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07.02.2014
Stimulating certain regions in the amygdala, such as the central nucleus, triggers physiological responses typically associated with fear.
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To achieve the low average spontaneous firing rate of ~1 Hz in the experiments modeled (Quirk et al., 1995), independent, Poisson distributed, random excitatory background inputs were delivered to all the pyramidal cells. Washington, Jan 28 : The link between amygdala and fear, especially a fear of others unlike us, has gone too far not only in pop culture but also in psychological science, a new study has claimed. The amygdala are almond-shaped groups of nuclei located deep within the medial temporal lobes of the brain in complex vertebrates, including humans.
Instead, the amygdala seems to be doing something more subtle - processing events that are related to what a person cares about at the moment. A lot of the time, people really don't know, or won't say, what they're thinking, and it would be nice to be able to look at a picture of their brain and know the answer. Tip: To turn text into a link, highlight the text, then click on a page or file from the list above. But the amygdala has other functions, as suggested by the fact that stimulation of other regions can produce a sensation of benign peacefulness, while the stimulation of still others can produce a towering rage! Structure of the model ITC network with 15 neurons each in ITCD and ITCV clusters (adapted from Li et al., 2011). Nair[1] Department of Electrical and Computer EngineeringUniversity of Missouri - Columbia, Columbia, MO, USA1. A computational model combines different types of information related to a system using mathematical equations, and then describes the system’s response to prescribed inputs.
These inputs represent unmodeled synaptic connections from other brain areas such as prefrontal cortex and hippocampus. Shown in research to perform a primary role in the processing and memory of emotional reactions, the amygdalae are considered part of the limbic system. But it also activates at other times, for example in response to pleasant photographs and happy faces. A lot of people came to the amygdala from the study of fear, says Wil Cunningham of Ohio State University, who co-wrote the new paper with Tobias Brosch of New York University. The tone information is delivered to LA via the medial division of medial geniculate body (MGN) and the shock information is delivered to LA via posterior intralaminar nucleus (PIN).
Each cell model has soma (spherical) and dendrite (cylindrical) compartments with each having the specific current channels shown.
For clarity and illustration purpose, the connectivity in the figure is partial and representative only.
IntroductionThe human brain has 100 billion neurons that are constantly humming with electrical and chemical activity. Similar background inputs were provided to the interneurons to generate the reported spontaneous firing rates of ~8 Hz (Pare and Gaudreau, 1996).
Cunningham also thinks many scientists have gotten too attached to a rigid definition of emotions-anger, fear, sadness, happiness, and so on.
Membrane potential responses for pyramidal cell (top panel) and interneuron (lower panel) to a segment of the training trial. Triangles 18 are pyramidal cells (5 of type A (1-5), 2 of type B (6-7), and 1 of type C (8)); circles are interneurons (1-2). 1997) and the model tone responses for the last block of 5 trials in sensitization and successive 5-trial blocks during extinction. These individual neurons are networked into complex local and inter-region circuits that are thought to implement functions that support life. Simulations were performed on a personal computer using the software package GENESIS with the Crank-Nicholson integration method, and a time step of 10 ?s.Model validation. Output from the LA projects to the central nucleus (Ce) through inter-calated cells (ITC, not show in the figure) and BA neurons, eliciting a fear response. In the segment the input consists of a series of two tones (green bars) and two shocks (red bars) with the second tone paired with the second shock. The total spikes (0–50 ms) in each block of 5 trials were normalized to the responses in the 1st block of extinction for each cell and the mean ratio (with SE) among 4 significant conditioned pyramidal cells calculated.B.
One such circuit that is critical for survival is the fear circuit, the key elements of which are thought to include the amygdala, prefrontal cortex, and the hippocampus. In addition to matching unit responses in the model to unit experimental data, the model of the ‘network’ should also reproduce experimentally observed behavior. BA fear neurons project to CeM and BA extinction neurons project to ITCV (fear recall circuit in bold and extinction recall in dashed line type).
The network has five Ce output neurons that receive excitatory inputs from BA, and inhibitory inputs from ITCV neurons.
For an airplane, such a model would integrate the complex mathematical equations for air flow, engine dynamics, frame vibrations, and responses of the control surfaces, and then predict their effect on outputs such as ride quality. Unit tone responses from the lateral amygdala of behaving animals have been reported by Quirk et al. Ce represents the amygdalar output which projects to the brainstem and other regions eliciting fear. Pyramidal cells 3, 5, 7, and 8, received direct tone, while pyramidal cells 1, 4, 5, 7, and 8 received direct shock input. The role of the amygdala in fear has been studied using fear conditioning, a training paradigm in which an organism learns to predict aversive events.
Computational models have now become indispensable for the airplane designer because they enable rapid and inexpensive evaluation of a variety of ‘what if’ scenarios, including the effect of design changes. Typically, a relatively neutral stimulus (conditioning stimulus, CS), such as a tone, light or an odor, is paired with an aversive one (unconditioned stimulus, US), such as a footshock.
Their main finding was that conditioning significantly increased the number of tone-elicited spikes with the greatest effects at the shortest latency following tone onset.
After only a few pairings, the previously neutral stimulus becomes aversive and can itself evoke an emotional reaction typically resulting in a freezing behavior.
For instance, recent technical advances have resulted in a rapid accumulation of information on intracellular signaling pathways and their relationships to long-term neuronal changes (Byrne and Roberts, 2004). The learning processes underlying conditioning develop rapidly and the memory of this association persists for long periods of time, reflecting the biological significance of the learning experience for the organism. Computational techniques and tools are being developed to model such mechanisms with increasing accuracy and are found to be essential to generate an understanding of the underlying functions in such cases (Koch and Segev, 2001; Mauk, 2000). With tuning of the plasticity parameters in the model, the LA network model unit tone responses successfully reproduced experimental data in Quirk et al.
Even though there is consensus that the amygdala is a critical component of the mammalian fear circuit, the relevant interconnections among the amygdalar nuclei and their contributions to the acquisition and storage of fear and extinction memories are not well understood presently. The term ‘computational neuropharmacology’ has recently been proposed for the application of computational modeling to drug development, drug discovery, and the modeling of the mechanisms of action of psychiatric drugs (Aradi and Erdi, 2006). Disruption of the fear circuit is thought to underlie the pathology of post-traumatic stress and of other anxiety disorders (Corcoran and Quirk, 2007). In this chapter, we review the preliminary insights related to the amygdalar fear circuit provided by biologically realistic computational models. Such disruptions are also manifested as changes in excitability of individual neurons, as well as changes in synaptic strengths between neurons in specific sub-circuits, within these areas.
Specifically, we investigate how sensory information might be associated within the amygdala, and how the various amygdalar nuclei interact to acquire and store both fear and extinction memories via long term potentiation and depression of synapses.
Increasing understanding of brain functioning due to advances in basic neuroscience techniques and imaging modalities has led to the emergence of computational modeling as an important tool for studying such changes.
The ‘higher’ level structures such as the prefrontal cortex and the hippocampus are known to influence the amygdala to modulate such memories. Progress in the areas of cellular neurophysiology and synaptic plasticity permit the development of biologically realistic computational models that more closely approximate learning, both at the membrane and network levels. However, not much is known about the underlying mechanisms presently and so this modulatory effect is discussed only briefly. The total spikes (0–50 ms) in each block of 5 trials were normalized to the responses in the 1st block of extinction for each cell and the mean ratio (with SE) among 4 significant conditioned pyramidal cells calculated.showed clear frequency adaptation with the tone responses concentrated in the first 100 ms, indicating a good match with the experimental recordings. Such biologically realistic computational models have the potential to enhance our understanding of brain circuits with potential applicability to a range of phenomena from the neural basis of mental illness to the mechanism of action of drugs.
This is followed by a discussion of the unique insights that computational models might add to what is already known about the fear circuit, and about the potential of such models to contribute to reverse engineering the mammalian fear circuit. Figure 7 shows the match between experimental and model conditioned tone responses for the last block of five trials in sensitization and successive five-trial blocks during extinction. This ability of computational models derives from the fact that they can ‘integrate’ different types of information into a self-consistent and coherent portrait of how fear might be learned, and, in the process, reveal presently unknown mechanisms and interactions associated with such learning. The large potentiation of the excitatory inputs onto pyramidal cells caused by conditioning, together with the potentiation of connections between several pyramidal cells leads to elevated tone responses in early extinction. As extinction progresses, increasing feedforward and feedback inhibition from the interneurons, combined with depotentiation at excitatory synapses onto pyramidal cells bring tone responses back to pre-conditioning levels or even lower.


Auditory fear and the amygdalaFear is one of the few emotions that can be observed in non-primate mammalian organisms (LeDoux, 2000). After fear conditioning, a number of physiological manifestations can be observed upon re-exposure to the conditioning stimulus, including increased autonomic arousal, increased stress hormone release, reflex potentiation, and defensive behaviors (LeDoux, 2000). Once validated, a model can be used to provide insights into the underlying mechanisms, as described next.Insights provided by the LA modelThe Li et al. Extensive studies indicate that freezing is a defensive behavior and serves as a reliable index of fear in rodents (Blanchard and Blanchard, 1972). The mammalian circuit related to auditory fear and extinction has been studied by several researchers and, although not fully understood, consensus is emerging about the specific roles of the amygdalar nuclei in this circuit. The model was used to determine how the intrinsic and synaptic mechanisms interact in a network to shape unit tone responses. In rodents, such studies typically use fear conditioning, which is a form of Pavlovian learning where the stimulus parameters can be regulated by the experimenter. Fear conditioning is a highly conserved form of behavior that is exhibited in both laboratory situations and in normal environments (LeDoux, 1994).
Animals do not need to be food- or water-deprived to demonstrate fear conditioning.Fear conditioning protocol. The model identified two possible sites for fear memory storage in LA: the tone synapses from the auditory thalamus (or cortex) onto the pyramidal cells and the synapses between pyramidal cells.
Hebbian pairing indicates that the synaptic coupling between two pyramidal cells will be strengthened as long as both receive strong inputs such as shocks. In contrast, the tone synaptic weight increases only when tone and shock are paired and decreases when tone and shock are unpaired.
Freezing, defined as the total lack of movement except for respiration, is used as the measure of learning in this task (Blanchard and Blanchard, 1972). After conditioning, a robust and long-lasting behavioral change is produced which is particularly amenable to genetic and pharmacological studies. The model also showed that the pyr-pyr synapses decayed less on average in extinction compared to the tone-pyr synapses. It also affords the advantage that detailed time courses of the sequence of events that may occur after conditioning can be easily generated. This was because of frequency adaptation of pyramidal cells resulting in lower incidence of Hebbian weakening. Thus, fear conditioning serves as a model for elucidating the crucial electrophysiological and biochemical mechanisms underlying learning (Wehner and Radcliffe, 2004). The cells with stronger adaptation are slower to learn fear but are able to maintain fear memory for a long time, whereas the cells with weaker adaptation learn fear faster, but also extinguish faster (Fig. This suggests that pyramidal cells with weaker adaptation are important for fear expression, whereas those with stronger adaptation are important for long-term storage of fear associations.
However, the tone-interneuron and inter-pyramidal cell synapses potentiated much larger during both extinction sessions, compared to the pyramidal-interneuron synapses.How does the low spontaneous firing rate of pyramidal cells affect memory storage? The amygdala is located within the medial temporal lobe and is recognized as being critical for Pavlovian fear learning. LA cells signal fear and so it is logical that they have low spontaneous firing rates of around 1 Hz. Experiments with the model revealed that the low rate of spontaneous firing in LA may act to preserve the fear memory due to decreased incidence of Hebbian weakening. Multiple experimental modalities including field potential response to high frequency stimulation, patch clamp recordings, single unit recordings, pharmacological manipulations and transgenic approaches all implicated the amygdala in the acquisition of learned fear. The high spontaneous rates of interneurons, on the other hand, leads to a comparatively faster weakening of extinction memory stored in the interneurons synapses. However, what is not clear is whether a part of the fear memory is also lost during extinction trials. Blocking NMDA receptors in experiments would prevent depotentiation of the excitatory synapses onto pyramidal cells (LTD) but at the same time it would also block potentiation of inhibitory connections. A model, however, can implement selective blockade of LTD only at the tone-pyramidal and pyramidal-pyramidal synapses by preventing Ca2+ influx via the NMDA channels, to separate the effects of both these phenomena. Thalamic inputs conveying information about the auditory tone (conditioned stimulus, CS) and foot shock (unconditioned stimulus, US) arrive first at the lateral nucleus. Our study showed how such models are poised to complement experimental investigations by providing insights into how cellular and synaptic mechanisms contribute to implementing functions in brains.
There are two main types of neurons within the LA and the BA: pyramidal glutamatergic projection neurons and local circuit ?-aminobutyric acid (GABA) releasing interneurons. The amygdalar nuclei LA, BA, Ce and the ITC clusters act in concert to store auditory fear and extinction memories, and these nuclei are in turn modulated by external structures such as the prefrontal cortex and hippocampus. The amygdala intercalated cells (ITC; fig 2) are distributed along the lateral and medial parts of the basolateral amygdaloid complex.
This strategic location of the medial ITC clusters, between the sensory input (BLA) and fear output (Ce) stations of the amygdala, is thought to be critical for regulating classically conditioned fear responses (Pare et al., 2004). Consistent with this, IL stimulation was found to dramatically reduce the responsiveness of Ce neurons to BLA inputs (Quirk et al., 2003). LA has distinct dorsal and ventral regions which seem to store fear memories in different ways (Repa et al., 2001). Additionally, three different types of short-term synaptic plasticity have been observed in inter-ITC connections (Geracitano et al., 2007), but the role of such synaptic heterogeneity is not clear. How then might IL inputs overcome the inter-ITC inhibition and reduce the responsiveness of Ce? Again, it is currently difficult to address this question experimentally, because we lack criteria to identify ITC cells on the basis of their extracellularly recorded activity. Fear cells acquire CS responses as a result of fear conditioning, but lose them following extinction training; extinction cells become CS responsive only following extinction training, and extinction- resistant cells acquire CS responses during conditioning and remain CS responsive even after extinction training. So, in order to study how inter-ITC inhibitory connections affect their responses to IL inputs, we developed a biologically realistic model of the ITC network (Fig.
ITC cells express an unusual voltage-dependent K+ conductance whose slow-deinactivation kinetics allow them to produce a prolonged depolarizing plateau after a transient suprathreshold depolarization (Royer et al., 2000). Even with these advances in understanding, a clear portrait of how the various amygdalar nuclei interact to acquire and store fear is still lacking.Once acquired, conditioned fear associations are not always expressed. This enables ITC neurons to transform transient excitatory inputs into a prolonged state of increased excitability with possibly important consequences for the regulation of conditioned fear. Repeated presentation of the tone CS in the absence of the US causes conditioned fear responses to diminish rapidly, a phenomenon termed as fear extinction (Myers and Davis, 2007). The neural mechanisms of fear extinction are not well understood, and a neural analysis of extinction and inhibition is still in its infancy (Delamater, 2004; Quirk and Mueller, 2008). Some psychological theories describe extinction as an “unlearning” process due to a violation of the CS-US association established during acquisition of fear (Rescorla and Wagner, 1972). This unlearning view has been challenged by the observation that fear recovers spontaneously after extinction. An alternative theory proposes that extinction does not erase the CS-US association but instead forms a new memory that inhibits conditioned responding (Bouton and King, 1983; Quirk, 2002). The leakage reversal potential was set to -93 mV to match experimental measurements of their resting potential (–85 mV). Fear is thought to be expressed via projections from LA to BA, ITC and Ce (see fig 2), and expression of this fear memory has been shown to be influenced by cortical structures. The resulting input resistance was about 600M? when measured from rest, consistent with experimental observations. For instance, although LA responds transiently to conditioned tones, the animal continues to freeze throughout the period of a 30 second tone. The ITC model contained several ionic currents including a leakage current IL, a spike-generating sodium current INa, a potassium delayed rectifier current IDR, a slow deinactivating current ISD, a voltage-gated persistent muscarinic current IM, a hyperpolarization-activated current IH, a high-voltage activated Ca2+ current ICaL, and a slow Ca2+- dependent after-hyperpolarization current IsAHP.
Quirk and colleagues investigated whether the prelimbic (PL) region of the medial prefrontal cortex (mPFC) might be involved in sustaining freezing.
This finding is supported by studies examining neuronal activity with cFos which showed that PL activation is correlated with fear expression and extinction failure.What are the structures that might modulate the memory of auditory fear extinction? This led the authors to suggest that extinction failure might be caused by excessive activity in PL, combined with deficient activity in the IL, and that recall of fear and extinction memories may depend on the optimal balance of activity between PL and IL.What is the role of context in auditory fear?
That is, after fear conditioning, it will express fear by freezing in the trained context, even in the absence of tone.
Acquisition of contextual fear may involve configural or spatial learning and many lines of evidence support hippocampal involvement in contextual fear conditioning (Anagnostaras et al., 1999).
LA inputs projected to ITCD, while IL inputs projected equally to ITCD and ITCV (McDonald et al., 1996).
Contextual information is processed in the hippocampal formation (HPC), which plays a critical role in gating the response of rats to extinguished tone stimuli (Corcoran et al., 2005).
This pathway has been hypothesized to serve a ‘teaching’ role for IL neurons, by generating Ca-dependent bursting in IL neurons. The extinction inputs did not project to Ce because the activation profile of extinction cells was opposite to the expression of fear (Herry et al., 2008).


Instead, both fear and ER inputs projected to Ce.ITC neurons exhibit NMDA-dependent bidirectional synaptic plasticity (Royer and Pare, 2002) and in a recent experimental study, the BA inputs to ITC cells were reported to show a three-fold potentiation during extinction training (Fig. Given the fact that the firing rate of LA neurons is significantly increased after conditioning (Quirk et al., 1995), it is reasonable to assume that the LA–ITCD connection is potentiated by conditioning.
Hence, we used a threefold synaptic weight (compared with the habituation state) for the LA–ITCD synapses in the fear state and a threefold synaptic weight for the BA–ITC synapses in the extinction state.
The model was trained using a modified Hebbian-type learning rule and was able to reproduce data related to frequency-specific changes of the receptive fields known to exist in the auditory thalamus and amygdala. However, based on experimental findings (Geracitano et al., 2007), the presynaptic release probability of the ITC–ITC and ITC–Ce synapses was modifiable, and were split equally into facilitating, depressing, and constant types.
Balkenius and Moren (2001) proposed a neural network model for emotional conditioning focusing on the amygdala and the orbitofrontal cortex and their interaction.
Amygdala was the locus of acquisition and the orbitofrontal cortex was the site for extinction learning.
The model simulated basic phenomena related to emotional conditioning including acquisition, extinction, blocking, and habituation. We determined responses of the model to a 2-sec auditory tone input (CS) during three different network states: habituation, following fear conditioning, and after extinction training.
The model revealed how the two populations might encode contextual specificity of fear and extinction memories.
The model was developed using a firing rate framework and was able to reproduce several known features of fear learning and make testable predictions.
One reason for this is the large difference in both temporal and spatial complexities between the levels. Computational modeling platforms at the cellular and network levels include public domain software such as NEURON (Carnevale and Hines, 2006) and GENESIS (Bower and Beeman, 2003) which are being designed for biologists, and require minimal understanding of the underlying mathematics. Such packages can perform simulations of models ranging from single neurons to complex networks representing brain circuits.
Single cell models can be developed for the pyramidal cell and the interneuron using the software cited. The modeling process involves several steps where the software requires the user to define ‘LEGO’ blocks for the neuron, such as the soma or cell body, dendrite and axon, ‘insert’ into them specific suites of membrane channels, and then connect them to form networks (fig 4). Such software aim to provide an easy-to-use interface, making most of the mathematical details transparent to the user. The model parameters within all these blocks are then iteratively adjusted, within biophysical bounds, to match biological data such as resting potential, input resistance, and membrane potential responses to various current injections.
Figure 4 illustrates the development of a two-cell network model showing how ‘memories’ can be stored in the synapses (Li et al., 2008).
In this example case, we use a lateral amygdala pyramidal cell model and a lateral amygdala interneuron model. As cited, the single cell properties should match those reported in biology, including the membrane potential responses to various current injections. Once such single cell models are developed (see Li et al., 2009), they can be embedded into networks. The pyramidal cell, on the other hand, excited the interneuron via an excitatory AMPA (alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) synapse. Both cells received random background inputs that represent afferent connections from other brain areas such as prefrontal cortex and hippocampus. The frequency and strength of the random inputs was adjusted to obtain pyramidal cell spontaneous firing rates of less than 1 Hz seen in LA neurons.
We implemented plasticity in all the synapses using a calcium-based learning rule, and then ‘trained’ the model with the fear conditioning protocol used in experiments (Quirk et al., 1995).
Figure 5a shows the membrane potential responses of a pyramidal cell and interneuron for a segment of the training cycle. This segment consisted of two tones (500 ms each) and two shocks (100 ms each) with the second shock occurring during the last 100 ms of the second tone. Due to Hebbian strengthening between tone inputs and shock inputs, the tone input to the pyramidal cell strengthens during conditioning and is maintained throughout extinction. In the interneuron, on the other hand, tone inputs strengthen during the extinction phase, due to Hebbian pairing between different sets of tone inputs. Consistent with behavioral findings, the fear memory is not lost during extinction, but is suppressed by LTP-like potentiation of inhibition. The concepts and insights illustrated by this simple two-cell network, such as potential storage sites for memory, translate directly to larger networks, as we discuss in the following.4.
Reverse engineering auditory fear circuits in the amygdalaThe overall goal of the reverse engineering effort is to integrate diverse morphological and neurophysiological data into biologically realistic models of the various amygdalar nuclei (lateral nucleus, basal nucleus, intercalated cells, and the central nucleus) and then use the model to investigate how the different nuclei participate in the acquisition and extinction of auditory fear memories, and how they are modulated by cortical structures.
We initiated the model development of the overall fear circuit using a bottom-up approach starting with the core unit: the lateral amygdala nucleus, LA. The LA and ITC models provided unique insights that are presently not possible to obtain via experiments. After a review of the development of the model, and its validation, we discuss the unique insights provided by the model.Single cell properties. There are two types of principal neurons within the LA: pyramidal-like glutamatergic projection neurons, and local circuit GABAergic interneurons (Faber and Sah, 2001). Principal neurons in the LA exhibit a range of firing properties in response to prolonged current injection (Faber et al., 2001). Accordingly three types of pyramidal cells were modeled, types A, B, and C, where type A had strong, B had medium, and C had minimal frequency adaptation. The interneuron was modeled as a basket-type, fast-spiking, aspiny cell with each compartment containing a fast Na+ current and a delayed rectifier K+ current with different kinetics from those of pyramidal cells to reproduce its much shorter spike duration (Durstweitz et al., 2000). For each cell, the AMPA and the N-methyl D-aspartate (NMDA) channels were placed in the dendrite compartment, and the inhibitory GABAA channels were placed on the soma. Fig.4 provides details of typical pyramidal cell and interneuron models with the various ionic and synaptic channels.
The LA network model consisted of eight pyramidal cells and two GABAergic interneurons (fig. Among the eight pyramidal cells, five were type A (P1–P5), two were type B (P6 –P7), and one was type C (P8).
In the network model, we were particularly interested in information processing in the dorsal sensory-receptive region of LA (LAd).
In this fully connected architecture, each pyramidal neuron received excitatory inputs from all other pyramidal cells as well as inhibitory inputs from the two interneurons. Both interneurons received excitatory inputs from all pyramidal cells and thus provided feedforward and feedback inhibition to pyramidal cells. The synaptic delays for tone and shock inputs were set to 8 ms to represent the transmission delay between the start of tone and the arrival of information in the LA (Li et al., 1996).
In addition, plasticity was also modeled in GABAergic inhibitory synapses from interneurons to pyramidal cells.
The occurrence of synaptic potentiation versus depression was determined by intracellular calcium levels, according to the calcium control hypothesis. Learning of conditioned fear leads to changes in synaptic strength in the neural circuitry and the magnitude and sign of these variations are unique insights that a computational model can provide. The schedule of tone and shock inputs to the model was based on in vivo studies (Quirk et al., 1995, 1997). We scaled down the timing of the auditory fear conditioning protocol by approximately two orders of magnitude so that it would be suitable for computational study.
The simulation included a sensitization phase, a conditioning phase, and two extinction phases. Each tone lasted 500 ms and each shock lasted 100 ms, and the interval between two tones was 3.5 s. During the sensitization phase, 10 unpaired tones and shocks were presented to the network with the shocks occurring randomly between the tones.
Following sensitization, 10 paired tones and shocks were provided in the conditioning phase with shock present during the last 100 ms of the tone. The gap between conditioning and extinction phases was 40 s and the model was tested for spontaneous recovery after a delay of 840 s.
The firing frequency for the tone and shock inputs was set at 200 Hz to model the summed activity of multiple inputs in vivo.
The tone inputs also included noise represented by random Poisson spikes with an average frequency of 2 Hz. Given that the tone starts out as neutral and the shock as noxious, the conductance strength encoding the shock information was set much higher than that representing the tone inputs.



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