AIMC Topic: Neurons

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Visualizing a joint future of neuroscience and neuromorphic engineering.

Neuron
Recent research resolves the challenging problem of building biophysically plausible spiking neural models that are also capable of complex information processing. This advance creates new opportunities in neuroscience and neuromorphic engineering, w...

Unsupervised neural network models of the ventral visual stream.

Proceedings of the National Academy of Sciences of the United States of America
Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream. However, such networks have remained implausible as a model of the development of the ventral stream...

Functional differentiations in evolutionary reservoir computing networks.

Chaos (Woodbury, N.Y.)
We propose an extended reservoir computer that shows the functional differentiation of neurons. The reservoir computer is developed to enable changing of the internal reservoir using evolutionary dynamics, and we call it an evolutionary reservoir com...

Stability, bifurcation and phase-locking of time-delayed excitatory-inhibitory neural networks.

Mathematical biosciences and engineering : MBE
We study a model for a network of synaptically coupled, excitable neurons to identify the role of coupling delays in generating different network behaviors. The network consists of two distinct populations, each of which contains one excitatory-inhib...

3D computational cannula fluorescence microscopy enabled by artificial neural networks.

Optics express
Computational cannula microscopy (CCM) is a high-resolution widefield fluorescence imaging approach deep inside tissue, which is minimally invasive. Rather than using conventional lenses, a surgical cannula acts as a lightpipe for both excitation and...

Computation Through Neural Population Dynamics.

Annual review of neuroscience
Significant experimental, computational, and theoretical work has identified rich structure within the coordinated activity of interconnected neural populations. An emerging challenge now is to uncover the nature of the associated computations, how t...

Validation of a Convolutional Neural Network Model for Spike Transformation Using a Generalized Linear Model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Identification of causal relationships of neural activity is one of the most important problems in neuroscience and neural engineering. We show that a novel deep learning approach using a convolutional neural network to model output neural spike acti...

Frequency-dependent response in cortical network with periodic electrical stimulation.

Chaos (Woodbury, N.Y.)
Electrical stimulation can shape oscillations in brain activity. However, the mechanism of how periodic electrical stimulation modulates brain oscillations by time-delayed neural networks is poorly understood at present. To address this question, we ...

Dynamics and bifurcations in multistable 3-cell neural networks.

Chaos (Woodbury, N.Y.)
We disclose the generality of the intrinsic mechanisms underlying multistability in reciprocally inhibitory 3-cell circuits composed of simplified, low-dimensional models of oscillatory neurons, as opposed to those of a detailed Hodgkin-Huxley type [...

Deep neural networks capture texture sensitivity in V2.

Journal of vision
Deep convolutional neural networks (CNNs) trained on visual objects have shown intriguing ability to predict some response properties of visual cortical neurons. However, the factors (e.g., if the model is trained or not, receptive field size) and co...