AIMC Topic: Models, Neurological

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Mean-Field Approximations With Adaptive Coupling for Networks With Spike-Timing-Dependent Plasticity.

Neural computation Aug 7, 2023
Understanding the effect of spike-timing-dependent plasticity (STDP) is key to elucidating how neural networks change over long timescales and to design interventions aimed at modulating such networks in neurological disorders. However, progress is r...
Neural Networks, Computer Action Potentials Neurons Neuronal Plasticity Models, Neurological
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Neural Networks for Navigation: From Connections to Computations.

Annual review of neuroscience Jul 10, 2023
Many animals can navigate toward a goal they cannot see based on an internal representation of that goal in the brain's spatial maps. These maps are organized around networks with stable fixed-point dynamics (attractors), anchored to landmarks, and r...
Connectome Models, Neurological Head Neural Networks, Computer Learning Brain Animals
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An efficient deep learning approach to identify dynamics in in vitro neural networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2023
Understanding and discriminating the spatiotemporal patterns of activity generated by in vitro and in vivo neuronal networks is a fundamental task in neuroscience and neuroengineering. The state-of-the-art algorithms to describe the neuronal activity...
Neural Networks, Computer Algorithms Humans Models, Neurological Action Potentials Deep Learning
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NeuroAI: If grid cells are the answer, is path integration the question?

Current biology : CB Mar 13, 2023
Spatially modulated neurons known as grid cells are thought to play an important role in spatial cognition. A new study has found that units with grid-cell-like properties can emerge within artificial neural networks trained to path integrate, and de...
Neurons Space Perception Action Potentials Entorhinal Cortex Neural Networks, Computer Learning Cognition Models, Neurological
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An Area- and Energy-Efficient Spiking Neural Network With Spike-Time-Dependent Plasticity Realized With SRAM Processing-in-Memory Macro and On-Chip Unsupervised Learning.

IEEE transactions on biomedical circuits and systems Feb 1, 2023
In this article, we present a spiking neural network (SNN) based on both SRAM processing-in-memory (PIM) macro and on-chip unsupervised learning with Spike-Time-Dependent Plasticity (STDP). Co-design of algorithm and hardware for hardware-friendly SN...
Models, Neurological Unsupervised Machine Learning Algorithms Neuronal Plasticity Neural Networks, Computer
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Macroscopic dynamics of neural networks with heterogeneous spiking thresholds.

Physical review. E Feb 1, 2023
Mean-field theory links the physiological properties of individual neurons to the emergent dynamics of neural population activity. These models provide an essential tool for studying brain function at different scales; however, for their application ...
Neurons Adaptation, Physiological Action Potentials Neural Networks, Computer Models, Neurological
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Spiking Neural Networks and Mathematical Models.

Advances in experimental medicine and biology Jan 1, 2023
Neural networks are applied in various scientific fields such as medicine, engineering, pharmacology, etc. Investigating operations of neural networks refers to estimating the relationship among single neurons and their contributions to the network a...
Neural Networks, Computer Brain Models, Neurological Action Potentials Neurons
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Neural Information Processing and Computations of Two-Input Synapses.

Neural computation Sep 12, 2022
Information processing in artificial neural networks is largely dependent on the nature of neuron models. While commonly used models are designed for linear integration of synaptic inputs, accumulating experimental evidence suggests that biological n...
Synapses Artificial Intelligence Synaptic Transmission Models, Neurological Neurons
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Scalability of Large Neural Network Simulations via Activity Tracking With Time Asynchrony and Procedural Connectivity.

Neural computation Aug 16, 2022
We present a new algorithm to efficiently simulate random models of large neural networks satisfying the property of time asynchrony. The model parameters (average firing rate, number of neurons, synaptic connection probability, and postsynaptic dura...
Animals Synapses Models, Neurological Neural Networks, Computer Action Potentials Computer Simulation Humans Mammals
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Permitted Sets and Convex Coding in Nonthreshold Linear Networks.

Neural computation Aug 16, 2022
Hebbian theory proposes that ensembles of neurons form a basis for neural processing. It is possible to gain insight into the activity patterns of these neural ensembles through a binary analysis, regarding neurons as either active or inactive. The f...
Neural Networks, Computer Algorithms Models, Neurological Neurons
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