant using limiting values and its relation to the minimum spatial scale is discussed. In addition, a one-parameter family of masses was introduced, including the mass of the Universe, the Planck mass and the mass of the graviton, which also establish relationships between quantities differing by 120 orders of magnitude. It is shown that entropic forces also obey the same universal limiting constraints as ordinary forces. Thus, the existence of limiting values extends to informational limitations in the Universe. It is fundamentally important that on any event horizon, regardless of its scale (i.e., its gravitational radius), the universal value of limit force c4/4G is realized. This allows you to relate the characteristics of the Universe related to various stages of its evolution.

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Schizophrenia-associated variation at ZNF804A correlates with altered experience-dependent dynamics of sleep slow waves and spindles in healthy young adults

SLEEP ◽

10.1093/sleep/zsab191 ◽

2021 ◽

Author(s):

Ullrich Bartsch ◽

Laura J Corbin ◽

Charlotte Hellmich ◽

Michelle Taylor ◽

Kayleigh E Easey ◽

...

Keyword(s):

Memory Consolidation ◽

Neural Circuit ◽

Activity Patterns ◽

Nrem Sleep ◽

Network Activity ◽

Healthy Population ◽

Adult Males ◽

Motor Sequence ◽

Performance Improvements ◽

Common Genetic Variants

Abstract The rs1344706 polymorphism in ZNF804A is robustly associated with schizophrenia and schizophrenia is, in turn, associated with abnormal non-rapid eye movement (NREM) sleep neurophysiology. To examine whether rs1344706 is associated with intermediate neurophysiological traits in the absence of disease, we assessed the relationship between genotype, sleep neurophysiology, and sleep-dependent memory consolidation in healthy participants. We recruited healthy adult males with no history of psychiatric disorder from the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort. Participants were homozygous for either the schizophrenia-associated ‘A’ allele (N=22) or the alternative ‘C’ allele (N=18) at rs1344706. Actigraphy, polysomnography (PSG) and a motor sequence task (MST) were used to characterize daily activity patterns, sleep neurophysiology and sleep-dependent memory consolidation. Average MST learning and sleep-dependent performance improvements were similar across genotype groups, albeit more variable in the AA group. During sleep after learning, CC participants showed increased slow-wave (SW) and spindle amplitudes, plus augmented coupling of SW activity across recording electrodes. SW and spindles in those with the AA genotype were insensitive to learning, whilst SW coherence decreased following MST training. Accordingly, NREM neurophysiology robustly predicted the degree of overnight motor memory consolidation in CC carriers, but not in AA carriers. We describe evidence that rs1344706 polymorphism in ZNF804A is associated with changes in the coordinated neural network activity that supports offline information processing during sleep in a healthy population. These findings highlight the utility of sleep neurophysiology in mapping the impacts of schizophrenia-associated common genetic variants on neural circuit oscillations and function.

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Evidence against altered excitatory/inhibitory balance in the posteromedial cortex of young adult APOE E4 carriers: a resting state 1H-MRS study

10.1101/2021.05.12.443879 ◽

2021 ◽

Author(s):

Alison G Costigan ◽

Katja Umla-Runge ◽

C John Evans ◽

Rachel Raybould ◽

Kim S Graham ◽

...

Keyword(s):

Resting State ◽

Genetic Risk ◽

Occipital Cortex ◽

Network Activity ◽

Resonance Spectroscopy ◽

1H Mrs ◽

Gaba A ◽

Spatio Temporal ◽

E4 Allele

A strategy to gain insight into early changes that may predispose people to Alzheimer's disease is to study the brains of younger cognitively healthy people that are at increased genetic risk of AD. The Apolipoprotein (APOE) E4 allele is the strongest genetic risk factor for AD, and several neuroimaging studies comparing APOE E4 carriers with non-carriers at age ~20-30 have detected hyperactivity (or reduced deactivation) in posteromedial cortex (PMC), a key hub of the default network (DN) which has a high susceptibility to early amyloid deposition in AD. Transgenic mouse models suggest such early network activity alterations may result from altered excitatory/inhibitory (E/I) balance, but this is yet to be examined in humans. Here we test the hypothesis that PMC fMRI hyperactivity could be underpinned by altered levels of excitatory (glutamate) and/or inhibitory (GABA) neurotransmitters in this brain region. Forty-seven participants (20 APOE E4 carriers and 27 non-carriers) aged 18-25 underwent resting-state proton magnetic resonance spectroscopy (1H-MRS), a non-invasive neuroimaging technique to measure glutamate and GABA in vivo. Metabolites were measured in a PMC voxel of interest and in a comparison voxel in the occipital cortex (OCC). There was no difference in either glutamate or GABA between the E4 carriers and non-carriers in either MRS voxel, nor in the ratio of glutamate to GABA, a measure of E/I balance. Default Bayesian t-tests revealed evidence in support of this null finding. Results suggest that PMC hyperactivity in APOE E4 carriers is unlikely to be associated with, or indeed may precede, alterations in local resting-state PMC neurotransmitters, thus informing the spatio-temporal order and the cause/effect dynamic of neuroimaging differences in APOE E4 carriers.

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Oxytocin shapes spontaneous activity patterns in the developing visual cortex by activating somatostatin interneurons

10.1101/866251 ◽

2019 ◽

Cited By ~ 1

Author(s):

Paloma P Maldonado ◽

Alvaro Nuno-Perez ◽

Jan Kirchner ◽

Elizabeth Hammock ◽

Julijana Gjorgjieva ◽

...

Keyword(s):

Visual Cortex ◽

Spontaneous Activity ◽

Sensory Processing ◽

Activity Patterns ◽

Network Activity ◽

Synaptic Connections ◽

Pairwise Correlations ◽

Early Brain Development

SummarySpontaneous network activity shapes emerging neuronal circuits during early brain development, however how neuromodulation influences this activity is not fully understood. Here, we report that the neuromodulator oxytocin powerfully shapes spontaneous activity patterns. In vivo, oxytocin strongly decreased the frequency and pairwise correlations of spontaneous activity events in visual cortex (V1), but not in somatosensory cortex (S1). This differential effect was a consequence of oxytocin only increasing inhibition in V1 and increasing both inhibition and excitation in S1. The increase in inhibition was mediated by the depolarization and increase in excitability of somatostatin+ (SST) interneurons specifically. Accordingly, silencing SST+ neurons pharmacogenetically fully blocked oxytocin’s effect on inhibition in vitro as well its effect on spontaneous activity patterns in vivo. Thus, oxytocin decreases the excitatory/inhibitory ratio and modulates specific features of V1 spontaneous activity patterns that are crucial for refining developing synaptic connections and sensory processing later in life.

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