25 papers · ranked by Valyu relevance
Krishna V. Shenoy, Jonathan C. Kao
Neural recording technologies increasingly enable simultaneous measurement of neural activity from multiple brain areas. To gain insight into distributed neural computations, a commensurate advance in experimental and analytical methods is necessary. We discuss two opportunities towards this end: the manipulation and…
Tianwei Wang, Yun Chen, He Cui
In contrast to traditional representational perspectives in which the motor cortex is involved in motor control via neuronal preference for kinetics and kinematics, a dynamical system perspective emerging in the last decade views the motor cortex as a dynamical machine that generates motor commands by autonomous…
Jonathan C. Kao, Paul Nuyujukian, Stephen I. Ryu, Mark M. Churchland + 2 more
'John P. Cunningham' 'Krishna V. Shenoy'] Increasing evidence suggests that neural population responses have their own internal drive, or dynamics, that describe how the neural population evolves through time. An important prediction of neural dynamical models is that previously observed neural activity is informative…
Chia-Ying Lin, Ping-Han Chen, Hsiu-Hau Lin, Wen-Min Huang
Neurons convert external stimuli into action potentials, or spikes, and encode the contained information into the biological nervous system. Despite the complexity of neurons and the synaptic interactions in between, rate models are often adapted to describe neural encoding with modest success. However, it is not clear…
Ahana Gangopadhyay, Darshit Mehta, Shantanu Chakrabartty
In neuromorphic engineering, neural populations are generally modeled in a bottom-up manner, where individual neuron models are connected through synapses to form large-scale spiking networks. Alternatively, a top-down approach treats the process of spike generation and neural representation of excitation in the…
Evan D. Remington, Devika Narain, Eghbal A. Hosseini, Mehrdad Jazayeri
Sensorimotor computations can be flexibly adjusted according to internal states and contextual inputs. The mechanisms supporting this flexibility are not understood. Here, we tested the utility of a dynamical system perspective to approach this problem. In a dynamical system whose state is determined by interactions…
Yu-Juan Sun, Weimin Zhang
We propose a neural network model of multi-neuron interacting system that simulates neurons to interact each other through the surroundings of neuronal cell bodies. We physically model the neuronal cell surroundings, include the dendrites, the axons and the synapses as well as the surrounding glial cells, as a…
Giacomo Indiveri, Yulia Sandamirskaya
Artificial neural networks and computational neuroscience models have made tremendous progress, allowing computers to achieve impressive results in artificial intelligence (AI) applications, such as image recognition, natural language processing, or autonomous driving. Despite this remarkable progress, biological…
Szilvia Szeier, Henrik Jörntell
Behavior ultimately depends on the spatiotemporal patterns of the neuron population activity across the brain. Here we address the issue of how the evolving patterns of population activity can be governed by the intrinsically available mechanisms within the brain. We show how the control of the evolving activity can be…
Mahta Ramezanian Panahi, Germán Abrevaya, Jean-Christophe Gagnon-Audet, Vikram Voleti + 2 more
'Jean-Christophe Gagnon-Audet' 'Vikram Voleti' 'Irina Rish' 'Guillaume Dumas'] The principled design and discovery of biologically- and physically-informed models of neuronal dynamics has been advancing since the mid-twentieth century. Recent developments in artificial intelligence (AI) have accelerated this progress.…
Chenfei Zhang, Omer Revah, Fred Wolf, Andreas Neef
The information processing capabilities of large neuronal circuits form the basis of higher-level cognition. It is unknown how the constituent cells’ properties interact with the external stimuli to shape the network’s collective activity patterns and the associated computations. The dynamic gain function, a spectrally…
Chih-Hsu Huang, Chou-Ching K. Lin
Nowadays, building low-dimensional mean-field models of neuronal populations is still a critical issue in the computational neuroscience community, because their derivation is difficult for realistic networks of neurons with conductance-based interactions and spike-frequency adaptation that generate nonlinear…
Nicole Sandra-Yaffa Dumont, Andreas Stöckel, P. Michael Furlong, Madeleine Bartlett + 4 more
'Madeleine Bartlett' 'Chris Eliasmith' 'Terrence C. Stewart' 'Baingio Pinna' 'Amedeo D’Angiulli'] The Neural Engineering Framework (Eliasmith & Anderson, 2003) is a long-standing method for implementing high-level algorithms constrained by low-level neurobiological details. In recent years, this method has been…
A. V. Goltsev, Marinho A. Lopes, K.-E. Lee, J. F. F. Mendes
Brain rhythms contribute to every aspect of brain function. Here, we study critical and resonance phenomena that precede the emergence of brain rhythms. Using an analytical approach and simulations of a cortical circuit model of neural networks with stochastic neurons in the presence of noise, we show that spontaneous…
Blake J. Cook, Andre D. H. Peterson, Wessel Woldman, John R. Terry
Mathematical modelling of the macroscopic electrical activity of the brain is highly nontrivial and requires a detailed understanding of not only the associated mathematical techniques, but also the underlying physiology and anatomy. Neural field theory is a population-level approach to modelling the non-linear…
Qi Shi, Fang Han, Zhijie Wang, Caiyun Li
Rhythmic oscillations of neuronal network are actually kind of synchronous behaviors, which play an important role in neural systems. In this paper, the properties of excitement degree and oscillation frequency of excitatory bursting Hodkin-Huxley neuronal network which incorporates a synaptic learning rule are…
Hari Teja Kalidindi, Kevin P. Cross, Timothy P. Lillicrap, Mohsen Omrani + 3 more
Recent studies hypothesize that motor cortical (MC) dynamics are generated largely through its recurrent connections based on observations that MC activity exhibits rotational structure. However, behavioural and neurophysiological studies suggest that MC behaves like a feedback controller where continuous sensory…
Lara Escuain-Poole, Jordi Garcia-Ojalvo, Antonio J. Pons
Data assimilation, defined as the fusion of data with preexisting knowledge, is particularly suited to elucidating underlying phenomena from noisy/insufficient observations. Although this approach has been widely used in diverse fields, only recently have efforts been directed to problems in neuroscience, using mainly…
Radu Grosu
This paper shows that ResNets, NeuralODEs, and CT-RNNs, are particular neural regulatory networks (NRNs), a biophysical model for the nonspiking neurons encountered in small species, such as the C.elegans nematode, and in the retina of large species. Compared to ResNets, NeuralODEs and CT-RNNs, NRNs have an additional…
Authors not listed
This paper formally defines an operational isomorphism between spectral damping in molecular vibronic systems and neuromodulatory control in biological sensory systems. Without asserting causal continuity or physical identity across scales, we show that both domains instantiate the same class of output-selective…
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Accurate modeling of drug concentration--time (C--t) profiles is central to pharmacokinetics (PK) and plays a critical role in both early-stage compound selection and late-stage individualized dosing. Traditional PK model offer mechanistic interpretability but often rely on rigid assumptions, extensive…
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Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…
James Swift, Matthew Arran Turner, James Christopher Reynolds
A rapid headspace analysis method for the authenticity testing of whiskies of different brands and years was developed for a low cost, deployable atmospheric pressure ionisation mass spectrometer, which required minimal sample preparation. Principal component analysis was applied to the time-averaged mass spectra, the…
Authors not listed
Scanning ion conductance microscopy (SICM) offers non-contact, label-free imaging of live cells with nanometer-scale resolution. However, its conventional imaging mode is inherently slow due to repeated vertical scanning, limiting temporal resolution and causing inertial issues. Here, we present Scanning Counter Ion…
Emanuele Quattrocchi, Baptiste Py, Adeleke Maradesa, Quentin Meyer + 2 more
Electrochemical impedance spectroscopy (EIS) is a characterization technique widely used to evaluate the properties of electrochemical systems. The distribution of relaxation times (DRT) has emerged as a model-free alternative to equivalent circuits and physical models to circumvent the inherent challenges of EIS…