scholarly journals Noise-Induced Alternations in an Attractor Network Model of Perceptual Bistability

2007 ◽  
Vol 98 (3) ◽  
pp. 1125-1139 ◽  
Author(s):  
Rubén Moreno-Bote ◽  
John Rinzel ◽  
Nava Rubin
Keyword(s):  
Natural Extension ◽  
Mean Field ◽  
Activity Levels ◽  
Alternation Rate ◽  
Neural Architecture ◽  
Electrophysiological Recordings ◽  
Neural Populations ◽  
Attractor Model ◽  
Open Question ◽  
Competing Populations

When a stimulus supports two distinct interpretations, perception alternates in an irregular manner between them. What causes the bistable perceptual switches remains an open question. Most existing models assume that switches arise from a slow fatiguing process, such as adaptation or synaptic depression. We develop a new, attractor-based framework in which alternations are induced by noise and are absent without it. Our model goes beyond previous energy-based conceptualizations of perceptual bistability by constructing a neurally plausible attractor model that is implemented in both firing rate mean-field and spiking cell-based networks. The model accounts for known properties of bistable perceptual phenomena, most notably the increase in alternation rate with stimulation strength observed in binocular rivalry. Furthermore, it makes a novel prediction about the effect of changing stimulus strength on the activity levels of the dominant and suppressed neural populations, a prediction that could be tested with functional MRI or electrophysiological recordings. The neural architecture derived from the energy-based model readily generalizes to several competing populations, providing a natural extension for multistability phenomena.

scholarly journals Value Representations in the Rodent Orbitofrontal Cortex Drive Learning, not Choice

2018 ◽  
Author(s):  
Kevin J. Miller ◽  
Matthew M. Botvinick ◽  
Carlos D. Brody
Keyword(s):  
Orbitofrontal Cortex ◽  
Future Expectations ◽  
Neural Representations ◽  
Electrophysiological Recordings ◽  
Expected Values ◽  
Open Question ◽  
Reward Information ◽  
To Receive ◽  
The Brain

AbstractHumans and animals make predictions about the rewards they expect to receive in different situations. In formal models of behavior, these predictions are known as value representations, and they play two very different roles. Firstly, they drive choice: the expected values of available options are compared to one another, and the best option is selected. Secondly, they support learning: expected values are compared to rewards actually received, and future expectations are updated accordingly. Whether these different functions are mediated by different neural representations remains an open question. Here we employ a recently-developed multi-step task for rats that computationally separates learning from choosing. We investigate the role of value representations in the rodent orbitofrontal cortex, a key structure for value-based cognition. Electrophysiological recordings and optogenetic perturbations indicate that these representations do not directly drive choice. Instead, they signal expected reward information to a learning process elsewhere in the brain that updates choice mechanisms.

scholarly journals Non-linear and chaotic dynamo regimes

2012 ◽  
Vol 8 (S294) ◽  
pp. 387-398
Author(s):  
Axel Brandenburg
Keyword(s):  
Mean Field ◽  
Rotation Frequency ◽  
Magnetic Activity ◽  
Current Status ◽  
Small Scale ◽  
Field Approach ◽  
Magnetic Prandtl Number ◽  
The Mean ◽  
Open Question

AbstractAn update is given on the current status of solar and stellar dynamos. At present, it is still unclear why stellar cycle frequencies increase with rotation frequency in such a way that their ratio increases with stellar activity. The small-scale dynamo is expected to operate in spite of a small value of the magnetic Prandtl number in stars. Whether or not the global magnetic activity in stars is a shallow or deeply rooted phenomenon is another open question. Progress in demonstrating the presence and importance of magnetic helicity fluxes in dynamos is briefly reviewed, and finally the role of nonlocality is emphasized in modeling stellar dynamos using the mean-field approach. On the other hand, direct numerical simulations have now come to the point where the models show solar-like equatorward migration that can be compared with observations and that need to be understood theoretically.

scholarly journals Distinct contribution of cone photoreceptor subtypes to the mammalian biological clock

2021 ◽  
Vol 118 (22) ◽  
pp. e2024500118
Author(s):  
Hester C. van Diepen ◽  
Robin A. Schoonderwoerd ◽  
Ashna Ramkisoensing ◽  
Jan A. M. Janse ◽  
Samer Hattar ◽  
...  
Keyword(s):  
Neuronal Activity ◽  
Uv Light ◽  
Biological Clock ◽  
Green Light ◽  
Sustained Response ◽  
Activity Levels ◽  
Cone Photoreceptors ◽  
Light Levels ◽  
Electrophysiological Recordings

Ambient light detection is important for the synchronization of the circadian clock to the external solar cycle. Light signals are sent to the suprachiasmatic nuclei (SCN), the site of the major circadian pacemaker. It has been assumed that cone photoreceptors contribute minimally to synchronization. Here, however, we find that cone photoreceptors are sufficient for mediating entrainment and transmitting photic information to the SCN, as evaluated in mice that have only cones as functional photoreceptors. Using in vivo electrophysiological recordings in the SCN of freely moving cone-only mice, we observed light responses in SCN neuronal activity in response to 60-s pulses of both ultraviolet (UV) (λmax 365 nm) and green (λmax 505 nm) light. Higher irradiances of UV light led to irradiance-dependent enhancements in SCN neuronal activity, whereas higher irradiances of green light led to a reduction in the sustained response with only the transient response remaining. Responses in SCN neuronal activity decayed with a half-max time of ∼9 min for UV light and less than a minute for green light, indicating differential input between short-wavelength–sensitive and mid-wavelength–sensitive cones for the SCN responsiveness. Furthermore, we show that UV light is more effective for photoentrainment than green light. Based on the lack of a full sustained response in cone-only mice, we confirmed that rapidly alternating light levels, rather than slowly alternating light, caused substantial phase shifts. Together, our data provide strong evidence that cone types contribute to photoentrainment and differentially affect the electrical activity levels of the SCN.

scholarly journals Biologically realistic mean-field models of conductancebased networks of spiking neurons with adaptation

2018 ◽  
Author(s):  
Matteo di Volo ◽  
Alberto Romagnoni ◽  
Cristiano Capone ◽  
Alain Destexhe
Keyword(s):  
Large Scale ◽  
Field Model ◽  
Mean Field ◽  
Inhibitory Neurons ◽  
Activity Levels ◽  
Mean Field Model ◽  
Nonlinear Properties ◽  
Mean Field Models ◽  
The Mean

AbstractAccurate population models are needed to build very large scale neural models, but their derivation is difficult for realistic networks of neurons, in particular when nonlinear properties are involved such as conductance-based interactions and spike-frequency adaptation. Here, we consider such models based on networks of Adaptive exponential Integrate and fire excitatory and inhibitory neurons. Using a Master Equation formalism, we derive a mean-field model of such networks and compare it to the full network dynamics. The mean-field model is capable to correctly predict the average spontaneous activity levels in asynchronous irregular regimes similar to in vivo activity. It also captures the transient temporal response of the network to complex external inputs. Finally, the mean-field model is also able to quantitatively describe regimes where high and low activity states alternate (UP-DOWN state dynamics), leading to slow oscillations. We conclude that such mean-field models are “biologically realistic” in the sense that they can capture both spontaneous and evoked activity, and they naturally appear as candidates to build very large scale models involving multiple brain areas.

scholarly journals A mean-field approach to the dynamics of networks of complex neurons, from nonlinear Integrate-and-Fire to Hodgkin-Huxley models

2019 ◽  
Author(s):  
M. Carlu ◽  
O. Chehab ◽  
L. Dalla Porta ◽  
D. Depannemaecker ◽  
C. Héricé ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Mean Field ◽  
Population Models ◽  
Inhibitory Neurons ◽  
Mathematical Tool ◽  
Mean Field Model ◽  
Integrate And Fire ◽  
Mean Field Models ◽  
Electrophysiological Recordings ◽  
Field Formalism

AbstractWe present a mean-field formalism able to predict the collective dynamics of large networks of conductance-based interacting spiking neurons. We apply this formalism to several neuronal models, from the simplest Adaptive Exponential Integrate-and-Fire model to the more complex Hodgkin-Huxley and Morris-Lecar models. We show that the resulting mean-field models are capable of predicting the correct spontaneous activity of both excitatory and inhibitory neurons in asynchronous irregular regimes, typical of cortical dynamics. Moreover, it is possible to quantitatively predict the populations response to external stimuli in the form of external spike trains. This mean-field formalism therefore provides a paradigm to bridge the scale between population dynamics and the microscopic complexity of the individual cells physiology.NEW & NOTEWORTHYPopulation models are a powerful mathematical tool to study the dynamics of neuronal networks and to simulate the brain at macroscopic scales. We present a mean-field model capable of quantitatively predicting the temporal dynamics of a network of complex spiking neuronal models, from Integrate-and-Fire to Hodgkin-Huxley, thus linking population models to neurons electrophysiology. This opens a perspective on generating biologically realistic mean-field models from electrophysiological recordings.

scholarly journals Living with ankylosing spondylitis: an open response survey exploring physical activity experiences

2019 ◽  
Vol 3 (2) ◽  
Author(s):  
Peter C Rouse ◽  
Martyn Standage ◽  
Raj Sengupta
Keyword(s):  
Physical Activity ◽  
Online Survey ◽  
Physical Activities ◽  
Activity Levels ◽  
Physically Active ◽  
Self Monitoring ◽  
Diverse Range ◽  
Open Questions ◽  
Specific Factors ◽  
Open Question

Abstract Objective The aim was to gather in-depth, rich accounts of physical activity experiences of people living with AS, to include symptom management, consequences for symptoms, factors that encourage and disrupt physical activity, and motivations that underpin participation in physical activity. Methods Participants (n = 149; 60% female) completed a Bristol Online Survey that consisted of open questions to capture rich qualitative data. In total, 96% of participants self-reported having AS (1% other arthritis; 3% missing), and 51% had this diagnosis for >20 years. A content analysis was conducted to identify the key themes/factors from within the open question responses. A frequency analysis was used to ascertain the most commonly identified themes and factors. Results Fifty different physical activities were participated in over the previous month. Physical activity can improve and worsen arthritis symptoms, and fluctuations in participation exist even in the most active. Pain and fatigue were the two most frequently identified factors that stopped people with AS from being physically active. Participants reported more autonomously driven motivations than controlled motivations for participating in physical activity. Conclusion People with AS can and do participate in a diverse range of physical activities, but fluctuations in activity levels occur owing to disease- and non-disease-specific factors. Individually tailored plans and self-monitoring are important to optimize levels of physical activity and maximize benefits for people living with AS. Multiple reasons why AS patients participate in physical activity were revealed that included both adaptive (i.e. autonomous) and maladaptive (i.e. controlled) forms of motivation.

scholarly journals Theta oscillations shift towards optimal frequency for cognitive control

2020 ◽  
Author(s):  
Mehdi Senoussi ◽  
Pieter Verbeke ◽  
Kobe Desender ◽  
Esther De Loof ◽  
Durk Talsma ◽  
...  
Keyword(s):  
Cognitive Control ◽  
Peak Frequency ◽  
Task Demands ◽  
Theta Oscillations ◽  
Neural Oscillations ◽  
Theta Band ◽  
Critical Aspect ◽  
Optimal Frequency ◽  
Electrophysiological Recordings ◽  
Neural Populations

AbstractCognitive control is supported by theta band (4-7Hz) neural oscillations coordinating neural populations for task implementation. Task performance has been shown to depend on theta amplitude but a second critical aspect of theta oscillations, its peak frequency, has mostly been overlooked. Using modelling, behavioral and electrophysiological recordings, we show that theta oscillations adapt to task demands by shifting towards the optimal frequency.

scholarly journals Mean Field and Capacity in Realistic Networks of Spiking Neurons Storing Sparsely Coded Random Memories

2004 ◽  
Vol 16 (12) ◽  
pp. 2597-2637 ◽  
Author(s):  
Emanuele Curti ◽  
Gianluigi Mongillo ◽  
Giancarlo La Camera ◽  
Daniel J. Amit
Keyword(s):  
Amplitude Ratio ◽  
Mean Field ◽  
Spiking Neurons ◽  
Repeated Presentation ◽  
Fixed Set ◽  
Neural Populations ◽  
The Mean ◽  
Activity State ◽  
Constitutive Parameters

Mean-field (MF) theory is extended to realistic networks of spiking neurons storing in synaptic couplings of randomly chosen stimuli of a given low coding level. The underlying synaptic matrix is the result of a generic, slow, long-term synaptic plasticity of two-state synapses, upon repeated presentation of the fixed set of the stimuli to be stored. The neural populations subtending the MF description are classified by the number of stimuli to which their neurons are responsive (multiplicity). This involves 2p + 1 populations for a network storing p memories. The computational complexity of the MF description is then significantly reduced by observing that at low coding levels (f), only a few populations remain relevant: the population of mean multiplicity –pf and those of multiplicity of order √pf around the mean. The theory is used to produce (predict) bifurcation diagrams (the onset of selective delay activity and the rates in its various stationary states) and to compute the storage capacity of the network (the maximal number of single items used in training for each of which the network can sustain a persistent, selective activity state). This is done in various regions of the space of constitutive parameters for the neurons and for the learning process. The capacity is computed in MF versus potentiation amplitude, ratio of potentiation to depression probability and coding level f. The MF results compare well with recordings of delay activity rate distributions in simulations of the underlying microscopic network of 10,000 neurons.

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