A Computational Model Integrating Multiple Phenomena on Cued Fear Conditioning, Extinction, and Reinstatement

Abstract

Conditioning, extinction, and reinstatement are fundamental learning processes of animal adaptation, also strongly involved in human pathologies such as post-traumatic stress disorder, anxiety, depression, and dependencies. Cued fear conditioning, extinction, restatement, and systematic manipulations of the underlying brain amygdala and medial prefrontal cortex, represent key experimental paradigms to study such processes. Numerous empirical studies have revealed several aspects and the neural systems and plasticity underlying them, but at the moment we lack a comprehensive view. Here we propose a computational model based on firing rate leaky units that contributes to such integration by accounting for 25 different experiments on fear conditioning, extinction, and restatement, on the basis of a single neural architecture having a structure and plasticity grounded in known brain biology. This allows the model to furnish three novel contributions to understand these open issues: (a) the functioning of the central and lateral amygdala system supporting conditioning; (b) the role played by the endocannabinoids system in within- and between-session extinction; (c) the formation of three important types of neurons underlying fear processing, namely fear, extinction, and persistent neurons. The model integration of the results on fear conditioning goes substantially beyond what was done in previous models.

Keywords: amygdala; endocannabinoids; fear conditioning; fear extinction; prefrontal cortex.

Figure 1

Overview of the brain areas reproduced by the model, and their main connections and functions played in conditioning and extinction. The scheme shows how the input (CS, US) is conveyed from LA to CeM through three main circuits: a persistent circuits (LA-BA-CeM and LA-CeL-CeM), a fear circuit (LA-BA-PL-BA-CeM), and an extinction circuit (BA-IL-BA-ITC-BA-CeM), within which the persistent/fear/extinction neurons are found.

Figure 2

Neural units and connection pathways of the model. The width of the connections is roughly proportional to the connection weights at the beginning of the simulations.

Figure 3

Model functioning during the tests. The graph shows in particular the activation of the model units, indicated in the y-axis, during a session of fear conditioning, formed by: 3 CS stimuli paired with a short US; two sessions of extinction, consisting in 20 CS stimuli each; and a session of fear reinstatement, involving a sequence of “CS, US, CS” stimuli. The x-axis represents the time of the sessions. The activation of CeM represents the expression of freezing.

Figure 4

Changes of the connection weights and PSP before and after fear conditioning. First row: simulated connection weights before and after fear conditioning. Second row: simulated effect of a 30 ms presynaptic stimulation on the PSP of the plastic connections of the model (the presynaptic stimulation was adjusted to obtain a change of maximum 10 mV in the postsynaptic unit). Third row: available real data from literature, showing an LTP respectively in the output current evoked in LA neurons by a stimulation of the CS-pathway (McKernan and Shinnick-Gallagher, 1997), in the normalized current evoked in CeL-ON neurons with a stimulation of LA (Li et al., 2013), and in the response of BA to the mPFC input (Vouimba and Maroun, 2011). The other simulated synapses shown here to be stable have never been reported in the literature to undergo LTP or LTD during fear conditioning.

Figure 5

Changes in connection weights and PSP before and after fear extinction. First row: simulated connection weights before and after fear extinction. Second row: simulate effect of a 30 ms presynaptic stimulation on the PSP of the plastic connections of the model (the presynaptic stimulation was adjusted to obtain a change of maximum 10 mV in the postsynaptic unit). Third row: real data (Vouimba and Maroun, 2011) showing that, as in simulation, the mPFC-induced activation of BA decreases after extinction whereas the BA-induced activation of mPFC increases; moreover, as reported by Amano et al. (2010), BA stimulation evokes a higher EPSC slope in ITC neurons after extinction. On the other hand, AMPA/NMDA ratio at the synapse between the CS pathway and LA does not change (Kim and Cho, 2017). The other simulated synapses shown here to be stable have never been reported in the literature to undergo LTP or LTD during fear extinction.

Figure 6

Changes in connection weights and PSP before and after fear reinstatement. First row: simulated connection weights before and after fear reinstatement. Second row: simulated effect of a 30 ms presynaptic stimulation on the PSP of the plastic connections of the model (the presynaptic stimulation was adjusted to obtain a change of maximum 10 mV in the postsynaptic unit). Third row: real data from Vouimba and Maroun (2011) showing that, as in simulation, inputs from the mPFC to BA increase after reinstatement, while those from BA to the mPFC decrease, restoring the synaptic strength to conditioning levels. The other synapses shown here to be stable have never been reported in the literature to undergo LTP or LTD during fear reinstatement.

Figure 7

Effects of various manipulations of the model during conditioning. (A) When the PL is inactivated, mimicking the action of TTX, CeM activation is impaired during the CS delivery in the three trials of fear conditioning; this manipulation does not affect fear conditioning, as shown in a successive test phase, where the PL is restored. The simulation correctly reproduces real data reconstructed from Corcoran and Quirk (2007). (B) CeM activity after the CS delivery in the control model (Ctrl), in the model where the CS-pathway to LA was potentiated (LTP), conditioned (Cond), depotentiated (LTD), and repotentiated (LTP). The simulation mimics the findings of Nabavi et al. (2014). (C) If LA (La inact) or CeL (CeL inact) are inactivated during conditioning, in the successive test phase the CS fails to activate CeM. Real data reconstructed from Wilensky et al. (2006) showing that the GABAa agonist muscimol, injected to inactivate LA or CeL during training, strongly impairs fear conditioning in the test phase.

Figure 8

The involvement of CeL in conditioning. (A) Activity of CeL-ON and (B) CeL-OFF when the CS is delivered before and after conditioning, and comparison with data from Ciocchi et al. (2010). (C) CeM activity during the CS delivery in the three trials of conditioning, when the CS is paired with a maximum depolarization of PLp1 and PLp2; we used a similar protocol as control where we unpaired the CS and depolarization.

Figure 9

Substitution of US with LA activation during conditioning. (A) When LAp1 and LAp2 are activated at they maximum, substituting the US during the three trials of conditioning, the extinction circuit composed by BAp2, ILp, BAp3, and ITC is engaged. This causes the suppression of the activity of BAp4. (B) When LA activation is used instead of the US there is potentiation of only two of the three connections that should undergo LTP during conditioning. (C) Fear conditioning obtained with LA activation, in control condition (Ctrl, black dots) or when the extinction pathway is inactivated (BAp2 inactivation, white dots).

Figure 10

Effects on extinction of the manipulation of the DSI/E. (A) CeM activity during the two sessions of extinction in the control model (black dots) and in a model where DSI/E is inactivated (white dots) in the whole amygdala. As shown in real data from Marsicano et al. (2002) in the right graph, knockout mice for the endocannabinoid receptor CB1 are severely impaired in both within- and between-session extinction. (B) CeM activity during the two sessions of extinction in the control model and in a model where DSI/E is inactivated in CeM during the first session. In agreement with data from Kamprath et al. (2011), extinction is impaired in the first session but is spared in the second session. (C) CeM activity during the two sessions of extinction in the control model and in a model where DSI/E is inactivated in BA during the first session. The control and DSI/E-inactivated models have comparable levels of extinction in the first session, but the manipulated models shows a deficit in between-session extinction, in agreement with real data (Kamprath et al., 2011).

Figure 11

Effects on synaptic plasticity of DSI blockade in BA during extinction. If DSI is inactivated during extinction, plasticity is severely compromised at the synapses PLp-BAp4, BAp2-ILp, BAp3-ITC, and BAcck-BAp2 (compare these results with Figure 4).

Figure 12

Effects of the IL and PL manipulation on extinction. (A) The inactivation of ILp does not influence conditioning and the model shows extinction during the first session, as observed by Quirk et al. (2000). On the other hand, the model shows that the absence of a functioning IL causes a deficit in between-session extinction, as shown in Quirk et al. (2000). (B) Conversely, when the IL is stimulated extinction is faster, in accordance with data presented by Vidal-Gonzalez et al. (2006). (C) The connection between the PL and IL controls the speed of fear extinction, as demonstrated in real data from Marek et al. (2018). In particular, if PLp-ILp connections are stimulated then fear extinction occurs earlier. (D) Instead, if PLp-ILp connections are inactivated then extinction takes longer.