Basolateral amygdala oscillations enable fear learning in a biophysical model

Abstract

The basolateral amygdala (BLA) is a key site where fear learning takes place through synaptic plasticity. Rodent research shows prominent low theta (~3-6 Hz), high theta (~6-12 Hz), and gamma (>30 Hz) rhythms in the BLA local field potential recordings. However, it is not understood what role these rhythms play in supporting the plasticity. Here, we create a biophysically detailed model of the BLA circuit to show that several classes of interneurons (PV, SOM, and VIP) in the BLA can be critically involved in producing the rhythms; these rhythms promote the formation of a dedicated fear circuit shaped through spike-timing-dependent plasticity. Each class of interneurons is necessary for the plasticity. We find that the low theta rhythm is a biomarker of successful fear conditioning. The model makes use of interneurons commonly found in the cortex and, hence, may apply to a wide variety of associative learning situations.

Keywords: Hodgkin-Huxley networks; Local field potential; PV interneurons; Pavlovian fear conditioning; SOM interneurons; Theta rhythm; VIP interneurons; biomarker; gamma rhythm; spike-timing-dependent plasticity.

Figure 1.. Isolated neurons produce fundamental rhythms.

A. dynamics in response to depolarizing and hyperpolarizing currents mimic the electrophysiological behavior of BLA interneurons classified in (Sosulina et al., 2010). B. dynamics in the baseline condition. C. Interacting PV and excitatory projection neuron (E) entrain in a pyramidal-interneuron network gamma rhythm (PING).

Figure 2.. BLA interneurons and the excitatory projection neurons interact and modulate the network activity.

A (top): Network made of three interneurons (VIP, SOM, and PV) and the excitatory projection neuron encoding fear (F) without US input (left) and with US input (right). A (bottom): before the onset of US, VIP shows gamma bursts nested in the low theta rhythm (blue trace), and SOM fires at a natural frequency in the high theta range (purple trace). PV is completely silent due to the lack of any external input (green trace). F, despite its natural frequency of around 11 Hz, is silent due to the inhibition from SOM (orange trace). After US onset, due to the longer VIP bursts and the US input, F shows a pronounced activity during the VIP active phase and outside when the SOM and PV inhibition fade. PV is active only when excited by F, and then gives inhibitory feedback to F. B (top): Network in panel A with the excitatory projection neuron encoding the CS input (ECS) during the CS presentation (left) and with paired CS and US inputs (right). B (bottom): 2-second dynamics of all the neurons in the BLA network affected by CS, and by US after 1 second has elapsed. As in panel A, VIP shows gamma bursting activity nested in the low theta frequency range with bursts duration affected by the presence or absence of US. VIP inhibits i) SOM, which fires at high theta (purple trace) regardless of the external inputs, and ii) PV, which fires at gamma. ECS (light blue trace) and F are both active when both CS and US are present and VIP is active, producing a gamma nested into a low theta rhythm. The evolution in time of the conductance (gAMPA) shows an overall potentiation over the second half of the dynamics when both ECS and F are active. C: blowup of ECS-F burst of activity and gAMPA dynamic shown in the gray area in panel B (bottom); ECS (blue trace) fires most of the time right before F, thus creating the correct pre-post timing conducive for potentiation of the ECS to F conductance. The order of each pair of ECS-F spikes is labeled with “c” (correct) or “w” (wrong).

Figure 3.. ECS to F conductance across network realizations.

A: Mean (color-coded curves) and standard deviation (color-coded shaded areas) of the AMPA conductance (gAMPA) from ECS to F across 40 network realizations over 40 seconds. Red curve and shaded area represent the mean and standard deviation, respectively, across network realizations endowed with all the interneurons. B: Evolution in time of the AMPA conductance for the 40 full network realizations in A. C: AMPA conductance of 20 network realizations over 45 seconds with F strongly activated by US (2 seconds) and its effects (13 seconds), and ECS active the whole interval because of CS; 19 out of 20 network realizations show potentiation after one trial.

Figure 4.. PV-only network and VIP-PV network lead to depression.

A: Left, network with PV as the only interneuron. PV cell is at an excitation level that supports PING. Middle, dynamics of PV and F that reciprocally interact and generate PING. Right, PV and F entrain in PING (top), ECS and F activity (middle), and ECS to F conductance (bottom). B: Left, network with both PV and VIP. Right, network dynamics (top, middle) followed by the evolution in time of the ECS to F AMPA conductance (bottom). The detailed mechanism behind the evolution of AMPA conductance in panels A and B is in the Supplementary Information, including Fig. S2.

Figure 5.. Heterogeneous BLA fear network is capable of establishing the association between CS and fear.

A: whole BLA network with multiple and heterogeneous neurons. B: Dynamics in the first 5000 ms after the onset of US and CS of each of the neurons in the BLA network. C: Dynamics of ECS to F conductance over 5000 ms shaped by the activity in B. D: Left, mean and standard deviation across 40 network realizations of the ECS to F conductance for the full (red), no VIP (green), no SOM (purple), no PV (black), no SOM and PV (magenta) networks. The green, purple, back, and magenta curves are superimposed on each other. Right, dynamics of all the 40 full network realizations over 40 seconds.

Figure 6.. Heterogeneous network dynamics and spectral properties pre versus post fear conditioning for network realizations in learners and non-learners.

All power spectra are represented as mean and standard deviation across 20 network realizations. A: Dynamic of BLA heterogeneous networks pre (left) and post (learner, middle; non-learner, right) fear conditioning. B: Power spectra of network spiking activity before fear conditioning (blue) and after successful (purple) and non-successful fear conditioning (orange); top, right: inset between 2 and 6 Hz. Blue and orange curves closely overlap. C: Power spectra of the LFP proxy (linear sum of AMPA, GABA, D-, NaP-, and H-currents); all the details as in B. D, E: 25^th, 50^th and 75^th percentiles of LFP low theta power in the 2.5–4 Hz range where the peaks of power exist (D) and high theta power in the 12–14 Hz range again where the peaks of power exist (E) in 20 network realizations before and after (in both learners and non-learners) fear conditioning. ***: p-value < 0.001; n.s.: non-significant difference. F: Power spectra of AMPA currents from ECS to F (black curve) and from F to PV interneurons (red curve). G: Power spectra mean and standard deviation of the LFP signals derived from only AMPA currents (red curve), GABA currents (green curve), D-current, NaP-current and H-current (light blue curve). AMPA currents are generated by the interactions from ECS to F, F to VIPs, and F to PVs. VIP cells contribute to the D-current and SOM cells to H-current and NaP-current (see the result’s section “Rhythms in the BLA can be produced by interneurons” for a description of these currents).