AIMC Topic: Models, Neurological

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Scalability of Large Neural Network Simulations via Activity Tracking With Time Asynchrony and Procedural Connectivity.

Neural computation
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...

Permitted Sets and Convex Coding in Nonthreshold Linear Networks.

Neural computation
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...

Extended Anderson Criticality in Heavy-Tailed Neural Networks.

Physical review letters
We investigate the emergence of complex dynamics in networks with heavy-tailed connectivity by developing a non-Hermitian random matrix theory. We uncover the existence of an extended critical regime of spatially multifractal fluctuations between the...

Noise-mitigation strategies in physical feedforward neural networks.

Chaos (Woodbury, N.Y.)
Physical neural networks are promising candidates for next generation artificial intelligence hardware. In such architectures, neurons and connections are physically realized and do not leverage digital concepts with their practically infinite signal...

[A spatial localization model of mobile robot based on entorhinal-hippocampal cognitive mechanism in rat brain].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Physiological studies reveal that rats rely on multiple spatial cells for spatial navigation and memory. In this paper, we investigated the firing mechanism of spatial cells within the entorhinal-hippocampal structure of the rat brain and proposed a ...

Learning to represent continuous variables in heterogeneous neural networks.

Cell reports
Animals must monitor continuous variables such as position or head direction. Manifold attractor networks-which enable a continuum of persistent neuronal states-provide a key framework to explain this monitoring ability. Neural networks with symmetri...

Coherent oscillations in balanced neural networks driven by endogenous fluctuations.

Chaos (Woodbury, N.Y.)
We present a detailed analysis of the dynamical regimes observed in a balanced network of identical quadratic integrate-and-fire neurons with sparse connectivity for homogeneous and heterogeneous in-degree distributions. Depending on the parameter va...

Surrogate gradients for analog neuromorphic computing.

Proceedings of the National Academy of Sciences of the United States of America
To rapidly process temporal information at a low metabolic cost, biological neurons integrate inputs as an analog sum, but communicate with spikes, binary events in time. Analog neuromorphic hardware uses the same principles to emulate spiking neural...

A connectivity-constrained computational account of topographic organization in primate high-level visual cortex.

Proceedings of the National Academy of Sciences of the United States of America
Inferotemporal (IT) cortex in humans and other primates is topographically organized, containing multiple hierarchically organized areas selective for particular domains, such as faces and scenes. This organization is commonly viewed in terms of evol...

The structural aspects of neural dynamics and information flow.

Frontiers in bioscience (Landmark edition)
BACKGROUND: Neurons have specialized structures that facilitate information transfer using electrical and chemical signals. Within the perspective of neural computation, the neuronal structure is an important prerequisite for the versatile computatio...