| | Models | Description |
| 1. |
Impact of Small Time Delays on the Onset of Oscillations and Synchrony (Al-Darabsah et al 2021)
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Code to compute Lyapunov Exponents with Delay in the Wilson-Cowan system:
Al-Darabsah, I., Chen, L., Nicola, W., & Campbell, S. A. (2021). The Impact of Small Time Delays on the Onset of Oscillations and Synchrony in Brain Networks. Frontiers in Systems Neuroscience, 58. |
| 2. |
A multiscale approach to analyze circadian rhythms (Vasalou & Henson, 2010) (CellML)
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" ... We developed a firing rate code model to incorporate known
electrophysiological properties of SCN (suprachiasmatic nucleus)
pacemaker cells, including circadian dependent changes in membrane
voltage and ion conductances. Calcium dynamics were included in the
model as the putative link between electrical firing and gene
expression. Individual ion currents exhibited oscillatory patterns
matching experimental data both in current levels and phase
relationships. VIP and GABA neurotransmitters, which encode synaptic
signals across the SCN, were found to play critical roles in daily
oscillations of membrane excitability and gene expression. Blocking
various mechanisms of intracellular calcium accumulation by simulated
pharmacological agents (nimodipine, IP3- and ryanodine-blockers)
reproduced experimentally observed trends in firing rate dynamics and
core-clock gene transcription. The intracellular calcium concentration
was shown to regulate diverse circadian processes such as firing
frequency, gene expression and system periodicity. The model predicted
a direct relationship between firing frequency and gene expression
amplitudes, demonstrated the importance of intracellular pathways for
single cell behavior and provided a novel multiscale framework which
captured characteristics of the SCN at both the electrophysiological
and gene regulatory levels." |
| 3. |
A multiscale approach to analyze circadian rhythms (Vasalou & Henson, 2010) (SBML)
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" ... We developed a firing rate code model to incorporate known
electrophysiological properties of SCN (suprachiasmatic nucleus)
pacemaker cells, including circadian dependent changes in membrane
voltage and ion conductances. Calcium dynamics were included in the
model as the putative link between electrical firing and gene
expression. Individual ion currents exhibited oscillatory patterns
matching experimental data both in current levels and phase
relationships. VIP and GABA neurotransmitters, which encode synaptic
signals across the SCN, were found to play critical roles in daily
oscillations of membrane excitability and gene expression. Blocking
various mechanisms of intracellular calcium accumulation by simulated
pharmacological agents (nimodipine, IP3- and ryanodine-blockers)
reproduced experimentally observed trends in firing rate dynamics and
core-clock gene transcription. The intracellular calcium concentration
was shown to regulate diverse circadian processes such as firing
frequency, gene expression and system periodicity. The model predicted
a direct relationship between firing frequency and gene expression
amplitudes, demonstrated the importance of intracellular pathways for
single cell behavior and provided a novel multiscale framework which
captured characteristics of the SCN at both the electrophysiological
and gene regulatory levels." |
| 4. |
Basal Ganglia and Levodopa Pharmacodynamics model for parameter estimation in PD (Ursino et al 2020)
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Parkinson disease (PD) is characterized by a clear beneficial motor response to levodopa (LD) treatment. However, with disease progression and longer LD exposure, drug-related motor fluctuations usually occur. Recognition of the individual relationship between LD concentration and its effect may be difficult, due to the complexity and variability of the mechanisms involved. This work proposes an innovative procedure for the automatic estimation of LD pharmacokinetics and pharmacodynamics parameters, by a biologically-inspired mathematical model. An original issue, compared with previous similar studies, is that the model comprises not only a compartmental description of LD pharmacokinetics in plasma and its effect on the striatal neurons, but also a neurocomputational model of basal ganglia action selection. Parameter estimation was achieved on 26 patients (13 with stable and 13 with fluctuating LD response) to mimic plasma LD concentration and alternate finger tapping frequency along four hours after LD administration, automatically minimizing a cost function of the difference between simulated and clinical data points. Results show that individual data can be satisfactorily simulated in all patients and that significant differences exist in the estimated parameters between the two groups. Specifically, the drug removal rate from the effect compartment, and the Hill coefficient of the concentration-effect relationship were significantly higher in the fluctuating than in the stable group.
The model, with individualized parameters, may be used to reach a deeper comprehension of the PD mechanisms, mimic the effect of medication, and, based on the predicted neural responses, plan the correct management and design innovative therapeutic procedures. |
| 5. |
Bump Attractor Models: Delayed Response & Recognition Span - spatial condition (Ibanez et al 2019)
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The archive contains examples of two spatial working memory tasks: the Delayed Response Task (DRT) or oculomotor task & the Delayed Recognition Span Task in the spatial condition (DRSTsp).
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| 6. |
Coding explains development of binocular vision and its failure in Amblyopia (Eckmann et al 2020)
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This is the MATLAB code for the Active Efficient Coding model introduced in Eckmann et al 2020.
It simulates an agent that self-calibrates vergence and accommodation eye movements in a simple visual environment. All algorithms are explained in detail in the main manuscript and the supplementary material of the paper. |
| 7. |
Cortico - Basal Ganglia Loop (Mulcahy et al 2020)
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The model represents learning and reversal tasks and shows performance in control, Parkinsonian and Huntington disease conditions |
| 8. |
Generation of stable heading representations in diverse visual scenes (Kim et al 2019)
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"Many animals rely on an internal heading representation when
navigating in varied environments. How this
representation is linked to the sensory cues that define different
surroundings is unclear. In the fly brain, heading is represented by
‘compass’ neurons that innervate a ring-shaped structure known as the
ellipsoid body. Each compass neuron receives inputs from ‘ring’
neurons that are selective for particular visual features;
this combination provides an ideal substrate for the extraction of
directional information from a visual scene. Here we combine
two-photon calcium imaging and optogenetics in tethered flying flies
with circuit modelling, and show how the correlated activity of
compass and visual neurons drives plasticity, which
flexibly transforms two-dimensional visual cues into a stable heading
representation. ... "
See the supplementary information for model details. |
| 9. |
Hierarchical anti-Hebbian network model for the formation of spatial cells in 3D (Soman et al 2019)
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This model shows how spatial representations in 3D space could emerge using unsupervised neural networks. Model is a hierarchical one which means that it has multiple layers, where each layer has got a specific function to achieve. This architecture is more of a generalised one i.e. it gives rise to different kinds of spatial representations after training. |
| 10. |
Inhibitory neuron plasticity as a mechanism for ocular dominance plasticity (Bono & Clopath 2019)
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"Ocular dominance plasticity is a well-documented phenomenon
allowing us to study properties of cortical
maturation. Understanding this maturation might be an important
step towards unravelling how cortical circuits function. However,
it is still not fully understood which mechanisms are responsible
for the opening and closing of the critical period for ocular
dominance and how changes in cortical responsiveness arise after
visual deprivation. In this article, we present a theory of
ocular dominance plasticity. Following recent experimental work,
we propose a framework where a reduction in inhibition is
necessary for ocular dominance plasticity in both juvenile and
adult animals. In this framework, two ingredients are crucial to
observe ocular dominance shifts: a sufficient level of inhibition
as well as excitatory-to-inhibitory synaptic plasticity. In our
model, the former is responsible for the opening of the critical
period, while the latter limits the plasticity in adult
animals. Finally, we also provide a possible explanation for the
variability in ocular dominance shifts observed in individual
neurons and for the counter-intuitive shifts towards the closed
eye." |
| 11. |
Interplay between somatic and dendritic inhibition promotes place fields (Pedrosa & Clopath 2020)
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Hippocampal pyramidal neurons are thought to encode spatial information. A subset of these cells, named place cells, are active only when the animal traverses a specific region within the environment. Although vastly studied experimentally, the development and stabilization of place fields are not fully understood. Here, we propose a mechanistic model of place cell formation in the hippocampal CA1 region. Using our model, we reproduce place field dynamics observed experimentally and provide a mechanistic explanation for the stabilization of place fields. Finally, our model provides specific predictions on protocols to shift place field location. |
| 12. |
Large-scale laminar model of macaque cortex (Mejias et al 2016)
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This code reproduces the large-scale cortical model with laminar structure presented in Mejias et al., Science Advances 2016. The model includes different scales (intra-laminar, inter-laminar, inter-areal and large-scale) across macaque neocortex and reproduces experimentally observed dynamics of gamma and alpha/beta oscillations across these scales. It makes use of real anatomical data from the macaque cortex. Some parts of the code require external packages or data (see readme file for details). |
| 13. |
Modelling gain modulation in stability-optimised circuits (Stroud et al 2018)
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We supply Matlab code to create 'stability-optimised circuits'. These networks can give rise to rich neural activity transients that resemble primary motor cortex recordings in monkeys during reaching. We also supply code that allows one to learn new network outputs by changing the input-output gain of neurons in a stability-optimised network. Our code recreates the main results of Figure 1 in our related publication. |
| 14. |
Modulation of cortical Up-Down state switching by astrocytes (Moyse & Berry, 2022)
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Recent experimental studies have suggested that the astrocytes of the local network can actually control the emergence of Up-Down regimes. Here we propose and study a neural net-
work model to explore the implication of astrocytes in this dynamical phenomenon. We consider three populations of cells: excitatory neurons, inhibitory neurons and astrocytes, interconnected by gliotransmission events, from neurons to astrocytes and back. We derive two models for this three-population system: a rate model and a stochastic
spiking neural network with thousands of neurons and astrocytes. In numerical simulations of these three-population models, the presence of astrocytes is indeed observed
to promote the emergence of Up-Down regimes with realistic characteristics. |
| 15. |
NN for proto-object based contour integration and figure-ground segregation (Hu & Niebur 2017)
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"Visual processing of objects makes use of both
feedforward and feedback streams of information. However, the nature of feedback signals is largely unknown, as
is the identity of the neuronal populations in lower visual
areas that receive them. Here, we develop a recurrent neural
model to address these questions in the context of contour
integration and figure-ground segregation. A key feature
of our model is the use of grouping neurons whose activity represents tentative objects (“proto-objects”) based on
the integration of local feature information. Grouping neurons receive input from an organized set of local feature
neurons, and project modulatory feedback to those same
neurons. ..." |
| 16. |
Plasticity forms non-overlapping adjacent ON and OFF RFs in cortical neurons (Sollini et al 2018)
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Hebbian plasticity of a feedforward network modelling ON-OFF receptive field changes in auditory cortex. |
| 17. |
Recurrent amplification of grid-cell activity (D'Albis and Kempter 2020)
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| 18. |
Stochastic and periodic inputs tune ongoing oscillations (Hutt et al. 2016)
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" ... We here analyze a network of recurrently connected spiking neurons with time delay displaying stable synchronous dynamics. Using mean-field and stability analyses, we investigate the influence of dynamic inputs on the frequency of firing rate oscillations. ..." |
| 19. |
Towards a biologically plausible model of LGN-V1 pathways (Lian et al 2019)
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"Increasing evidence supports the hypothesis that the visual system
employs a sparse code to represent visual stimuli, where information
is encoded in an efficient way by a small population of cells that
respond to sensory input at a given time. This includes simple cells
in primary visual cortex (V1), which are defined by their linear
spatial integration of visual stimuli. Various models of sparse coding
have been proposed to explain physiological phenomena observed in
simple cells. However, these models have usually made the simplifying
assumption that inputs to simple cells already incorporate linear
spatial summation. This overlooks the fact that these inputs are known
to have strong non-linearities such as the separation of ON and OFF
pathways, or separation of excitatory and inhibitory
neurons. Consequently these models ignore a range of important
experimental phenomena that are related to the emergence of linear
spatial summation from non-linear inputs, such as segregation of ON
and OFF sub-regions of simple cell receptive fields, the push-pull
effect of excitation and inhibition, and phase-reversed
cortico-thalamic feedback. Here, we demonstrate that a two-layer model
of the visual pathway from the lateral geniculate nucleus to V1 that
incorporates these biological constraints on the neural circuits and
is based on sparse coding can account for the emergence of these
experimental phenomena, diverse shapes of receptive fields and
contrast invariance of orientation tuning of simple cells when the
model is trained on natural images. The model suggests that sparse
coding can be implemented by the V1 simple cells using neural circuits
with a simple biologically plausible architecture." |
