Chaos in the Brain: A New Strategy to Discriminate Deterministic Low Dimensional Dynamics in the Spontaneous Activity of the Human Cortex

Biomag 96 ◽  
2000 ◽  
pp. 963-966
Author(s):  
L. Narici ◽  
S. Boccaletti ◽  
A. Giaquinta ◽  
T. Arecchi
Keyword(s):  
Spontaneous Activity ◽  
Human Cortex ◽  
New Strategy ◽  
Low Dimensional ◽  
Low Dimensional Dynamics ◽  
The Brain

scholarly journals Low-dimensional dynamics for working memory and time encoding

2020 ◽  
Vol 117 (37) ◽  
pp. 23021-23032 ◽  
Author(s):  
Christopher J. Cueva ◽  
Alex Saez ◽  
Encarni Marcos ◽  
Aldo Genovesio ◽  
Mehrdad Jazayeri ◽  
...  
Keyword(s):  
Working Memory ◽  
Neural Data ◽  
Sustained Activity ◽  
Sensory Experiences ◽  
Time Encoding ◽  
Low Dimensional ◽  
Low Dimensional Dynamics ◽  
Working Memory Models ◽  
The Brain ◽  
Rule Out

Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these experiences and, simultaneously, estimate the timing between events. To understand the mechanisms underlying working memory and time encoding, we analyze neural activity recorded during delays in four experiments on nonhuman primates. To disambiguate potential mechanisms, we propose two analyses, namely, decoding the passage of time from neural data and computing the cumulative dimensionality of the neural trajectory over time. Time can be decoded with high precision in tasks where timing information is relevant and with lower precision when irrelevant for performing the task. Neural trajectories are always observed to be low-dimensional. In addition, our results further constrain the mechanisms underlying time encoding as we find that the linear “ramping” component of each neuron’s firing rate strongly contributes to the slow timescale variations that make decoding time possible. These constraints rule out working memory models that rely on constant, sustained activity and neural networks with high-dimensional trajectories, like reservoir networks. Instead, recurrent networks trained with backpropagation capture the time-encoding properties and the dimensionality observed in the data.

scholarly journals Low dimensional dynamics for working memory and time encoding

2018 ◽  
Author(s):  
Christopher J. Cueva ◽  
Alex Saez ◽  
Encarni Marcos ◽  
Aldo Genovesio ◽  
Mehrdad Jazayeri ◽  
...  
Keyword(s):  
Working Memory ◽  
Neural Data ◽  
Sustained Activity ◽  
Sensory Experiences ◽  
Time Encoding ◽  
Low Dimensional ◽  
Low Dimensional Dynamics ◽  
Working Memory Models ◽  
The Brain ◽  
Rule Out

Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these experiences and, simultaneously, estimate the timing between events. To understand the mechanisms underlying working memory and time encoding we analyze neural activity recorded during delays in four experiments on non-human primates. To disambiguate potential mechanisms, we propose two analyses, namely, decoding the passage of time from neural data, and computing the cumulative dimensionality of the neural trajectory over time. Time can be decoded with high precision in tasks where timing information is relevant and with lower precision when irrelevant for performing the task. Neural trajectories are always observed to be low dimensional. These constraints rule out working memory models that rely on constant, sustained activity, and neural networks with high dimensional trajectories, like reservoir networks. Instead, recurrent networks trained with backpropagation capture the time encoding properties and the dimensionality observed in the data.

scholarly journals Development of a New Polymeric Nanocarrier Dedicated to Controlled Clozapine Delivery at the Dopamine D2-Serotonin 5-HT1A Heteromers

Polymers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1000
Author(s):  
Sylwia Łukasiewicz
Keyword(s):  
Physicochemical Parameters ◽  
Modern Science ◽  
Dopamine D2 ◽  
Targeted Transport ◽  
First Choice ◽  
New Strategy ◽  
Cell Components ◽  
Second Generation Antipsychotic ◽  
The Brain ◽  
Polymeric Nanocarrier

Clozapine, the second generation antipsychotic drug, is one of the prominent compounds used for treatment of schizophrenia. Unfortunately, use of this drug is still limited due to serious side effects connected to its unspecific and non-selective action. Nevertheless, clozapine still remains the first-choice drug for the situation of drug-resistance schizophrenia. Development of the new strategy of clozapine delivery into well-defined parts of the brain has been a great challenge for modern science. In the present paper we focus on the presentation of a new nanocarrier for clozapine and its use for targeted transport, enabling its interaction with the dopamine D2 and serotonin 5-HT1A heteromers (D2-5-HT1A) in the brain tissue. Clozapine polymeric nanocapsules (CLO-NCs) were prepared using anionic surfactant AOT (sodium docusate) as an emulsifier, and bio-compatible polyelectrolytes such as: poly-L-glutamic acid (PGA) and poly-L-lysine (PLL). Outer layer of the carrier was grafted by polyethylene glycol (PEG). Several variants of nanocarriers containing the antipsychotic varying in physicochemical parameters were tested. This kind of approach may enable the availability and safety of the drug, improve the selectivity of its action, and finally increase effectiveness of schizophrenia therapy. Moreover, the purpose of the manuscript is to cover a wide scope of the issues, which should be considered while designing a novel means for drug delivery. It is important to determine the interactions of a new nanocarrier with many cell components on various cellular levels in order to be sure that the new nanocarrier will be safe and won’t cause undesired effects for a patient.

scholarly journals Multiscale low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hamidreza Abbaspourazad ◽  
Mahdi Choudhury ◽  
Yan T. Wong ◽  
Bijan Pesaran ◽  
Maryam M. Shanechi
Keyword(s):  
Neural Control ◽  
Multiple Scales ◽  
Network Activity ◽  
Neural Dynamics ◽  
Population Activity ◽  
Reach And Grasp ◽  
Spatiotemporal Scales ◽  
Motor Cortical ◽  
Low Dimensional ◽  
Low Dimensional Dynamics

AbstractMotor function depends on neural dynamics spanning multiple spatiotemporal scales of population activity, from spiking of neurons to larger-scale local field potentials (LFP). How multiple scales of low-dimensional population dynamics are related in control of movements remains unknown. Multiscale neural dynamics are especially important to study in naturalistic reach-and-grasp movements, which are relatively under-explored. We learn novel multiscale dynamical models for spike-LFP network activity in monkeys performing naturalistic reach-and-grasps. We show low-dimensional dynamics of spiking and LFP activity exhibited several principal modes, each with a unique decay-frequency characteristic. One principal mode dominantly predicted movements. Despite distinct principal modes existing at the two scales, this predictive mode was multiscale and shared between scales, and was shared across sessions and monkeys, yet did not simply replicate behavioral modes. Further, this multiscale mode’s decay-frequency explained behavior. We propose that multiscale, low-dimensional motor cortical state dynamics reflect the neural control of naturalistic reach-and-grasp behaviors.

Differential Involvement of Projection Neurons During Emergence of Spontaneous Activity in the Developing Avian Hindbrain

2009 ◽  
Vol 101 (2) ◽  
pp. 591-602 ◽  
Author(s):  
Hiraku Mochida ◽  
Gilles Fortin ◽  
Jean Champagnat ◽  
Joel C. Glover
Keyword(s):  
Spontaneous Activity ◽  
Projection Neuron ◽  
Projection Neurons ◽  
Charge Coupled Device ◽  
Neuron Populations ◽  
Motor Nerves ◽  
Differential Involvement ◽  
A Charge ◽  
The Brain ◽  
Calcium Green

To better characterize the emergence of spontaneous neuronal activity in the developing hindbrain, spontaneous activity was recorded optically from defined projection neuron populations in isolated preparations of the brain stem of the chicken embryo. Ipsilaterally projecting reticulospinal (RS) neurons and several groups of vestibuloocular (VO) neurons were labeled retrogradely with Calcium Green-1 dextran amine and spontaneous calcium transients were recorded using a charge-coupled-device camera mounted on a fluorescence microscope. Simultaneous extracellular recordings were made from one of the trigeminal motor nerves (nV) to register the occurrence of spontaneous synchronous bursts of activity. Two types of spontaneous activity were observed: synchronous events (SEs), which occurred in register with spontaneous bursts in nV once every few minutes and were tetrodotoxin (TTX) dependent, and asynchronous events (AEs), which occurred in the intervals between SEs and were TTX resistant. AEs occurred developmentally before SEs and were in general smaller and more variable in amplitude than SEs. SEs appeared at the same stage as nV bursts early on embryonic day 4, first in RS neurons and then in VO neurons. All RS neurons participated equally in SEs from the outset, whereas different subpopulations of VO neurons participated differentially, both in terms of the proportion of neurons that exhibited SEs, the fidelity with which the SEs in individual neurons followed the nV bursts, and the developmental stage at which SEs appeared and matured. The results show that spontaneous activity is expressed heterogeneously among hindbrain projection neuron populations, suggesting its differential involvement in the formation of different functional neuronal circuits.

Evidence of low-dimensional dynamics in a traffic noise time series

2001 ◽  
Vol 12 (5) ◽  
pp. 859-864
Author(s):  
V.K. Jain ◽  
A.K. Srivastava ◽  
Anup Das ◽  
Vikas Rai
Keyword(s):  
Time Series ◽  
Traffic Noise ◽  
Low Dimensional ◽  
Low Dimensional Dynamics

Low-dimensional dynamics of a turbulent wall flow

2001 ◽  
Vol 435 ◽  
pp. 81-91 ◽  
Author(s):  
JAVIER JIMÉNEZ ◽  
MARK P. SIMENS
Keyword(s):  
Numerical Simulations ◽  
Single Copy ◽  
Streamwise Vortices ◽  
Low Velocity ◽  
Wall Distance ◽  
Wall Flow ◽  
Low Dimensional ◽  
Turbulent Wall ◽  
Low Dimensional Dynamics ◽  
Masking Function

The low-dimensional dynamics of the structures in a turbulent wall flow are studied by means of numerical simulations. These are made both ‘minimal’, in the sense that they contain a single copy of each relevant structure, and ‘autonomous’ in the sense that there is no outer turbulent flow with which they can interact. The interaction is prevented by a numerical mask that damps the flow above a given wall distance, and the flow behaviour is studied as a function of the mask height. The simplest case found is a streamwise wave that propagates without change. It takes the form of a single wavy low-velocity streak flanked by two counter-rotating staggered quasi-streamwise vortices, and is found when the height of the numerical masking function is less than δ+1 ≈ 50. As the mask height is increased, this solution bifurcates into an almost-perfect limit cycle, a two-frequency torus, weak chaos, and full-edged bursting turbulence. The transition is essentially complete when δ+1 ≈ 70, even if the wall-parallel dimensions of the computational box are small enough for bursting turbulence to be metastable, lasting only for a few bursting cycles. Similar low-dimensional dynamics are found in somewhat larger boxes, containing two copies of the basic structures, in which the bursting turbulence is self-sustaining.

scholarly journals Distributed context-dependent choice information in mouse dorsal-parietal cortex

2021 ◽  
Author(s):  
Javier Orlandi ◽  
Mohammad Adbolrahmani ◽  
Ryo Aoki ◽  
Dmitry Lyamzin ◽  
Andrea Benucci
Keyword(s):  
Decision Making ◽  
Decision Variable ◽  
Visual Stream ◽  
Posterior Cortex ◽  
Neural Data ◽  
Context Dependent ◽  
Cortical Regions ◽  
Low Dimensional ◽  
Independent Decision ◽  
The Brain

Abstract Choice information appears in the brain as distributed signals with top-down and bottom-up components that together support decision-making computations. In sensory and associative cortical regions, the presence of choice signals, their strength, and area specificity are known to be elusive and changeable, limiting a cohesive understanding of their computational significance. In this study, examining the mesoscale activity in mouse posterior cortex during a complex visual discrimination task, we found that broadly distributed choice signals defined a decision variable in a low-dimensional embedding space of multi-area activations, particularly along the ventral visual stream. The subspace they defined was near-orthogonal to concurrently represented sensory and motor-related activations, and it was modulated by task difficulty and contextually by the animals’ attention state. To mechanistically relate choice representations to decision-making computations, we trained recurrent neural networks with the animals’ choices and found an equivalent decision variable whose context-dependent dynamics agreed with that of the neural data. In conclusion, our results demonstrated an independent decision variable broadly represented in the posterior cortex, controlled by task features and cognitive demands. Its dynamics reflected decision computations, possibly linked to context-dependent feedback signals used for probabilistic-inference computations in variable animal-environment interactions.

scholarly journals Latent learning drives sleep-dependent plasticity in distinct CA1 subpopulations

2020 ◽  
Author(s):  
Wei Guo ◽  
Jie J. Zhang ◽  
Jonathan P. Newman ◽  
Matthew A. Wilson
Keyword(s):  
Hippocampal Neurons ◽  
Dimensional Manifold ◽  
Latent Learning ◽  
Neural Ensembles ◽  
Correlated Activity ◽  
Place Fields ◽  
Internal States ◽  
Low Dimensional ◽  
Low Dimensional Manifold ◽  
The Brain

AbstractLatent learning allows the brain the transform experiences into cognitive maps, a form of implicit memory, without reinforced training. Its mechanism is unclear. We tracked the internal states of the hippocampal neural ensembles and discovered that during latent learning of a spatial map, the state space evolved into a low-dimensional manifold that topologically resembled the physical environment. This process requires repeated experiences and sleep in-between. Further investigations revealed that a subset of hippocampal neurons, instead of rapidly forming place fields in a novel environment, remained weakly tuned but gradually developed correlated activity with other neurons. These ‘weakly spatial’ neurons bond activity of neurons with stronger spatial tuning, linking discrete place fields into a map that supports flexible navigation.

Sign in / Sign up

Export Citation Format

Share Document