The survival of animals, including us humans, depends on the ability to discriminate good food from bad. We would prefer eating a given taste if it did not cause any negative feelings after eating it for the first time; however, we would avoid eating that specific taste if it caused any digestive discomfort. This ability to connect sensory events that happen close in time is called associative learning.

One longstanding theory of associative learning suggests that if the neurons that are activated by a taste fire at the same time as those that control nausea, the connections between the two groups of neurons are strengthened. This helps that particular taste to become associated with the feeling of illness. Animals can also link events that are separated in time – for example, they can become averse to a food even when its ill effects are felt several hours after eating it. An important question is how a new event (such as a new food) is internally represented and maintained for a certain time so that it associates with a response (sickness) that occurs much later.

One method used to investigate associative learning is to feed rats a new food, and then later make them feel nauseous to measure how much this causes them to avoid the food in the future. The gustatory cortex is the part of the brain responsible for perceiving taste.

Chinnakkaruppan and Rosenblum now use this experimental method to investigate the molecular mechanisms in the gustatory cortex that enable the internal representation (or memory trace) of a new taste to be associated with an unwell feeling that occurs much later.

The results of the experiments show that rats will avoid food with a certain flavor if they feel unwell within eight hours of eating it. However, the response of the rats differs depending on when the rat becomes ill. Underpinning these behaviors is the formation of two parallel internal representations of the new taste: a short-term, robust trace that lasts for three hours; and a parallel, longer lasting, weaker trace that lasts for eight hours to associate the taste with its outcome. The weaker, longer-lasting memory trace only forms if the shorter, stronger trace also occurs.

Chinnakkaruppan and Rosenblum found that forming the shorter, stronger memory requires the activity of a signaling pathway in the gustatory cortex that involves biochemical molecules called NMDAR-CaMKII-GluA1. These molecules can increase the strength of signaling between neurons and are already implicated in learning and memory. The next challenge is to put this newly identified molecular mechanism within the relevant neural circuit in the gustatory cortex.

Association between events in time and space is a major mechanism for all animals, including humans, to learn about the world, which can potentially change their behavior in future circumstances. The most influential books concerning learning theory published early in the 20th century were Animal Intelligence (1911), wherein Thorndike proposed that the rate of learning diminishes as the interval between response and discomfort/satisfaction is increased (Thorndike, 1911), and Conditioned Reflexes (1927), wherein Pavlov highlighted that a connection is formed in the nervous system, not between a conditioned stimulus (CS) and unconditioned stimulus (US) separated in time, but between the sensory aftereffects, i.e., trace of the CS, and the US (Pavlov, 1927). Thus, in essence, these theories mention that temporal contiguity has a vital influence on associative learning.

The prevailing dogma for cellular mechanisms underlying associative learning is based on modifications of Hebb’s pioneering cell assembly theory (1949) which posits that ‘cells which fire together, wire together', and thus the firing cells epitomize the internal representations of the two sensory events related in time (Hebb, 1949; Miltner et al., 1999; Nabavi et al., 2014; Yiu et al., 2014; Johansen et al., 2014). Indeed, it has been shown that at least in certain cases, selective facilitation of densely connected neurocircuits through excitability and/or synaptic plasticity, as a result of an experience, constitutes the basis for learning and memory (Nabavi et al., 2014; Yiu et al., 2014; Johansen et al., 2014).

Most of these studies have examined the cellular and molecular mechanisms underlying trace associative learning processes using classical conditioning paradigms in which the association takes place within the timescale of msec to sec (Johansen et al., 2014; Kitamura et al., 2014; Beylin et al., 2001). However, evolution has produced other type(s) of associative learning, wherein the CS and US can be experienced several minutes to hours apart. For example, the inter-time-interval (ITI) between the CS (novel taste) and the US (visceral information) in the conditioned taste aversion (CTA) paradigm can last for hours (Garcia et al., 1955; 1985; Kalat and Rozin, 1971; 1973; Chambers, 1990; Rosenblum et al., 1993; 1997; Yamamoto et al., 1995; Hashikawa et al., 2013; Adaikkan and Rosenblum, 2012; Stern et al., 2013; Inberg et al., 2013; Chinnakkaruppan et al., 2014; Parkes et al., 2014; Sano et al., 2014). Here, we attempted to understand the mechanisms that enable the taste memory trace to be associated with visceral information after such a long time.

In order to evaluate the effect of the ITI between taste and malaise in CTA learning (ITI-CTA) quantitatively and qualitatively, we conducted a behavioral experiment, in which rats were presented with a novel taste (0.1% saccharin; CS) and later were i.p. injected with lithium chloride (0.14 M LiCl; US) at increasing time points ranging between 1 hr and 20 hr (Figure 1A). In agreement with previous findings, we found that CTA was acquired after separating the taste and malaise stimuli for up to 8 hr but not 20 hr (Figure 1B, Figure 1—source data 1 and Figure 1—figure supplement 1) (Kalat and Rozin, 1971; Kalat and Rozin, 1973; Rozin and Kalat, 1971; Revusky, 1971; Gutiérrez et al., 2003; Koh et al., 2009; Chinnakkaruppan et al., 2014). Interestingly, non-parametric cluster analysis revealed that there are two different clusters among 1–8 hr ITI-CTAs: 1–3 hr (hereinafter short-trace) and 4–8 hr (hereinafter long-trace) ITI-CTAs (Figure 1B).

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Statistical analysis of Figure 1.

Kruskal-Wallis non-parametric ANOVA (1B, 1E), cluster analysis (1B), Mann-Whitney test (1B) and Friedman's non-parametric repeated measures ANOVA (1C) and independent samples t-test (1F) were conducted to analyse the group effect.

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Consistent with the retrieval results, long-trace (4–8 hr) CTA groups exhibited a similar faster CTA extinction pattern, whereas short-trace (1–3 hr) CTAs exhibited a slower extinction. The long-trace CTAs were not different from an unpaired control group by extinction day4, in comparison with day7 aversion index (AI) in short-trace CTAs (Figure 1C and Figure 1—source data 1). Reverse conditioning did not lead to taste aversion (Figure 1E), which indicates the importance of temporal order between taste and nausea in CTA learning. The strength of an association can be affected by ITI between CS and US or by the strength of US. We thus conducted an ITI-CTA with a weak US (0.025M LiCl) to test whether weaker CTA learning can be acquired when an association is made during the short- (1–3 hr) but not the long-trace (4–8 hr) intervals. We found that CTA was formed following the short-trace intervals, and interestingly, also following a 4 hr but not 5 hr trace interval. Together these results suggest that there is a shift in the associability of the taste memory trace at around 3–4 hr after exposure to the taste stimulus (Figure 1D).

The long ITI between CS and US in CTA is reasonable from physiological, ecological, and evolutionary perspectives. The minutes to hours process of digestion followed by absorption may dictate the animal to associate between the taste consumed and its delayed physiological consequences (for more information see reviews, Gal-Ben-Ari and Rosenblum, 2012; Kong and Singh, 2008).

It is possible that depending on the ITI there are two taste memory traces (as indicated by the behavioral readout): the first one is strong, but lasts for approximately 3 hr (short-trace), whereas the second trace is weak, but lasts for up to 8 hr (long-trace). This observation is further supported by the previous demonstration that micro-injection of the protein synthesis inhibitor, anisomycin, into the insular cortex (IC) within 3 hr but not 4 hr after the taste inhibits associative taste memory in the latent inhibition of CTA paradigm (Merhav and Rosenblum, 2008). It is therefore plausible to hypothesize that the underlying biological mechanism(s) of taste-nausea association may differ between short- and long-trace CTAs. Next, we set out to test this hypothesis.

Given ample evidence that novel taste experience impacts the phospho-proteome in the IC (see reviews, Adaikkan and Rosenblum, 2012; Gal-Ben-Ari and Rosenblum, 2012) and the idea that calcium calmodulin-dependent protein kinase II (CaMKII) has the potential to store information (Lisman, 2014 and the references therein), we aimed to test the possibility that CaMKIIα, through auto-phosphorylation (at T286; which can act as a molecular positive feedback loop), maintains the taste memory trace in the IC. Moreover, we were encouraged by the previous report that showed that novel taste consumption increases CaMKIIα expression in the IC (Belelovsky et al., 2005). Therefore, we followed up this finding and expanded it by measuring the phosphorylation/expression levels of CaMKIIα in the IC for up to 8 hr after the consumption of novel taste. We subjected IC samples of rats exposed to either water or novel taste solution (0.1% saccharin) to biochemical fractionation (Stern et al., 2013), and examined the phosphorylation and expression levels of CaMKIIα in the crude synaptosomal fraction (P2-fraction) by Western-blotting analysis (Figure 2A–C and Figure 2—figure supplement 1). We replicated the finding from the previous report (Figure 2—figure supplement 2), that 15 min after novel taste consumption, CaMKIIα expression is increased in the IC. Interestingly, we observed that the T286 phosphorylation of CaMKIIα (pT286CaMKIIα) was increased in the P2-fraction 30 min following novel taste consumption, persisting for up to 3 hr (1 and 3 hr), but not 5 or 8 hr afterwards (Figure 2D,E and Figure 2—figure supplement 1,2).

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Statistical analysis of Figure 2.

Independent samples t-test (2E, 2F, 2G) and one way ANOVA (2I, 2J, 2K) were conducted to analyse the group effect.

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There was no significant difference in T305 phosphorylation of CaMKIIα (pT305CaMKIIα; a phosphorylation site which prevents calcium/calmodulin activation of CaMKII) between water control and novel taste groups at any time point (Figure 2D–G and Figure 2—figure supplement 1,2). Intriguingly, the temporal dynamics of pT286CaMKIIα in the IC after the novel taste experience corresponds to the short-trace timescale, indicating a possible link between CaMKIIα and short-trace CTA learning.

It is possible that NMDAR activation is upstream to CaMKIIα phosphorylation, because activation of NMDAR in the IC is crucial for taste-malaise association and NMDAR regulates CaMKIIα phosphorylation (Rosenblum et al., 1997; Ferreira et al., 2002; Sanhueza et al., 2011; Halt et al., 2012; Parkes et al., 2014). Therefore, we examined if NMDAR activation is necessary for novel taste-dependent CaMKIIα activation. Indeed, novel taste experience-induced phosphorylation of T286CaMKIIα is NMDAR-dependent, since i.p. injection of the NMDAR antagonist, MK801 (0.2 mg/kg b.w), 30 min before novel taste learning precluded phosphorylation of T286CaMKIIα in the IC (Figure 2H,I and Figure 2—figure supplement 3). pT305CaMKIIα and total CaMKIIα levels did not differ among the water group injected with saline and the novel taste groups injected with either saline or MK801 (Figure 2J,K and Figure 2—figure supplement 3).

Taste memory trace can persist for an association, and at the very same time can undergo sensory information processing to form incidental taste learning (Chinnakkaruppan et al., 2014; Rosenberg et al., 2014). Therefore, the correlation between the consumption of novel taste and NMDAR-dependent T286CaMKIIα phosphorylation can serve the taste memory trace for an association, and/or incidental taste memory. In order to dissociate between these possibilities, we investigated the role of NMDAR and CaMKIIα in non-associative incidental and associative CTA learning by micro-infusing the NMDAR antagonist APV (10 μg/1 μl/hemisphere) or CaMKIIα inhibitor TatCN21 (0.3 nM/1 μl/ hemisphere (Buard et al., 2010)) bilaterally into the IC. Consistent with the literature, our data reveal that NDMAR in the IC is dispensable for incidental taste learning but necessary for CTA learning (Figure 3—figure supplement 1) (Rosenblum et al., 1997; Barki-Harrington et al., 2009; Parkes et al., 2014). We also found that CaMKIIα in the IC is dispensable for incidental taste learning but necessary for CTA learning (Figure 3—figure supplement 1,2).

Given that NMDAR-dependent T286CaMKIIα phosphorylation in the IC is required specifically for CTA, we sought to test whether NMDAR-CaMKIIα signaling in the IC maintains the taste memory trace for the association with the US by infusing the respective antagonist or inhibitor into the IC at various time points between the CS and US. We micro-injected the NMDAR antagonist into the IC 25 min from the beginning of the taste consumption in 1 hr ITI-CTA conditioning with a weak US and observed an attenuated CTA memory, consistent with a previous report in which a strong US was administered (Rosenblum et al., 1997; but also see Ferreira et al., 2002) (Figure 3—figure supplement 3A). Intriguingly, we did not observe any effect when we made a similar manipulation in the IC 4 hr after the taste consumption in 5 hr ITI-CTA conditioning with a strong US (Figure 3—figure supplement 3B; all the remaining experiments were done with a strong US). Next, we micro-injected CaMKIIα inhibitor TatCN21 or Tat control (Tatcont) into the IC 25 min from the beginning of the taste consumption in short-trace 3 hr ITI-CTA conditioning, and observed an attenuated CTA memory (Figure 3B and Figure 3—source data 1). We micro-injected TatCN21 into the IC 25 min (i.e. short-trace timescale) from the beginning of the taste consumption in the long-trace 5 h ITI-CTA conditioning, and also observed an attenuated CTA memory (Figure 3C). However, consistent with the temporal dynamics of CaMKIIα phosphorylation in the IC, CaMKIIα inhibition in the IC 4 h after the taste consumption in the long-trace 5 hr ITI-CTA conditioning had no effect on CTA memory (Figure 3D).

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Statistical analysis of Figure 3.

Independent samples t-test was conducted to analyse the group effect. The differences between the variances of groups were corrected following Levene’s test for equality of variances.

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These results indicate that novel taste consumption induces NMDAR-dependent T286CaMKIIα phosphorylation in the IC, which is necessary for maintaining the short taste memory trace for the association with visceral information. If this is the case, then inhibiting CaMKII 2 hr after novel taste consumption in 3 hr ITI-CTA should still impede the CS-US association. To answer this, we performed a 3 hr ITI-CTA experiment in which we micro-infused CaMKIIα inhibitor TatCN21 2 hr after the taste consumption, and administered the LiCl 1 hr later. We observed a small non-significant effect (Figure 3E). It is thus more likely that T286CaMKIIα phosphorylation in the IC is necessary for the development of the taste memory trace for the association and downstream target/s play a vital role in maintaining the taste memory trace for the CS-US association.

CaMKII-dependent modulation of GluA1-containing AMPA receptors and GluN2B-containing NMDARs has been implicated in different forms of learning, memory and synaptic plasticity, and can induce changes in synaptic strength (Hayashi et al., 2000; Whitlock et al., 2006; Sanhueza et al., 2011). Therefore, we investigated whether pT286CaMKIIα modulates GluA1 and/or GluN2B phosphorylation/expression in the IC after novel taste experience. Indeed, experiencing novel taste increased total GluA1 but not pS831GluA1 or pS1303GluN2B in the synaptosomal fraction in the IC 1 hr later (Figure 4A and Figure 4—figure supplement 1). Linear regression analysis revealed a positive correlation between pT286CaMKIIα and GluA1 expression in animals sampling novel taste (Figure 4B and Figure 4—figure supplement 1).

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Statistical analysis of Figure 4.

Independent samples t-test (4A, 4C, 4D, 4E, 4F, 4G) was conducted to analyse the group effect. Pearson’s correlation was used to analyse the association between the association between GluA1 and pT286CaMKIIα (4B).

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To examine whether pT286CaMKIIα is required for the increased GluA1 expression in the P2-fraction after the taste consumption, we micro-infused CaMKIIα inhibitor TatCN21 into the IC 30 min before novel taste exposure and measured GluA1 expression 1 hr later. TatCN21 application reduced novel taste experience-dependent GluA1 expression in the P2-fraction (Figure 4C and Figure 4—figure supplement 2). Moreover, NMDAR antagonist MK801 injection 30 min before the novel taste also reduced GluA1 expression (Figure 4—figure supplement 3), indicating that NMDAR- and CaMKIIα-dependent increased synaptic expression of GluA1 in the IC mediates short-trace taste memory for the association with the US.

If indeed GluA1 in the IC mediates the taste memory trace for the association, we hypothesized that pharmacological inhibition of AMPAR following taste experience would interfere with the association with the US. Microinjection of AMPAR antagonist, CNQX, (1 μl/hemisphere; 3 nM/μl) (Tse et al., 2011) into the IC 1 hr after the taste consumption in short-trace 3 hr ITI-CTA conditioning attenuated the CTA memory (Figure 4D). Interestingly, CNQX micro-injection into the IC 2 hr after the taste consumption in 3 hr ITI-CTA learning also attenuated CTA memory (Figure 4E). Furthermore, microinjection of CNQX into the IC 1 hr (short-trace timescale) after the taste consumption in long-trace 5 hr ITI-CTA conditioning also attenuated the CTA memory (Figure 4F). However, in accordance with the timescale of short-trace and CaMKIIα-GluA1 activation, we found that CNQX micro-injection 4 hr after novel taste consumption in 5 hr ITI-CTA had no effect on CTA (Figure 4G).

It is intriguing that the inhibition of CaMKIIα or AMPAR in the IC during short-trace timescale attenuated short-trace CTA memory. In addition, inhibition of the CaMKIIα-GluA1 pathway during short-trace timescale also attenuated long-trace CTA. However, long-trace CTA was not affected when CaMKIIα-GluA1 pathway in the IC was left intact for 3 hr. Together, these data reveal that there are two parallel taste memory traces which are subserved by different mechanisms: one that is robust but decays quickly, and another which is weak but lasts longer.

It is important to note that NMDAR-CaMKIIα-AMPAR in the IC are not necessary for incidental taste learning and memory, whereas muscarinic receptors are critical for incidental taste learning (Ferreira et al., 2002; Parkes et al., 2014). It is interesting that different molecular mechanisms take place in the same cortex, the insular cortex, to mediate associative and non-associative taste learning and memory.

We should emphasize that different pharmacological manipulation (APV, TatCN21, CNQX) in the IC when applied within 3 hr after the CS presentation only modifies but not completely erases the CTA memory. It is also noteworthy that several previous reports demonstrate that pharmacological perturbations in the IC during CTA conditioning only partially disrupt CTA memory, and that the disrupted CTA appears similar to long-trace CTA memory (Rosenblum et al., 1993; Rosenblum et al., 1997; Berman et al., 2000; Eisenberg et al., 2003; Gutiérrez et al., 2003; Barki-Harrington et al., 2009; Inberg et al., 2013; Stern et al., 2013; Parkes et al., 2014). Thus, it is possible that over time, there is a transformation from the taste memory trace dominance (i.e. short-trace) and IC dependency to multiple memory traces (i.e. during long-trace) due to the multi-channeled background experience, e,g., time, space, food digestion-dependent body physiology, etc. Such transformation, which involves multiple memory traces, for instance, episodic memory trace (how long ago the taste was consumed and under what context?), may result in a wider distribution and processing of the taste memory trace with the passage of time (Kalat and Rozin, 1971; Revusky, 1971; Kalat and Rozin, 1973; Koh et al., 2009; Chinnakkaruppan et al., 2014).

Since the initial demonstration of CTA by Garcia (1955), several studies have proposed that the reduced aversion index followed by the increased interval between the taste and malaise in CTA conditioning could be attributed to (i) the learned safety about the taste, (ii) accumulating background interference, and (iii) taste memory trace decay (Kalat and Rozin, 1971; Rozin and Kalat, 1971; Revusky, 1971; Kalat and Rozin, 1973; Gutiérrez et al., 2003; Koh et al., 2009; Chinnakkaruppan et al., 2014). First, in support of the learned safety theory, and in line with the previous reports, we found that animals exhibit higher preference to the taste hours following the first-time experience (Figure 3—figure supplement 4) (Kalat and Rozin, 1973; Gutiérrez et al., 2003). Second, with regards to background interference theory, we propose that the multi-channeled background information can be viewed as episodic components and that, for example, the hippocampus is critical to assimilate the episodic component in CTA (Chinnakkaruppan et al., 2014; Koh et al., 2009). Third, in line with the trace decay theory, our findings demonstrate that the robust short-trace fades within 3 hr in the IC but the long-trace, which is weak, lasts longer. Moreover, the non-linear decay of the short trace suggests that it dominates over the long trace, so that and when the former fades, the latter is revealed (Figure 4—figure supplement 5). We propose a multiple memory trace theory, wherein we suggest that CTA learning may engage multiple memory systems to take part in the long-trace associative process and that after experiencing the taste, the IC plays a critical role during short-trace (Figure 4—figure supplement 5).

Although multiple lines of evidence support that NMDAR-CaMKII-AMPAR signaling in the IC plays a crucial role in maintaining the short-trace for the association with the US, we do not rule out the possibility that other molecular mechanisms in the IC may participate as well in the long-trace CTA. For instance, protein acetylation, phosphorylation, and induction of proteins were observed in the IC many hours following novel taste consumption, and it is possible that these correlative molecular changes (proteins synthesis and epigenetic regulation) may contribute to the long-trace CTA (Swank and Sweatt, 2001; Yefet et al., 2006; Elkobi et al., 2008; for reviews see Gal-Ben-Ari and Rosenblum, 2012).

Conceptually, on the one hand, our short-trace CTA complies with the commonly held assumption of neuroscience theories of associative learning that convergence of CS and US information onto particular cells/circuits leads to changes in synaptic strength at the synapses mediating the CS input to those commonly activated cells/circuit and that it underlies association formation, i.e. Hebbian plasticity (NMDAR-CaMKII-AMPARs in the IC probably in co-ordination with the amygdala; Yasoshima et al., 2000; Ferreira et al., 2005; Hashikawa et al., 2013). On the other hand, the long-trace CTA challenges this assumption, and it is possible that because of the multi-channeled background information with an increasing time interval as discussed above, homeostatic plasticity may play a crucial role to encode multiple memory traces via coordinating several brain structures such as the IC, amygdala, hippocampus, prefrontal cortices in the long-trace CTA (Vitureira et al., 2012; Turrigiano, 2012; Pozo and Goda, 2010). Overall, our data suggest that the neural mechanisms of associative learning can be an assimilation of multiple memory traces when the two relevant experiences are separated in time.

1. 1. Adaikkan C
   2. Rosenblum K

   (2012) The role of protein phosphorylation in the gustatory cortex and amygdala during taste learning

   Experimental Neurobiology 21:37–51.

   https://doi.org/10.5607/en.2012.21.2.37

   • Google Scholar
2. 1. Barki-Harrington L
   2. Elkobi A
   3. Tzabary T
   4. Rosenblum K

   (2009) Tyrosine phosphorylation of the 2B subunit of the NMDA receptor is necessary for taste memory formation

   Journal of Neuroscience 29:9219–9226.

   https://doi.org/10.1523/JNEUROSCI.5667-08.2009

   • Google Scholar
3. 1. Belelovsky K
   2. Elkobi A
   3. Kaphzan H
   4. Nairn AC
   5. Rosenblum K

   (2005) A molecular switch for translational control in taste memory consolidation

   European Journal of Neuroscience 22:2560–2568.

   https://doi.org/10.1111/j.1460-9568.2005.04428.x

   • Google Scholar
4. 1. Berman DE
   2. Hazvi S
   3. Neduva V
   4. Dudai Y

   (2000)

   The role of identified neurotransmitter systems in the response of insular cortex to unfamiliar taste: activation of ERK1-2 and formation of a memory trace

   The Journal of Neuroscience  20:7017–7023.

   • Google Scholar
5. 1. Beylin AV
   2. Gandhi CC
   3. Wood GE
   4. Talk AC
   5. Matzel LD
   6. Shors TJ

   (2001) The role of the hippocampus in trace conditioning: temporal discontinuity or task difficulty?

   Neurobiology of Learning and Memory 76:447–461.

   https://doi.org/10.1006/nlme.2001.4039

   • Google Scholar
6. 1. Buard I
   2. Coultrap SJ
   3. Freund RK
   4. Lee Y-S
   5. dell'acqua ML
   6. Silva AJ
   7. Bayer KU

   (2010) CaMKII "autonomy" is required for initiating but not for maintaining neuronal long-term information storage

   Journal of Neuroscience 30:8214–8220.

   https://doi.org/10.1523/JNEUROSCI.1469-10.2010

   • Google Scholar
7. 1. Chambers KC

   (1990) A neural model for conditioned taste aversions

   Annual Review of Neuroscience 13:373–385.

   https://doi.org/10.1146/annurev.ne.13.030190.002105

   • Google Scholar
8. 1. Chinnakkaruppan A
   2. Wintzer ME
   3. Mchugh TJ
   4. Rosenblum K

   (2014) Differential contribution of hippocampal subfields to components of associative taste learning

   Journal of Neuroscience 34:11007–11015.

   https://doi.org/10.1523/JNEUROSCI.0956-14.2014

   • Google Scholar
9. 1. Eisenberg M

   (2003) Stability of retrieved memory: inverse correlation with trace dominance

   Science 301:1102–1104.

   https://doi.org/10.1126/science.1086881

   • Google Scholar
10. 1. Elkobi A
    2. Ehrlich I
    3. Belelovsky K
    4. Barki-Harrington L
    5. Rosenblum K

    (2008) ERK-dependent PSD-95 induction in the gustatory cortex is necessary for taste learning, but not retrieval

    Nature Neuroscience 11:1149–1151.

    https://doi.org/10.1038/nn.2190

    • Google Scholar
11. 1. Ferreira G
    2. Gutierrez R
    3. de La Cruz V
    4. Bermudez-Rattoni F

    (2002) Differential involvement of cortical muscarinic and NMDA receptors in short- and long-term taste aversion memory

    European Journal of Neuroscience 16:1139–1145.

    https://doi.org/10.1046/j.1460-9568.2002.02174.x

    • Google Scholar
12. 1. Ferreira G
    2. Miranda MI
    3. Cruz V
    4. Rodríguez-Ortiz CJ
    5. Bermúdez-Rattoni F

    (2005) Basolateral amygdala glutamatergic activation enhances taste aversion through NMDA receptor activation in the insular cortex

    European Journal of Neuroscience 22:2596–2604.

    https://doi.org/10.1111/j.1460-9568.2005.04440.x

    • Google Scholar
13. 1. Gal-Ben-Ari S
    2. Rosenblum K

    (2012) Molecular mechanisms underlying memory consolidation of taste information in the cortex

    Frontiers in Behavioral Neuroscience 5:87.

    https://doi.org/10.3389/fnbeh.2011.00087

    • Google Scholar
14. 1. Garcia J
    2. Kimeldorf DJ
    3. Koelling RA

    (1955)

    Conditioned aversion to saccharin resulting from exposure to gamma radiation

    Science 122:157–158.

    • Google Scholar
15. 1. Garcia J
    2. Lasiter PS
    3. Bermudez-Rattoni F
    4. Deems DA

    (1985) A general theory of aversion learning

    Annals of the New York Academy of Sciences 443:8–21.

    https://doi.org/10.1111/j.1749-6632.1985.tb27060.x

    • Google Scholar
16. 1. Gutiérrez R
    2. Rodriguez-Ortiz CJ
    3. de La Cruz V
    4. Núñez-Jaramillo L
    5. Bermudez-Rattoni F

    (2003) Cholinergic dependence of taste memory formation: evidence of two distinct processes

    Neurobiology of Learning and Memory 80:323–331.

    https://doi.org/10.1016/S1074-7427(03)00066-2

    • Google Scholar
17. 1. Halt AR
    2. Dallapiazza RF
    3. Zhou Y
    4. Stein IS
    5. Qian H
    6. Juntti S
    7. Wojcik S
    8. Brose N
    9. Silva AJ
    10. Hell JW

    (2012) CaMKII binding to GluN2B is critical during memory consolidation

    The EMBO Journal 31:1203–1216.

    https://doi.org/10.1038/emboj.2011.482

    • Google Scholar
18. 1. Hashikawa K
    2. Naka M
    3. Nakayama D
    4. Matsumoto N
    5. Neve R
    6. Matsuki N

    (2013) Blockade of stimulus convergence in amygdala neurons disrupts taste associative learning

    Journal of Neuroscience 33:4958–4963.

    https://doi.org/10.1523/JNEUROSCI.5462-12.2013

    • Google Scholar
19. 1. Hayashi Y

    (2000) Driving AMPA receptors into synapses by LTP and CaMKII: requirement for GluR1 and PDZ domain interaction

    Science 287:2262–2267.

    https://doi.org/10.1126/science.287.5461.2262

    • Google Scholar
20. Book

    1. Hebb DO

    (1949)

    Organization of Behavior

    New York: Wiley.

    • Google Scholar
21. 1. Inberg S
    2. Elkobi A
    3. Edri E
    4. Rosenblum K

    (2013) Taste familiarity is inversely correlated with Arc/Arg3.1 hemispheric lateralization

    Journal of Neuroscience 33:11734–11743.

    https://doi.org/10.1523/JNEUROSCI.0801-13.2013

    • Google Scholar
22. 1. Johansen JP
    2. Diaz-Mataix L
    3. Hamanaka H
    4. Ozawa T
    5. Ycu E
    6. Koivumaa J
    7. Kumar A
    8. Hou M
    9. Deisseroth K
    10. Boyden ES
    11. Ledoux JE

    (2014) Hebbian and neuromodulatory mechanisms interact to trigger associative memory formation

    Proceedings of the National Academy of Sciences of the United States of America 111:E5584.

    https://doi.org/10.1073/pnas.1421304111

    • Google Scholar
23. 1. Kalat JW
    2. Rozin P

    (1971) Role of interference in taste-aversion learning

    Journal of Comparative and Physiological Psychology 77:53–58.

    https://doi.org/10.1037/h0031585

    • Google Scholar
24. 1. Kalat JW
    2. Rozin P

    (1973) "Learned safety" as a mechanism in long-delay taste-aversion learning in rats

    Journal of Comparative and Physiological Psychology 83:198–207.

    https://doi.org/10.1037/h0034424

    • Google Scholar
25. 1. Kitamura T
    2. Pignatelli M
    3. Suh J
    4. Kohara K
    5. Yoshiki A
    6. Abe K
    7. Tonegawa S

    (2014) Island cells control temporal association memory

    Science 343:896–901.

    https://doi.org/10.1126/science.1244634

    • Google Scholar
26. 1. Koh MT
    2. Wheeler DS
    3. Gallagher M

    (2009) Hippocampal lesions interfere with long-trace taste aversion conditioning

    Physiology & Behavior 98:103–107.

    https://doi.org/10.1016/j.physbeh.2009.04.017

    • Google Scholar
27. 1. Kong F
    2. Singh RP

    (2008) Disintegration of solid foods in human stomach

    Journal of Food Science 73:R67–R80.

    https://doi.org/10.1111/j.1750-3841.2008.00766.x

    • Google Scholar
28. 1. Lisman JE

    (2014) Refreshing memories

    eLife 3:e02041.

    https://doi.org/10.7554/eLife.02041

    • Google Scholar
29. 1. Merhav M
    2. Rosenblum K

    (2008) Facilitation of taste memory acquisition by experiencing previous novel taste is protein-synthesis dependent

    Learning & Memory 15:501–507.

    https://doi.org/10.1101/lm.986008

    • Google Scholar
30. 1. Miltner WH
    2. Braun C
    3. Arnold M
    4. Witte H
    5. Taub E

    (1999) Coherence of gamma-band EEG activity as a basis for associative learning

    Nature 397:434–436.

    https://doi.org/10.1038/17126

    • Google Scholar
31. 1. Nabavi S
    2. Fox R
    3. Proulx CD
    4. Lin JY
    5. Tsien RY
    6. Malinow R

    (2014) Engineering a memory with LTD and LTP

    Nature 511:348–352.

    https://doi.org/10.1038/nature13294

    • Google Scholar
32. 1. Parkes SL
    2. de La Cruz V
    3. Bermúdez-Rattoni F
    4. Coutureau E
    5. Ferreira G

    (2014) Differential role of insular cortex muscarinic and NMDA receptors in one-trial appetitive taste learning

    Neurobiology of Learning and Memory 116:112–116.

    https://doi.org/10.1016/j.nlm.2014.09.008

    • Google Scholar
33. Book

    1. Pavlov IP

    (1927)

    Conditioned reflexes: an investigation of the physiological activity of the cerebral cortex

    Oxford, UK: Oxford University Press.

    • Google Scholar
34. 1. Pozo K
    2. Goda Y

    (2010) Unraveling mechanisms of homeostatic synaptic plasticity

    Neuron 66:337–351.

    https://doi.org/10.1016/j.neuron.2010.04.028

    • Google Scholar
35. 1. Revusky S

    (1971) The role of interference in association over a delay

    Animal Memory pp. 155–213.

    https://doi.org/10.1016/B978-0-12-355050-7.50009-6

    • Google Scholar
36. 1. Rosenberg T
    2. Gal-Ben-Ari S
    3. Dieterich DC
    4. Kreutz MR
    5. Ziv NE
    6. Gundelfinger ED
    7. Rosenblum K

    (2014) The roles of protein expression in synaptic plasticity and memory consolidation

    Frontiers in Molecular Neuroscience 7:86.

    https://doi.org/10.3389/fnmol.2014.00086

    • Google Scholar
37. 1. Rosenblum K
    2. Berman DE
    3. Hazvi S
    4. Lamprecht R
    5. Dudai Y

    (1997)

    NMDA receptor and the tyrosine phosphorylation of its 2B subunit in taste learning in the rat insular cortex

    The Journal of Neuroscience 17:5129–5135.

    • Google Scholar
38. 1. Rosenblum K
    2. Meiri N
    3. Dudai Y

    (1993) Taste memory: the role of protein synthesis in gustatory cortex

    Behavioral and Neural Biology 59:49–56.

    https://doi.org/10.1016/0163-1047(93)91145-D

    • Google Scholar
39. 1. Rozin P
    2. Kalat JW

    (1971) Specific hungers and poison avoidance as adaptive specializations of learning

    Psychological Review 78:459–486.

    https://doi.org/10.1037/h0031878

    • Google Scholar
40. 1. Sanhueza M
    2. Fernandez-Villalobos G
    3. Stein IS
    4. Kasumova G
    5. Zhang P
    6. Bayer KU
    7. Otmakhov N
    8. Hell JW
    9. Lisman J

    (2011) Role of the CaMKII/NMDA receptor complex in the maintenance of synaptic strength

    Journal of Neuroscience 31:9170–9178.

    https://doi.org/10.1523/JNEUROSCI.1250-11.2011

    • Google Scholar
41. 1. Sano Y
    2. Shobe Justin L.
    3. Zhou M
    4. Huang S
    5. Shuman T
    6. Cai Denise J.
    7. Golshani P
    8. Kamata M
    9. Silva Alcino J.

    (2014) CREB regulates memory allocation in the insular cortex

    Current Biology 24:2833–2837.

    https://doi.org/10.1016/j.cub.2014.10.018

    • Google Scholar
42. 1. Stern E
    2. Chinnakkaruppan A
    3. David O
    4. Sonenberg N
    5. Rosenblum K

    (2013) Blocking the eIF2α kinase (pKR) enhances positive and negative forms of cortex-dependent taste memory

    The Journal of Neuroscience 33:2517–2525.

    https://doi.org/10.1523/JNEUROSCI.2322-12.2013

    • Google Scholar
43. 1. Swank MW
    2. Sweatt JD

    (2001)

    Increased histone acetyltransferase and lysine acetyltransferase activity and biphasic activation of the ERK/RSK cascade in insular cortex during novel taste learning

    The Journal of Neuroscience 21:3383–3391.

    • Google Scholar
44. Book

    1. Thorndike EL

    (1911)

    Animal intelligence

    New York, NY: Macmillan.

    • Google Scholar
45. 1. Tse D
    2. Takeuchi T
    3. Kakeyama M
    4. Kajii Y
    5. Okuno H
    6. Tohyama C
    7. Bito H
    8. Morris RGM

    (2011) Schema-dependent gene activation and memory encoding in neocortex

    Science 333:891–895.

    https://doi.org/10.1126/science.1205274

    • Google Scholar
46. 1. Turrigiano G

    (2012) Homeostatic synaptic plasticity: local and global mechanisms for stabilizing neuronal function

    Cold Spring Harbor Perspectives in Biology 4:a005736.

    https://doi.org/10.1101/cshperspect.a005736

    • Google Scholar
47. 1. Vitureira N
    2. Letellier M
    3. Goda Y

    (2012) Homeostatic synaptic plasticity: from single synapses to neural circuits

    Current Opinion in Neurobiology 22:516–521.

    https://doi.org/10.1016/j.conb.2011.09.006

    • Google Scholar
48. 1. Whitlock JR

    (2006) Learning induces long-term potentiation in the hippocampus

    Science 313:1093–1097.

    https://doi.org/10.1126/science.1128134

    • Google Scholar
49. 1. Yamamoto T
    2. Fujimoto Y
    3. Shimura T
    4. Sakai N

    (1995) Conditioned taste aversion in rats with excitotoxic brain lesions

    Neuroscience Research 22:31–49.

    https://doi.org/10.1016/0168-0102(95)00875-T

    • Google Scholar
50. 1. Yasoshima Y
    2. Morimoto T
    3. Yamamoto T

    (2000) Different disruptive effects on the acquisition and expression of conditioned taste aversion by blockades of amygdalar ionotropic and metabotropic glutamatergic receptor subtypes in rats

    Brain Research 869:15–24.

    https://doi.org/10.1016/S0006-8993(00)02397-0

    • Google Scholar
51. 1. Yefet K
    2. Merhav M
    3. Kuulmann-vander S
    4. Elkobi A
    5. Belelovsky K
    6. Jacobson-Pick S
    7. Meiri N
    8. Rosenblum K

    (2006) Different signal transduction cascades are activated simultaneously in the rat insular cortex and hippocampus following novel taste learning

    European Journal of Neuroscience 24:1434–1442.

    https://doi.org/10.1111/j.1460-9568.2006.05009.x

    • Google Scholar
52. 1. Yiu Adelaide P
    2. Mercaldo V
    3. Yan C
    4. Richards B
    5. Rashid Asim J
    6. Hsiang Hwa-Lin Liz
    7. Pressey J
    8. Mahadevan V
    9. Tran Matthew M
    10. Kushner Steven A
    11. Woodin Melanie A
    12. Frankland Paul W
    13. Josselyn Sheena A

    (2014) Neurons are recruited to a memory trace based on relative neuronal excitability immediately before training

    Neuron 83:722–735.

    https://doi.org/10.1016/j.neuron.2014.07.017

    • Google Scholar

Author details

1. Chinnakkaruppan Adaikkan

   Sagol Department of Neurobiology, University of Haifa, Haifa, Israel

   Contribution

   CA, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist

2. Kobi Rosenblum

   1. Sagol Department of Neurobiology, University of Haifa, Haifa, Israel
   2. Center for Gene Manipulation in the Brain, University of Haifa, Haifa, Israel

   Contribution

   KR, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   kobir@psy.haifa.ac.il

   Competing interests

   The authors declare that no competing interests exist

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