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Models, Neurological

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Coherent noise enables probabilistic sequence replay in spiking neuronal networks.

PLoS computational biology
Animals rely on different decision strategies when faced with ambiguous or uncertain cues. Depending on the context, decisions may be biased towards events that were most frequently experienced in the past, or be more explorative. A particular type o...

Automatized offline and online exploration to achieve a target dynamics in biohybrid neural circuits built with living and model neurons.

Neural networks : the official journal of the International Neural Network Society
Biohybrid circuits of interacting living and model neurons are an advantageous means to study neural dynamics and to assess the role of specific neuron and network properties in the nervous system. Hybrid networks are also a necessary step to build e...

Effective and efficient neural networks for spike inference from calcium imaging.

Cell reports methods
Calcium imaging provides advantages in monitoring large populations of neuronal activities simultaneously. However, it lacks the signal quality provided by neural spike recording in traditional electrophysiology. To address this issue, we developed a...

Meta-learning biologically plausible plasticity rules with random feedback pathways.

Nature communications
Backpropagation is widely used to train artificial neural networks, but its relationship to synaptic plasticity in the brain is unknown. Some biological models of backpropagation rely on feedback projections that are symmetric with feedforward connec...

A virtuous cycle between invertebrate and robotics research: perspective on a decade of Living Machines research.

Bioinspiration & biomimetics
Many invertebrates are ideal model systems on which to base robot design principles due to their success in solving seemingly complex tasks across domains while possessing smaller nervous systems than vertebrates. Three areas are particularly relevan...

Emergence of time persistence in a data-driven neural network model.

eLife
Establishing accurate as well as interpretable models of network activity is an open challenge in systems neuroscience. Here, we infer an energy-based model of the anterior rhombencephalic turning region (ARTR), a circuit that controls zebrafish swim...

Bayesian inference in ring attractor networks.

Proceedings of the National Academy of Sciences of the United States of America
Working memories are thought to be held in attractor networks in the brain. These attractors should keep track of the uncertainty associated with each memory, so as to weigh it properly against conflicting new evidence. However, conventional attracto...

Energy-efficiency computing of up and down transitions in a neural network.

Journal of neurophysiology
Spontaneous periodic up and down transitions of membrane potentials are considered to be a significant spontaneous activity of slow-wave sleep. Previous theoretical studies have shown that stimulation frequency and the dynamics of intrinsic currents ...

Bayesian reconstruction of memories stored in neural networks from their connectivity.

PLoS computational biology
The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible to reconstruct the information stor...

Relating local connectivity and global dynamics in recurrent excitatory-inhibitory networks.

PLoS computational biology
How the connectivity of cortical networks determines the neural dynamics and the resulting computations is one of the key questions in neuroscience. Previous works have pursued two complementary approaches to quantify the structure in connectivity. O...