Learning associations between co-occurring events enables us to extract structure from our environment. Medial-temporal lobe structures are critical for associative learning. However, the role of the ventral visual pathway (VVP) in associative learni...
Conditioning, how animals learn to associate two or more events, is one of the most influential paradigms in learning theory. It is nevertheless unclear how current models of associative learning can accommodate complex phenomena without ad hoc repre...
IEEE transactions on neural networks and learning systems
27529878
In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation function. The weight parameters of neural networks are acquired by a set of inequalities without the...
The attractor neural network scenario is a popular scenario for memory storage in the association cortex, but there is still a large gap between models based on this scenario and experimental data. We study a recurrent network model in which both lea...
The Journal of neuroscience : the official journal of the Society for Neuroscience
30006366
Animals successfully thrive in noisy environments with finite resources. The necessity to function with resource constraints has led evolution to design animal brains (and bodies) to be optimal in their use of computational power while being adaptabl...
IEEE transactions on neural networks and learning systems
30703044
In this paper, we introduce a neural network (NN) model named clone-based neural network (CbNN) to design associative memories. Neurons in CbNN can be cloned statically or dynamically which allows to increase the number of data that can be stored and...
Neural networks : the official journal of the International Neural Network Society
30780044
This paper presents a deep associative neural network (DANN) based on unsupervised representation learning for associative memory. In brain, the knowledge is learnt by associating different types of sensory data, such as image and voice. The associat...
Neural networks : the official journal of the International Neural Network Society
31195207
This article proposes a biologically inspired neurocomputational architecture which learns associations between words and referents in different contexts, considering evidence collected from the literature of Psycholinguistics and Neurolinguistics. T...
What are the principles that govern whether neural representations move apart (differentiate) or together (integrate) as a function of learning? According to supervised learning models that are trained to predict outcomes in the world, integration sh...
The Journal of neuroscience : the official journal of the Society for Neuroscience
31270161
The ability of neural networks to associate successive states of network activity lies at the basis of many cognitive functions. Hence, we hypothesized that many ubiquitous structural and dynamical properties of local cortical networks result from as...