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IEEE Trans Neural Netw Learn Syst 2021 Apr 12;PP. Epub 2021 Apr 12.

Spike-timing-dependent plasticity (STDP) is one of the most popular and deeply biologically motivated forms of unsupervised Hebbian-type learning. In this article, we propose a variant of STDP extended by an additional activation-dependent scale factor. The consequent learning rule is an efficient algorithm, which is simple to implement and applicable to spiking neural networks (SNNs). Read More

J Neurosci 2021 Apr 6. Epub 2021 Apr 6.

Department of Neurobiology, University of Utah.

Memory consolidation is thought to occur through protein synthesis-dependent synaptic plasticity mechanisms such as long-term potentiation (LTP). Dynamic changes in gene expression and epigenetic modifications underlie the maintenance of LTP. Similar mechanisms may mediate the storage of memory. Read More

Curr Biol 2021 Mar 26. Epub 2021 Mar 26.

Department of Biology, Brandeis University, Waltham, MA 02453, USA. Electronic address:

Correlation-based (Hebbian) forms of synaptic plasticity are crucial for the initial encoding of associative memories but likely insufficient to enable the stable storage of multiple specific memories within neural circuits. Theoretical studies have suggested that homeostatic synaptic normalization rules provide an essential countervailing force that can stabilize and expand memory storage capacity. Although such homeostatic mechanisms have been identified and studied for decades, experimental evidence that they play an important role in associative memory is lacking. Read More

Eur J Neurosci 2021 Mar 26. Epub 2021 Mar 26.

Department of Physiology, Yokohama City University Graduate School of Medicine, Yokohama, Japan.

The semaphorin family is a well-characterized family of secreted or membrane-bound proteins that are involved in activity-independent neurodevelopmental processes, such as axon guidance, cell migration, and immune functions. Although semaphorins have recently been demonstrated to regulate activity-dependent synaptic scaling, their roles in Hebbian synaptic plasticity as well as learning and memory remain poorly understood. Here, using a rodent model, we found that an inhibitory avoidance task, a hippocampus-dependent contextual learning paradigm, increased secretion of semaphorin 3A in the hippocampus. Read More

Front Syst Neurosci 2021 2;15:580569. Epub 2021 Mar 2.

Brain and Cognition Research Unit, KU Leuven, Leuven, Belgium.

Brain network connections rewire adaptively in response to neural activity. Adaptive rewiring may be understood as a process which, at its every step, is aimed at optimizing the efficiency of signal diffusion. In evolving model networks, this amounts to creating shortcut connections in regions with high diffusion and pruning where diffusion is low. Read More

PLoS Comput Biol 2021 Mar 2;17(3):e1007957. Epub 2021 Mar 2.

Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia.

There are two distinct classes of cells in the primary visual cortex (V1): simple cells and complex cells. One defining feature of complex cells is their spatial phase invariance; they respond strongly to oriented grating stimuli with a preferred orientation but with a wide range of spatial phases. A classical model of complete spatial phase invariance in complex cells is the energy model, in which the responses are the sum of the squared outputs of two linear spatially phase-shifted filters. Read More

Psychol Rev 2021 Feb 25. Epub 2021 Feb 25.

Department of Psychological & Brain Sciences, University of California.

This article introduces a biologically detailed computational model of how rule-guided behaviors become automatic. The model assumes that initially, rule-guided behaviors are controlled by a distributed neural network centered in the prefrontal cortex, and that in addition to initiating behavior, this network also trains a faster and more direct network that includes projections from sensory association cortex directly to rule-sensitive neurons in the premotor cortex. After much practice, the direct network is sufficient to control the behavior, without prefrontal involvement. Read More

Neural Comput 2021 Feb 22:1-29. Epub 2021 Feb 22.

John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, U.S.A.

We propose a novel biologically plausible solution to the credit assignment problem motivated by observations in the ventral visual pathway and trained deep neural networks. In both, representations of objects in the same category become progressively more similar, while objects belonging to different categories become less similar. We use this observation to motivate a layer-specific learning goal in a deep network: each layer aims to learn a representational similarity matrix that interpolates between previous and later layers. Read More

Front Neurorobot 2021 4;15:629652. Epub 2021 Feb 4.

Computational Robotics Laboratory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czechia.

A repetitive movement pattern of many animals, a gait, is controlled by the Central Pattern Generator (CPG), providing rhythmic control synchronous to the sensed environment. As a rhythmic signal generator, the CPG can control the motion phase of biomimetic legged robots without feedback. The CPG can also act in sensory synchronization, where it can be utilized as a sensory phase estimator. Read More

Cognition 2021 Feb 17:104621. Epub 2021 Feb 17.

Department of Psychology, UCLA, United States.

Learning often requires splitting continuous signals into recurring units, such as the discrete words constituting fluent speech; these units then need to be encoded in memory. A prominent candidate mechanism involves statistical learning of co-occurrence statistics like transitional probabilities (TPs), reflecting the idea that items from the same unit (e.g. Read More

Phys Rev E 2021 Jan;103(1-1):012315

PMI Lab, School of Physics, Sun Yat-sen University, Guangzhou 510275, People's Republic of China.

By controlling synaptic and neural correlations, deep learning has achieved empirical successes in improving classification performances. How synaptic correlations affect neural correlations to produce disentangled hidden representations remains elusive. Here we propose a simplified model of dimension reduction, taking into account pairwise correlations among synapses, to reveal the mechanism underlying how the synaptic correlations affect dimension reduction. Read More

Neurosci Biobehav Rev 2021 May 10;124:224-234. Epub 2021 Feb 10.

Queens College, City University of New York, Department of Psychology, Flushing, NY, United States. Electronic address:

A major goal in psychology is to understand how environmental stimuli associated with primary rewards come to function as conditioned stimuli, acquiring the capacity to elicit similar responses to those elicited by primary rewards. Our neurobiological model is predicated on the Hebbian idea that concurrent synaptic activity on the primary reward neural substrate-proposed to be ventral tegmental area (VTA) dopamine (DA) neurons-strengthens the synapses involved. We propose that VTA DA neurons receive both a strong unconditioned stimulus signal (acetylcholine stimulation of DA cells) from the primary reward capable of unconditionally activating DA cells and a weak stimulus signal (glutamate stimulation of DA cells) from the neutral stimulus. Read More

Int J Mol Sci 2021 Jan 30;22(3). Epub 2021 Jan 30.

Institute of Physiology, Medical Faculty, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany.

Alzheimer's disease (AD) is a multifaceted neurodegenerative disorder characterized by progressive and irreversible cognitive decline, with no disease-modifying therapy until today. Spike timing-dependent plasticity (STDP) is a Hebbian form of synaptic plasticity, and a strong candidate to underlie learning and memory at the single neuron level. Although several studies reported impaired long-term potentiation (LTP) in the hippocampus in AD mouse models, the impact of amyloid-β (Aβ) pathology on STDP in the hippocampus is not known. Read More

Dev Sci 2021 Feb 11:e13103. Epub 2021 Feb 11.

Department of Psychological Sciences, Perception, Action, Cognition (PAC) Division, University of Connecticut, Storrs, CT, USA.

Previous work suggests that auditory-vestibular interactions, which emerge during bodily movement to music, can influence the perception of musical rhythm. In a seminal study on the ontogeny of musical rhythm, Phillips-Silver and Trainor (2005) found that bouncing infants to an unaccented rhythm influenced infants' perceptual preferences for accented rhythms that matched the rate of bouncing. In the current study, we ask whether nascent, diffuse coupling between auditory and motor systems is sufficient to bootstrap short-term Hebbian plasticity in the auditory system and explain infants' preferences for accented rhythms thought to arise from auditory-vestibular interactions. Read More

Elife 2021 Feb 9;10. Epub 2021 Feb 9.

Lund Vision Group, Department of Biology, Lund University, Lund, Sweden.

Neural organization of mushroom bodies is largely consistent across insects, whereas the ancestral ground pattern diverges broadly across crustacean lineages resulting in successive loss of columns and the acquisition of domed centers retaining ancestral Hebbian-like networks and aminergic connections. We demonstrate here a major departure from this evolutionary trend in Brachyura, the most recent malacostracan lineage. In the shore crab , instead of occupying the rostral surface of the lateral protocerebrum, mushroom body calyces are buried deep within it with their columns extending outwards to an expansive system of gyri on the brain's surface. Read More

Front Neural Circuits 2020 22;14:620052. Epub 2021 Jan 22.

Section on Critical Brain Dynamics, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States.

Neuronal avalanches are scale-invariant neuronal population activity patterns in the cortex that emerge in the awake state and during balanced excitation and inhibition. Theory and experiments suggest that avalanches indicate a state of cortex that improves numerous aspects of information processing by allowing for the transient and selective formation of local as well as system-wide spanning neuronal groups. If avalanches are indeed involved with information processing, one might expect that single neurons would participate in avalanche patterns selectively. Read More

Neuron 2021 02;109(3):401-403

Department of Neuroscience, Physiology and Pharmacology, University College London, 21 University St., London WC1E 6DE, UK. Electronic address:

Torrado Pacheco et al. demonstrate that downward firing rate homeostasis occurs when cellular activity levels increase beyond baseline, but only during sleep-dense periods. In contrast, Hebbian-facilitated changes in firing rate occur independently of sleep and wake states. Read More

Cell Calcium 2021 03 23;94:102358. Epub 2021 Jan 23.

UK Dementia Research Institute, Department of Brain Sciences, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK. Electronic address:

Aging is a complex process that differentially impacts multiple cognitive, sensory, neuronal and molecular processes. Technological innovations now allow for parallel investigation of neuronal circuit function, structure and molecular composition in the brain of awake behaving adult mice. Thus, mice have become a critical tool to better understand how aging impacts the brain. Read More

Front Robot AI 2020 2;7:40. Epub 2020 Apr 2.

Embodied Cognitive Science Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan.

Modeling of complex adaptive systems has revealed a still poorly understood benefit of unsupervised learning: when neural networks are enabled to form an associative memory of a large set of their own attractor configurations, they begin to reorganize their connectivity in a direction that minimizes the coordination constraints posed by the initial network architecture. This self-optimization process has been replicated in various neural network formalisms, but it is still unclear whether it can be applied to biologically more realistic network topologies and scaled up to larger networks. Here we continue our efforts to respond to these challenges by demonstrating the process on the connectome of the widely studied nematode worm . Read More

Phys Rev Lett 2021 Jan;126(1):018301

SISSA, Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy.

We derive the Gardner storage capacity for associative networks of threshold linear units, and show that with Hebbian learning they can operate closer to such Gardner bound than binary networks, and even surpass it. This is largely achieved through a sparsification of the retrieved patterns, which we analyze for theoretical and empirical distributions of activity. As reaching the optimal capacity via nonlocal learning rules like back propagation requires slow and neurally implausible training procedures, our results indicate that one-shot self-organized Hebbian learning can be just as efficient. Read More

Epilepsia 2021 02 16;62(2):542-556. Epub 2021 Jan 16.

Medical Faculty, Institute for Experimental Epileptology and Cognition Research, University of Bonn, Bonn, Germany.

Objective: Many antiseizure drugs (ASDs) act on voltage-dependent sodium channels, and the molecular basis of these effects is well established. In contrast, how ASDs act on the level of neuronal networks is much less understood.

Methods: In the present study, we determined the effects of eslicarbazepine (S-Lic) on different types of inhibitory neurons, as well as inhibitory motifs. Read More

Neural Dev 2021 01 7;16(1). Epub 2021 Jan 7.

Institute of Neuroscience, Howard Hughes Medical Institute, University of Oregon, Eugene, OR, USA.

Neuronal networks are capable of undergoing rapid structural and functional changes called plasticity, which are essential for shaping circuit function during nervous system development. These changes range from short-term modifications on the order of milliseconds, to long-term rearrangement of neural architecture that could last for the lifetime of the organism. Neural plasticity is most prominent during development, yet also plays a critical role during memory formation, behavior, and disease. Read More

Neural Netw 2021 Mar 2;135:192-200. Epub 2021 Jan 2.

CEMS, School of Computing, University of Kent, CT2 7NF, Canterbury, UK.

We analyse mathematically the constraints on weights resulting from Hebbian and STDP learning rules applied to a spiking neuron with weight normalisation. In the case of pure Hebbian learning, we find that the normalised weights equal the promotion probabilities of weights up to correction terms that depend on the learning rate and are usually small. A similar relation can be derived for STDP algorithms, where the normalised weight values reflect a difference between the promotion and demotion probabilities of the weight. Read More

Biol Cybern 2021 Feb 5;115(1):43-57. Epub 2021 Jan 5.

Department of Psychological Sciences, Department of Physics and CT Institute for Brain and Cognitive Sciences, University of Connecticut, 406 Babbidge Road, Unit 1020, Storrs, CT, 06269, USA.

We study multifrequency Hebbian plasticity by analyzing phenomenological models of weakly connected neural networks. We start with an analysis of a model for single-frequency networks previously shown to learn and memorize phase differences between component oscillators. We then study a model for gradient frequency neural networks (GrFNNs) which extends the single-frequency model by introducing frequency detuning and nonlinear coupling terms for multifrequency interactions. Read More

ACS Appl Mater Interfaces 2021 Feb 5;13(4):5216-5227. Epub 2021 Jan 5.

School of Electrical Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea.

MXene, a new state-of-the-art two-dimensional (2D) nanomaterial, has attracted considerable interest from both industry and academia because of its excellent electrical, mechanical, and chemical properties. However, MXene-based device engineering has rarely been reported. In this study, we explored TiC MXene for digital and analog computing applications by engineering the top electrode. Read More

Brain Commun 2020 27;2(2):fcaa203. Epub 2020 Nov 27.

Department of Psychology, University of Arizona, Tucson, AZ 85721, USA.

Homoeostatic metaplasticity is a neuroprotective physiological feature that counterbalances Hebbian forms of plasticity to prevent network destabilization and hyperexcitability. Recent animal models highlight dysfunctional homoeostatic metaplasticity in the pathogenesis of Alzheimer's disease. However, the association between homoeostatic metaplasticity and cognitive status has not been systematically characterized in either demented or non-demented human populations, and the potential value of homoeostatic metaplasticity as an early biomarker of cognitive impairment has not been explored in humans. Read More

Brain Lang 2021 Feb 22;213:104893. Epub 2020 Dec 22.

CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China. Electronic address:

Although there are considerable individual differences in eye movements during text reading, their neural correlates remain unclear. In this study, we investigated the relationship between the first-pass fixation duration (FPFD) in natural reading and resting-state functional connectivity (RSFC) in the brain. We defined the brain regions associated with early visual processing, word identification, attention shifts, and oculomotor control as seed regions. Read More

Evol Comput 2020 Dec 22:1-25. Epub 2020 Dec 22.

Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, 5612 AP, the Netherlands

A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons. However, the emergence of a coherent global learning behavior from local Hebbian plasticity rules is not very well understood. Read More