scholarly journals Hebbian learning and predictive mirror neurons for actions, sensations and emotions

2014 ◽  
Vol 369 (1644) ◽  
pp. 20130175 ◽  
Author(s):  
Christian Keysers ◽  
Valeria Gazzola
Keyword(s):  
Motor System ◽  
Mirror Neurons ◽  
Hebbian Learning ◽  
Learning Rule ◽  
Spike Timing ◽  
The Self ◽  
Joint Actions ◽  
Hebbian Learning Rule ◽  
Postsynaptic Activity ◽  
Neurophysiological Basis

Spike-timing-dependent plasticity is considered the neurophysiological basis of Hebbian learning and has been shown to be sensitive to both contingency and contiguity between pre- and postsynaptic activity. Here, we will examine how applying this Hebbian learning rule to a system of interconnected neurons in the presence of direct or indirect re-afference (e.g. seeing/hearing one's own actions) predicts the emergence of mirror neurons with predictive properties. In this framework, we analyse how mirror neurons become a dynamic system that performs active inferences about the actions of others and allows joint actions despite sensorimotor delays. We explore how this system performs a projection of the self onto others, with egocentric biases to contribute to mind-reading. Finally, we argue that Hebbian learning predicts mirror-like neurons for sensations and emotions and review evidence for the presence of such vicarious activations outside the motor system.

scholarly journals Synaptic memory devices from CoO/Nb:SrTiO 3 junction

2019 ◽  
Vol 6 (4) ◽  
pp. 181098 ◽  
Author(s):  
Le Zhao ◽  
Jie Xu ◽  
Xiantao Shang ◽  
Xue Li ◽  
Qiang Li ◽  
...  
Keyword(s):  
Large Scale ◽  
Hebbian Learning ◽  
Learning Rule ◽  
Charge Trapping ◽  
Spike Timing ◽  
Hebbian Learning Rule ◽  
Synaptic Functions ◽  
Neuromorphic System ◽  
Reduce Energy Consumption ◽  
Novel Design

Non-volatile memristors are promising for future hardware-based neurocomputation application because they are capable of emulating biological synaptic functions. Various material strategies have been studied to pursue better device performance, such as lower energy cost, better biological plausibility, etc. In this work, we show a novel design for non-volatile memristor based on CoO/Nb:SrTiO 3 heterojunction. We found the memristor intrinsically exhibited resistivity switching behaviours, which can be ascribed to the migration of oxygen vacancies and charge trapping and detrapping at the heterojunction interface. The carrier trapping/detrapping level can be finely adjusted by regulating voltage amplitudes. Gradual conductance modulation can therefore be realized by using proper voltage pulse stimulations. And the spike-timing-dependent plasticity, an important Hebbian learning rule, has been implemented in the device. Our results indicate the possibility of achieving artificial synapses with CoO/Nb:SrTiO 3 heterojunction. Compared with filamentary type of the synaptic device, our device has the potential to reduce energy consumption, realize large-scale neuromorphic system and work more reliably, since no structural distortion occurs.

Inheritance of Hippocampal Place Fields Through Hebbian Learning: Effects of Theta Modulation and Phase Precession on Structure Formation

2015 ◽  
Vol 27 (8) ◽  
pp. 1624-1672 ◽  
Author(s):  
Tiziano D’Albis ◽  
Jorge Jaramillo ◽  
Henning Sprekeler ◽  
Richard Kempter
Keyword(s):  
Hebbian Learning ◽  
Learning Rule ◽  
Place Cells ◽  
Spike Timing ◽  
Upstream Region ◽  
Place Field ◽  
Hebbian Learning Rule ◽  
Phase Precession ◽  
Place Fields ◽  
Field Formation

A place cell is a neuron that fires whenever the animal traverses a particular location of the environment—the place field of the cell. Place cells are found in two regions of the rodent hippocampus: CA3 and CA1. Motivated by the anatomical connectivity between these two regions and by the evidence for synaptic plasticity at these connections, we study how a place field in CA1 can be inherited from an upstream region such as CA3 through a Hebbian learning rule, in particular, through spike-timing-dependent plasticity (STDP). To this end, we model a population of CA3 place cells projecting to a single CA1 cell, and we assume that the CA1 input synapses are plastic according to STDP. With both numerical and analytical methods, we show that in the case of overlapping CA3 input place fields, the STDP learning rule leads to the formation of a place field in CA1. We then investigate the roles of the hippocampal theta modulation and phase precession on the inheritance process. We find that theta modulation favors the inheritance and leads to faster place field formation whereas phase precession changes the drift of CA1 place fields over time.

ASSOCIATIVE MEMORY IN NEURAL NETWORKS WITH THE HEBBIAN LEARNING RULE

1989 ◽  
Vol 03 (07) ◽  
pp. 555-560 ◽  
Author(s):  
M.V. TSODYKS
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Hebbian Learning ◽  
Learning Rule ◽  
Network Activity ◽  
Hebbian Learning Rule ◽  
Level Of Activity ◽  
The Mean ◽  
Full Network ◽  
Simultaneous Activity

We consider the Hopfield model with the most simple form of the Hebbian learning rule, when only simultaneous activity of pre- and post-synaptic neurons leads to modification of synapse. An extra inhibition proportional to full network activity is needed. Both symmetric nondiluted and asymmetric diluted networks are considered. The model performs well at extremely low level of activity p<K−1/2, where K is the mean number of synapses per neuron.

How the Shape of Pre- and Postsynaptic Signals Can Influence STDP: A Biophysical Model

2004 ◽  
Vol 16 (3) ◽  
pp. 595-625 ◽  
Author(s):  
Ausra Saudargiene ◽  
Bernd Porr ◽  
Florentin Wörgötter
Keyword(s):  
Membrane Potential ◽  
Weight Change ◽  
Hebbian Learning ◽  
Dendritic Tree ◽  
Learning Rule ◽  
Long Term Potentiation ◽  
Spike Timing ◽  
Biophysical Model ◽  
Nmda Channel

Spike-timing-dependent plasticity (STDP) is described by long-term potentiation (LTP), when a presynaptic event precedes a postsynaptic event, and by long-term depression (LTD), when the temporal order is reversed. In this article, we present a biophysical model of STDP based on a differential Hebbian learning rule (ISO learning). This rule correlates presynaptically the NMDA channel conductance with the derivative of the membrane potential at the synapse as the postsynaptic signal. The model is able to reproduce the generic STDP weight change characteristic. We find that (1) The actual shape of the weight change curve strongly depends on the NMDA channel characteristics and on the shape of the membrane potential at the synapse. (2) The typical antisymmetrical STDP curve (LTD and LTP) can become similar to a standard Hebbian characteristic (LTP only) without having to change the learning rule. This occurs if the membrane depolarization has a shallow onset and is long lasting. (3) It is known that the membrane potential varies along the dendrite as a result of the active or passive backpropagation of somatic spikes or because of local dendritic processes. As a consequence, our model predicts that learning properties will be different at different locations on the dendritic tree. In conclusion, such site-specific synaptic plasticity would provide a neuron with powerful learning capabilities.

A Nonlinear Hebbian Network that Learns to Detect Disparity in Random-Dot Stereograms

1996 ◽  
Vol 8 (3) ◽  
pp. 545-566 ◽  
Author(s):  
Christopher W. Lee ◽  
Bruno A. Olshausen
Keyword(s):  
Hebbian Learning ◽  
Learning Rule ◽  
Coincidence Detection ◽  
Input Stream ◽  
Hebbian Learning Rule ◽  
Pairwise Correlations ◽  
Input Layer ◽  
Random Dot Stereograms ◽  
Rule Analysis ◽  
Shed Light

An intrinsic limitation of linear, Hebbian networks is that they are capable of learning only from the linear pairwise correlations within an input stream. To explore what higher forms of structure could be learned with a nonlinear Hebbian network, we constructed a model network containing a simple form of nonlinearity and we applied it to the problem of learning to detect the disparities present in random-dot stereograms. The network consists of three layers, with nonlinear sigmoidal activation functions in the second-layer units. The nonlinearities allow the second layer to transform the pixel-based representation in the input layer into a new representation based on coupled pairs of left-right inputs. The third layer of the network then clusters patterns occurring on the second-layer outputs according to their disparity via a standard competitive learning rule. Analysis of the network dynamics shows that the second-layer units' nonlinearities interact with the Hebbian learning rule to expand the region over which pairs of left-right inputs are stable. The learning rule is neurobiologically inspired and plausible, and the model may shed light on how the nervous system learns to use coincidence detection in general.

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