Regulation of Fear by Amygdala Intercalated Cells in a Network Model of Fear Acquisition and Extinction

2008 ◽  
Author(s):  
Guoshi Li ◽  
Gregory J. Quirk ◽  
Satish S. Nair
Keyword(s):  
Computational Model ◽  
Fear Conditioning ◽  
Network Model ◽  
Hebbian Learning ◽  
Learning Rule ◽  
Biophysical Model ◽  
Intercalated Cells ◽  
Bistable Behavior ◽  
Lateral Amygdala ◽  
Hebbian Learning Rule

A computational model of the fear circuit was developed to study regulation of fear by amygdala intercalated (ITC) neurons within the amygdala. A new biophysical model of an ITC neuron was developed first to capture its bistable behavior caused by an unusual slowly deinactivating current. An existing lateral amygdala network model was then extended into an overall fear circuit by adding ITC neurons, together with additional amygdaloid structures. Using a biophysical Hebbian learning rule for plastic synapses, the model successfully simulated the amygdala responses during acquisition, extinction, and recall of extinction in auditory fear conditioning. Results showed that fear could be regulated by the bistability of ITC neurons. The model also suggested additional sites for the storage fear and extinction memories.

Computing with Continuous Attractors: Stability and Online Aspects

2005 ◽  
Vol 17 (10) ◽  
pp. 2215-2239 ◽  
Author(s):  
Si Wu ◽  
Shun-ichi Amari
Keyword(s):  
Network Model ◽  
Hebbian Learning ◽  
Learning Rule ◽  
Population Coding ◽  
Neural Population ◽  
Neutral Stability ◽  
Hebbian Learning Rule ◽  
Tuning Width ◽  
Tracking Time ◽  
Continuous Attractors

Two issues concerning the application of continuous attractors in neural systems are investigated: the computational robustness of continuous attractors with respect to input noises and the implementation of Bayesian online decoding. In a perfect mathematical model for continuous attractors, decoding results for stimuli are highly sensitive to input noises, and this sensitivity is the inevitable consequence of the system's neutral stability. To overcome this shortcoming, we modify the conventional network model by including extra dynamical interactions between neurons. These interactions vary according to the biologically plausible Hebbian learning rule and have the computational role of memorizing and propagating stimulus information accumulated with time. As a result, the new network model responds to the history of external inputs over a period of time, and hence becomes insensitive to short-term fluctuations. Also, since dynamical interactions provide a mechanism to convey the prior knowledge of stimulus, that is, the information of the stimulus presented previously, the network effectively implements online Bayesian inference. This study also reveals some interesting behavior in neural population coding, such as the trade-off between decoding stability and the speed of tracking time-varying stimuli, and the relationship between neural tuning width and the tracking speed.

scholarly journals Fear signaling in the prelimbic-amygdala circuit: a computational modeling and recording study

2013 ◽  
Vol 110 (4) ◽  
pp. 844-861 ◽  
Author(s):  
Sandeep Pendyam ◽  
Christian Bravo-Rivera ◽  
Anthony Burgos-Robles ◽  
Francisco Sotres-Bayon ◽  
Gregory J. Quirk ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Fear Conditioning ◽  
Large Scale ◽  
Conditioned Fear ◽  
Individual Variability ◽  
Biophysical Model ◽  
Human Homolog ◽  
Lateral Amygdala ◽  
Internal Inhibition ◽  
Β Blocker

The acquisition and expression of conditioned fear depends on prefrontal-amygdala circuits. Auditory fear conditioning increases the tone responses of lateral amygdala neurons, but the increase is transient, lasting only a few hundred milliseconds after tone onset. It was recently reported that that the prelimbic (PL) prefrontal cortex transforms transient lateral amygdala input into a sustained PL output, which could drive fear responses via projections to the lateral division of basal amygdala (BL). To explore the possible mechanisms involved in this transformation, we developed a large-scale biophysical model of the BL-PL network, consisting of 850 conductance-based Hodgkin-Huxley-type cells, calcium-based learning, and neuromodulator effects. The model predicts that sustained firing in PL can be derived from BL-induced release of dopamine and norepinephrine that is maintained by PL-BL interconnections. These predictions were confirmed with physiological recordings from PL neurons during fear conditioning with the selective β-blocker propranolol and by inactivation of BL with muscimol. Our model suggests that PL has a higher bandwidth than BL, due to PL's decreased internal inhibition and lower spiking thresholds. It also suggests that variations in specific microcircuits in the PL-BL interconnection can have a significant impact on the expression of fear, possibly explaining individual variability in fear responses. The human homolog of PL could thus be an effective target for anxiety disorders.

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.

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.

scholarly journals A Biologically Realistic Network Model of Acquisition and Extinction of Conditioned Fear Associations in Lateral Amygdala Neurons

2009 ◽  
Vol 101 (3) ◽  
pp. 1629-1646 ◽  
Author(s):  
Guoshi Li ◽  
Satish S. Nair ◽  
Gregory J. Quirk
Keyword(s):  
Fear Conditioning ◽  
Network Model ◽  
Basolateral Amygdala ◽  
Conditioned Fear ◽  
Pyramidal Cells ◽  
Model Learning ◽  
Lateral Amygdala ◽  
Single Structure ◽  
Firing Properties

The basolateral amygdala plays an important role in the acquisition and expression of both fear conditioning and fear extinction. To understand how a single structure could encode these “opposite” memories, we developed a biophysical network model of the lateral amygdala (LA) neurons during auditory fear conditioning and extinction. Membrane channel properties were selected to match waveforms and firing properties of pyramidal cells and interneurons in LA, from published in vitro studies. Hebbian plasticity was implemented in excitatory AMPA and inhibitory GABAA receptor-mediated synapses to model learning. The occurrence of synaptic potentiation versus depression was determined by intracellular calcium levels, according to the calcium control hypothesis. The model was able to replicate conditioning- and extinction-induced changes in tone responses of LA neurons in behaving rats. Our main finding is that LA activity during both acquisition and extinction can be controlled by a balance between pyramidal cell and interneuron activations. Extinction training depressed conditioned synapses and also potentiated local interneurons, thereby inhibiting the responses of pyramidal cells to auditory input. Both long-term depression and potentiation of inhibition were required to initiate and maintain extinction. The model provides insights into the sites of plasticity in conditioning and extinction, the mechanism of spontaneous recovery, and the role of amygdala NMDA receptors in extinction learning.

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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