Role of sensory input distribution and intrinsic connectivity in lateral amygdala during auditory fear conditioning: A computational study

Abstract

We propose a novel reduced order neuronal network modeling framework that includes an enhanced firing rate model and a corresponding synaptic calcium-based synaptic learning rule. Specifically, we propose enhancements to the Wilson-Cowan firing rate neuron model that permits full spike frequency adaptation seen in biological LA neurons, while being sufficiently general to accommodate other spike frequency patterns. We also report a technique to incorporate calcium-dependent plasticity in the synapses of the network using a regression scheme to link firing rate to postsynaptic calcium. Together, the single cell model and the synaptic learning scheme constitute a general framework to develop computationally efficient neuronal networks that employ biologically-realistic synaptic learning. The reduced order modeling framework was validated using a previously reported biophysical conductance-based neuronal network model of a rodent lateral amygdala (LA) that modeled features of Pavlovian conditioning and extinction of auditory fear (Li et al., 2009). The framework was then used to develop a larger LA network model to investigate the roles of tone and shock distributions and of intrinsic connectivity in auditory fear learning. The model suggested combinations of tone and shock densities that would provide experimental estimates of tone responsive and conditioned cell proportions. Furthermore, it provided several insights including how intrinsic connectivity might help distribute sensory inputs to produce conditioned responses in cells that do not directly receive both tone and shock inputs, and how a balance between potentiation of excitation and inhibition prevents stimulus generalization during fear learning.

Synaptic competition in the lateral amygdala and the stimulus specificity of conditioned fear: a biophysical modeling study

“…Note that in these inter-modal cases, potentiation of responses produced by blocking interneuronal plasticity has to depend on potentiation of PN–PN connections since CS*1–4 were never paired with the US. Therefore, these results together indicate that plasticity of interneuronal synapses promotes CS specificity whereas plasticity at PN–PN synapses promotes generalization (Ball et al 2012). …”

“…However, these types of models cannot incorporate data about the cellular, synaptic, and neuromodulatory mechanisms involved in fear learning. For instance, firing rate (Ball et al 2012) and Izhikevich-based spiking (Hummos et al 2014) model cells could not match passive properties and current injection responses simultaneously. Moreover, studying phenomena such as effects of voltage clamping at different levels, of neuro-modulation, of calcium-based plasticity rules, and of blocking particular current channels is typically difficult in networks with such cells.…”

“…Similar numbers were obtained for other runs. We followed the protocol in Lissek et al (2008) and Repa et al (2001) and conditioned the network to CS+ (paired CS and US) and to CS− (unpaired CS and US) (Ball et al 2012). The other CSs were used only for testing.…”

Qualitatively different effect of repeated stress during adolescence on principal neuron morphology across lateral and basal nuclei of the rat amygdala

“…Loss of specificity of amygdala neuronal responses can lead to fear generalization (Ghosh and Chattarji, 2014). Increased BLA neuronal excitability, impaired GABAergic regulation, and increased plasticity of intrinsic connectivity contribute to fear generalization (Shaban et al, 2006; Bergado-Acosta et al, 2008; Ball et al, 2012; Ghosh and Chattarji, 2014). However, impaired function of the prefrontal cortex and hippocampus (Zelinski et al, 2010; Xu and Sudhof, 2013), and their input to the BLA, also contributes to BLA-mediated fear generalization (Bergado-Acosta et al, 2008; Sangha et al, 2009; Lihktik et al, 2014).…”

Mechanisms of memory storage in a model perirhinal network

“…However, the simpler network might achieve the same outcome differently than the real one. Prior to developing the model described below, we explored the ability of simpler models (firing rate models; Ball et al 2012) to reproduce perirhinal mechanisms of activity-dependent plasticity. However, we encountered many difficulties (summarized in Section x of the supplementary information), which led us to the current model.…”