CHAPTER

Neural Mechanisms of Odor Rule Learning

10 Edi Barkai1

Sagol Department of Neurobiology, University of Haifa, Haifa, Israel 1 Corresponding author: Tel.: 972-4-8288419, Fax: 972-4-8288763

Abstract Rats that are trained in a particularly difficult olfactory discrimination task demonstrate a dramatic increase in their capability to acquire memories of new odors once they have learned the first discrimination task. Such high-skill learning, termed “rule learning” or “learning set” (see Saar et al., 1998, 2001) is accompanied by a series postsynaptic cellular modifications which have three major traits:

a. They are widespread throughout the piriform cortex network. Both physiological and morphological modifications are found in most of the studied neurons. b. The time course in which these modifications appear and disappear is strongly correlated with the time course in which the skill is acquired and decayed. However, memories for specific odors outlast these modifications by far. Thus, the identified modifications are related to rule learning (learning how to learn), rather than to longterm memory for the specific odors for which the rats are trained. c. While the above-mentioned changes act to enhance single-cell excitability, others reduced it; synaptic inhibition is enhanced after learning and the subunit composition of the NMDA receptor is modified in a manner that favors activity-induced synaptic weakening over synaptic enhancement.

Keywords olfactory discrimination, rule learning, piriform cortex, pyramidal neurons, intrinsic excitability, postburst AHP, EPSCs, IPSCs

Two general types of learning-related cellular changes occur in the piriform cortex after olfactory learning: modifications in the intrinsic properties of neurons and modifications at the synapses interconnecting these neurons. These modifications differ in the dynamics in which they appear and are maintained. One day after learning, Progress in Brain Research, Volume 208, ISSN 0079-6123, http://dx.doi.org/10.1016/B978-0-444-63350-7.00010-3 © 2014 Elsevier B.V. All rights reserved.

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pyramidal neurons show enhanced neuronal excitability. This enhancement results from reduction conductance of calcium-dependent potassium current, the sIAHP, which mediates the late postburst afterhyperpolarization (AHP) and thus controls repetitive spike firing. Such enhanced excitability lasts for 3 days and is followed by a series of synaptic modifications. On the third day after learning, several forms of long-term enhancement in synaptic connections between layer II pyramidal neurons appear; postsynaptic enhancement of synaptic transmission is indicated by reduced rise time of postsynaptic potentials and increased amplitude of excitatory synaptic events. Formation of new synaptic connections is indicated by increased spine density along dendrites of these neurons. Enhanced inhibitory synaptic transmission is expressed in hyperpolarization of the reversal potential of the GABAA-mediated IPSP and increased amplitude of inhibitory synaptic events. Such synaptic modifications last for up to 5 days.

1 RULE LEARNING The ability to extract generalizable rules from specific experiences is a fundamental attribute of higher cognitive functioning. In the example of learning olfactory discrimination (OD) tasks, two different types of learning can be distinguished: acquiring the knowledge of how to perform a discrimination task (procedural memory) and acquiring the knowledge of the specific discriminative stimulus within the task (declarative memory). While our understanding of procedural and declarative memory (often referred to as implicit and explicit memory, respectively) has grown substantially over the past 30 years, the neurobiology of elaborate forms of memory—such as rule learning (also referred to as “learning set”)—is not well understood (Kesner, 2009). Thus, an animal model that would allow a comprehensive study of the biological mechanisms underlying rule learning is of utmost importance.

2 OD LEARNING IN RODENTS AS A MODEL FOR RULE LEARNING OD tasks in rodents provide an excellent framework to investigate rule learning (Saar et al., 1998; Slotnick et al., 2000). Rats, for which olfaction is a dominant sensory modality, can efficiently learn to discriminate between positive and negative cues in pairs of odors. Furthermore, rats demonstrate the capability to acquire rule learning of odor discrimination, which then significantly enhances their performance in discrimination tasks (Saar et al., 1998). In addition, “reversal tests” (presenting previously learned odors with reversed valence) show that rats can reliably recall previously learned odors few weeks after training, even if additional odor memory was acquired during that time. OD training is known to induce changes in encoding of the learned odor in networks of multiple brain areas including the olfactory bulb (OB) (Doucette and

2 OD Learning in Rodents as a Model for Rule Learning

Restrepo, 2008; Freeman and Schneider, 1982; Mandairon et al., 2006), piriform cortex (Kadohisa and Wilson, 2006; Moriceau et al., 2006; Roesch et al., 2007), orbitofrontal cortex (OFC) (Schoenbaum et al., 1998, 1999, 2003), amygdala (Motanis et al., 2014; Schoenbaum et al., 1998, 1999, 2003; Sullivan and Wilson, 1993), and hippocampus (Zelcer et al., 2006). Odor learning displays characteristics typically associated with higher order learning, such as object-oriented perception (Yeshurun and Sobel, 2010), pattern completion (Barnes et al., 2008), rule learning (Saar et al., 1998; Slotnick et al., 2000), and transitive inference (Dusek and Eichenbaum, 1997), and can serve as an excellent model for investigating high cognitive functions. Interestingly, and in line with its generalized nature, we have previously shown that learning of a particularly difficult OD task opens a period of accelerated learning of other odors, manifested as a dramatic increase in the rats’ capability to acquire memories of new odors once they have learned the first discrimination task, implying that rule learning has taken place (Fig. 1). Rule learning is accompanied by a series of long-lasting modifications in intrinsic neuronal properties of piriform cortex pyramidal neurons, as well as in their excitatory synaptic inputs (Cohen-Matsliah et al., 2007; Knafo et al., 2001, 2005; Saar and Barkai, 2003; Saar et al., 1998, 1999, 2002). Such changes, while required for W P

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FIGURE 1 Complex olfactory learning apparatus and rule learning. (A) Schematic description of the 4-arm maze. Protocols for trained and pseudo-trained rats are similar: an electronic “start” command opens randomly two out of eight valves (V), releasing a positive-cue odor (P) into one of the arms and a negative-cue odor (N) into another. Eight seconds later, the two corresponding guillotine doors (D) are lifted to allow the rat to enter the selected arms. Upon reaching the far end of an arm (90 cm long), the rat body interrupts an infrared beam (I, arrow) and a drop of drinking water is released from a water hose (W) into a small drinking well (for a trained rat—only if the arm contains the positive-cue odor, for pseudo-trained rat—randomly). A trial ends when the rat interrupts a beam, or in 10 s, if no beam is interrupted. A fan is operated for 15 s between trials to remove odors. (B) Trained rats demonstrate acquisition of rule learning. Seven consecutive days of training were required for this group to reach criterion for discriminating between the first pair of odors (80% correct choices). Other groups usually require the same period also. Discrimination between any new pair of odors, starting from the third pair and fourth, could be reached within 1 day. Values represent mean  SE. n ¼ 11 rats.

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memory storage, must be counter balanced by modifications that would prevent overspreading of activity and uncontrolled strengthening of synaptic connectivity (Hasselmo and Barkai, 1995). Indeed, synaptic inhibition is also strengthened (Brosh and Barkai, 2009; Saar et al., 2012), and the NMDA receptor subunit composition is modified to favor activity-dependent weakening (Quinlan et al., 2004) after rule learning.

2.1 Rule Learning-Induced Modulation of Intrinsic Neuronal Excitability While it is commonly agreed that changes in strength of connections between neurons in the relevant networks underlie memory storage, it has been pointed out that modifications in intrinsic neuronal properties may also account for learning-related behavioral changes. Specifically, widespread modulation of intrinsic neuronal plasticity has been suggested as a mechanism for metaplasticity (for review, see Sehgal et al., 2013).

2.1.1 Learning-Induced Enhancement of Neuronal Excitability Learning-induced enhancement in neuronal excitability has been shown in hippocampal neurons following classical conditioning of the trace eyeblink response (Moyer et al., 1996; Thompson et al., 1996) and the Morris water maze task (Oh et al., 2003), and in piriform cortex neurons following operant conditioning (Saar and Barkai, 2003; Saar et al., 1998, 2001). In hippocampal and piriform cortex neurons, this enhanced excitability is manifested in reduced spike frequency adaptation in response to prolonged depolarizing current applications (Moyer et al., 1996; Saar et al., 2001; Thompson et al., 1996). OD learning also results in enhanced neuronal excitability in CA1 hippocampal neurons (Zelcer et al., 2006). Neuronal adaptation in neocortical, hippocampal, and piriform cortex pyramidal neurons is modulated by medium and slow AHPs, generated by potassium currents, which develop following a burst of action potentials (Constanti and Sim, 1987; Madison and Nicoll, 1984; Saar et al., 2001; Schwindt et al., 1988). Indeed, it was shown in hippocampal and piriform cortex pyramidal neurons that the postburst AHP amplitude is reduced for several days after learning (Moyer et al., 1996; Saar et al., 1998) (Fig. 2A–C).

2.1.2 Functional Significance of Postburst AHP Reduction Several findings suggested that AHP reduction, and its consequent enhancement in neuronal excitability, is not the mechanism by which memories for specific sensory inputs or sequences of events are stored. Rather, it may be the mechanism that enables neuronal ensembles to enter into a state which may be best termed learning mode. This state lasts for up to several days, and its behavioral manifestation is enhanced learning capability in tasks that depend on these particular neuronal ensembles. Specifically, enhanced neuronal excitability sets a time window in which most neurons in the relevant neuronal network are more excitable, and thus

2 OD Learning in Rodents as a Model for Rule Learning

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FIGURE 2 The postburst AHP is transiently reduced after rule learning in most neurons from trained rats. (A) Postburst AHP measurements in a piriform cortex pyramidal neuron. Neuron was held at a membrane potential of 60 mV and an AHP was generated by a 100 ms depolarizing current step injection via the recording electrode, with intensity sufficient to generate a train of six action potentials. (B) Time course of AHP reduction in neurons from trained rats. Amplitudes of AHPs recorded in neurons from trained rats on different days after the beginning of training compared with AHPs in neurons from pseudo-trained rats recorded at the same day. (C) Cumulative frequency distribution of AHP amplitudes. Each point represents the AHP in one cell. AHP amplitudes in neurons from trained rats create a curve that smoothly shifts to the left along the x-axis, relative to the curve of neurons from pseudo-trained rats, indicating that AHP reduction occurs throughout the neuronal population. (D) Effect of apamin on the postburst AHP. When the medium and the slow AHPs’ peaks were clearly distinguishable, apamin affected only the amplitude of the first peak (D1). In the more common type of response, when the medium and slow AHPs overlapped, the amplitude of the single apparent peak was reduced by apamin (D2). (E) Averaged AHP amplitude in neurons from the three groups recorded in prior to and after apamin application. Averaged AHP amplitude in trained rats is significantly smaller compared to naive and pseudo-trained rats (**p < 0.01). This difference is maintained after apamin application. Values represent mean  SE.

activity-dependent synaptic modifications are more likely to occur (Moyer et al., 1996; Saar et al., 1998). The main evidences supporting this notion are: a. The averaged AHP amplitude in neurons from hippocampus and piriform cortex tends to return back to its initial value within days when training is suspended. This recovery is not accompanied by memory loss. However, rule learning (manifested as the enhanced ability to acquire new memories rapidly and efficiently) is strongly correlated with reduced postburst AHP; return of AHP to its initial value is accompanied by reduced learning capability.

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b. Before olfactory training, application of a cholinergic agent reduces the postburst AHP and blocking cholinergic activity delays rule learning. However, once rule learning is established, acetylcholine’s ability to affect the AHP is abolished and it also does not affect further acquisition of memories (Saar et al., 2001). c. Learning impairment in aged animals is accompanied by enhanced postburst AHP (Moyer et al., 2000). d. LTP is more readily induced when the AHP is reduced (Norris et al., 1998). Moreover, learning-induced postburst AHP reduction in the piriform cortex occurs 2 days before enhanced synaptic connectivity appears (Barkai and Saar, 2001; Knafo et al., 2001; Saar et al., 1999). e. Application of apamin, a venom that reduces the AHP by blocking the IAHP current, enhances hippocampal-dependent memory encoding, but not retention (Stackman et al., 2002). f. Finally, it was shown that in the process of OD learning, the postburst AHP is reduced and neuronal excitability is transiently enhanced in CA1 pyramidal neurons, while synaptic transmission remains at its control value. Such olfactory learning-induced increased excitability in hippocampal neurons enhances the rats’ learning capability in another hippocampus-dependent task, the Morris water maze (Zelcer et al., 2006). These evidences suggest that enhanced excitability of CA1 neurons may serve as a mechanism for generalized enhancement of hippocampus-dependent learning capability.

3 LEARNING-INDUCED REDUCTION IN POSTBURST AHP IS MEDIATED BY REDUCTION IN THE CONDUCTANCE OF THE CALCIUM-DEPENDENT POTASSIUM CURRENT The potassium currents underlying the postburst AHP have been studied most extensively in hippocampal neurons. They have been characterized based on their latency from the action potentials, their duration, and their pharmacological properties. Five such currents are now identified: the voltage-dependent muscarin-sensitive IM, the calcium- and voltage-dependent IC, the apamin-sensitive calcium-dependent IAHP, the hyperpolarization-activated cation-current Ih, and the apamin-insensitive calcium-dependent sIAHP (Gasparini and DiFrancesco, 1999; Sah, 1996; Stocker et al., 1999; Storm, 1989). Several studies indicate that the learning-induced reduction in neuronal adaptation and in AHP amplitude results from reduction in one or more Ca2þ-dependent potassium currents (Power et al., 2002; Saar et al., 2001; Sanchez-Andres and Alkon, 1991). Three such currents could have been potentially affected: the IC which contributes to the fast AHP, the apamin-sensitive IAHP which is thought to underlie the medium AHP, and the apamin-insensitive sIAHP which is thought to underlie the slow AHP (Gasparini and DiFrancesco, 1999; Sah, 1996; Stocker et al., 1999; Storm,

4 Role of Second Messenger Systems

1989). The IC is not modified after learning (Moyer et al., 1996; Saar et al., 2001; Sanchez-Andres and Alkon, 1991). Thus, the IAHP and the sIAHP remain as the two potassium currents most likely to be affected by learning. The IAHP is mediated by small conductance Ca2þ-activated Kþ channels (SK) (for review, see Sah and Faber, 2002). Three SK channel genes (SK1, SK2, and SK3) are expressed in the brain (Sailer et al., 2002). The apamin-sensitive portion of the postburst AHP is thought to be mediated by the SK2 and the SK3 channels (Sah and Faber, 2002; Sailer et al., 2002; Stocker et al., 1999), while SK1 channels have a significantly lower sensitivity to apamin. The identity of the channel that mediates the sIAHP is yet unknown (Sah and Faber, 2002). In piriform cortex neurons, the postburst AHP has a considerably shorter duration than in hippocampal neurons, and the two types of AHP may overlap (Saar et al., 2001). When the medium and slow AHPs are clearly distinguishable, a specific IAHP blocker (apamin) affects only the first postburst negative peak (Fig. 2D1). More frequently, the medium and the slow AHPs do not have separate peaks. In these occasions, apamin reduces the amplitude of the single peak (Fig. 2D2). Apamin significantly reduces the AHP in neurons from all groups, and thus the difference in AHP amplitude between neurons from trained rats and controls remains (Fig. 2E). Moreover, the proportion of the AHP amplitude reduced by apamin is greater in neurons from trained rats. The contribution of the apamin-sensitive portion of the AHP to the AHP amplitude may be calculated by subtracting for each cell the amplitude of the AHP in the presence of apamin from the averaged amplitude of AHP in its group, recorded without the drug. The averaged value of the apaminsensitive part of the AHP is similar in all groups (Brosh et al., 2006), indicating that this part remains intact after olfactory learning. These data suggest that learninginduced enhancement in neuronal excitability is mediated by long-term reduction of the sIAHP.

4 ROLE OF SECOND MESSENGER SYSTEMS IN MAINTAINING PROLONGED AHP REDUCTION How are such modifications maintained for periods of days after training completion? The sIAHP is reduced by PKC-dependent activation of glutamate-mediated kainate receptors (Melyan et al., 2002, 2004). Accordingly, olfactory learning-induced postburst AHP reduction is mediated by persistent PKC activation (Seroussi et al., 2002). The mechanism by which such long-lasting PKC-dependent AHP reduction persists was unknown until recently. A wide range of studies testify to the importance for extracellular signalregulated kinase (ERK) in memory formation across many species and brain areas (reviewed in Adams and Sweatt, 2002). In a time frame of minutes and up to few hours, ERK activation increases transiently after acquisition of fear conditioning (Atkins et al., 1998; Schafe et al., 2000), taste learning (Berman et al., 1998), and

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water maze training (Blum et al., 1999). ERK is crucially involved in both short- and long-term modulation of synaptic transmission (Atkins et al., 1998; English and Sweatt, 1997; Rosenblum et al., 2000, 2002; Thomas and Huganir, 2004). Such short-term ERK-dependent modifications may be due to enhancement of intrinsic neuronal excitability by modulating the IA potassium current (Sheng et al., 1992; Yuan et al., 2002). The MEK inhibitor, PD98059, significantly increases the AHP amplitude in neurons from trained rats only (Cohen-Matsliah et al., 2007). In neurons from naı¨ve and pseudo-trained rats, the averaged AHP value was not affected by these agents. Two typical examples for the different effect of PD98059 on neurons from a pseudotrained rat and a trained rat are shown in Fig. 3A and B. Following PD98059 application, the difference in AHP amplitude between neurons from trained rats and controls was abolished (Fig. 3C). Accordingly, MEK inhibitors reduce neuronal excitability in neurons from trained rats only. In control conditions, the averaged number of action potentials evoked in neurons from trained rats is higher compared with neurons from control rats, as previously described (Saar et al., 2001). Application of PD98059 reduced significantly the averaged number of spikes in neurons from trained rats but had no effect on the averaged value in neurons from pseudo-trained rats (Fig. 3D and E). Recordings performed in piriform cortex neurons after olfactory learning show that the PKC blocker, GF-109203X, increases the AHP in neurons from trained rats only and that the PKC activator, OAG, reduces the AHP in neurons from control groups more than in neurons from trained rats (Seroussi et al., 2002). In particular, the PKC activator reduces the AHP in neurons from naı¨ve rats by 40% (Seroussi et al., 2002). In contrast, in the presence of the MEK inhibitor, the PKC activator does not affect the AHP amplitude in neurons from naı¨ve rats, indicating that PKC is upstream to MEK in the sequence of activation leading to postburst AHP reduction.

5 LEARNING-INDUCED MODULATION OF NORADRENALINE’S EFFECT ON NEURONAL EXCITABILITY Noradrebaline (NE) has several actions in target structures that would suggest a role in learning and memory processes, particularly in odor discrimination tasks (Gervais and Pager, 1983; Linster and Hasselmo, 2001; Przybyslawski et al., 1999; Sullivan and Wilson, 1991, 1993, 1994). NE was shown to enhance neuronal excitability (Foehring et al., 1989; Madison and Nicoll, 1984; Stocker et al., 1999). This action is mediated by reducing the sIAHP conductance, while the IAHP is enhanced (Stocker et al., 1999). That IAHP/sIAHP conductances ratio is increased after learning may result with reversal of the effect of NE on neuronal excitability. Consequently, NE may play different roles prior to and after learning.

5 Learning-Induced Modulation of Noradrenaline’s Effect

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FIGURE 3 MEK inhibitor enhances neuronal excitability in neurons from trained rats only. While PD98059 application had no effect on a neuron from a pseudo-trained rat (A), it enhanced the AHP in a neuron from a trained rat (B). (C) Averaged AHP amplitude in neurons from the three groups recorded in control solution, under PD98059, and under UO126. Averaged AHP amplitude in trained rats is significantly smaller compared to naive and pseudo-trained rats (**p < 0.01). This difference is abolished after PD98059 or UO126 application. (D) Examples for the effect of PD98059 on repetitive spike firing in a neuron from a pseudo-trained rat and a neuron from a trained rat. With stimulus intensity of Ith  2, a 1 s depolarizing pulse generates repetitive firing throughout the stimulation period. While PD98059 has no effect on the pseudo-trained neuron, repetitive firing of the trained neuron is significantly suppressed by the same agent. (E) While PD98059 application has no effect on the number of spikes evoked in neurons from pseudo-trained rats, it reduces significantly the averaged number of spikes in neurons from trained rats (**p < 0.001). Consequently, the difference in spike numbers between neurons from the pseudo-trained and trained groups disappears in the presence of the ERK inhibitor. Figure modified from Cohen-Matsliah et al. (2008).

Indeed, NE has different effects on neurons from trained rats and controls (Brosh et al., 2006). NE (10 mM) application to neurons from control rats reduces significantly the amplitude of the postburst AHP. In contrast, it increases the averaged AHP in neurons from trained rats. As a result of these opposing effects, NE abolished the difference in the AHP amplitudes between neurons from trained and control rats (Fig. 4A–C).

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FIGURE 4 NE enhances the AHP amplitude in neurons from trained rats only. (A) Averaged postburst AHP in control conditions and in NE are shown in two neurons from pseudo-trained rats. In these neurons, NE reduces the AHP, as previously reported. (B) In neurons from trained rats, the effect of NE on the AHP is reversed; NE significantly increases the averaged postburst AHP amplitude. (C) The difference in AHP between the different groups is abolished by application of NE. Figure modified from Brosh et al. (2006).

What functional significance may such a modification in NE effect on neuronal excitability have? While it is commonly accepted that synaptic strengthening is involved in learning and memory processes, it was pointed out by several studies that a compensation mechanism that would prevent overstrengthening of synaptic connections which are not relevant to memory storage should also be activated (Barkai et al, 1994; Quinlan et al., 2004). Such a mechanism would prevent runaway synaptic enhancement, thus preventing a situation in which the neuronal network becomes hyperexcitable, loses its ability to store memories, and responds with an epilepticlike activity to relatively mild stimulations (Barkai et al., 1994; Hasselmo and Barkai, 1995). Thus, after olfactory learning, NE may act to counterbalance these modifications and preserve the piriform cortex ability to subserve olfactory learning by increasing the threshold for inducing neuronal activation to the point where synaptic changes are induced.

6 LEARNING-INDUCED MODULATION OF SYNAPTIC TRANSMISSION Olfactory learning induces profound changes also in excitatory, AMPA receptorsmediated, and inhibitory, GABAA receptor-mediated, synaptic transmission. Such physiological modifications are apparent at least 4 days after learning completion. For both excitatory and inhibitory transmissions, an increase in miniature synaptic events is evident in most of the recorded neurons. Moreover, about a quarter of the neurons showed an exceptionally great increase in the amplitude of miniature events.

7 Enhancement of AMPAR-Mediated Synaptic Currents

7 ENHANCEMENT OF AMPAR-MEDIATED SYNAPTIC CURRENTS Modulation of postsynaptic AMPA receptors (AMPARs) has been long suggested to have a key role in the early cellular events leading to learning and memory formation (Kessels and Malinow, 2009; Malinow and Malenka, 2002). Enhanced AMPAR expression was shown in lateral amygdala neurons after fear conditioning (Rumpel et al., 2005) and cue-reward learning (Tye et al., 2008), and in hippocampal neurons after inhibitory avoidance training (Whitlock et al., 2006). OD learning induces a dramatic increase in the amplitude of excitatory miniature excitatory synaptic currents (mEPSCs) (Fig. 5A). The averaged mEPSC amplitude in neurons from trained rats is significantly higher compared to the averaged value in neurons from naı¨ve and pseudo-trained rats, which do not differ from each other. Similar differences are observed when comparing the median of amplitudes in the three groups (Fig. 5B). The increase in mEPSCs’ averaged amplitude is apparent throughout the sampled neuronal population (Fig. 5C). A growing body of evidences demonstrates similar large overall increase in synaptic strength (>50%) in different brain structures following various training paradigms (McKernan and ShinnickGallagher, 1997; Sacchetti et al., 2001, 2004; Tye et al., 2008; Yin et al., 2009). B 10 pA 100 ms

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FIGURE 5 Learning-induced enhancement of the AMPAR-mediated synaptic currents. (A) Miniature synaptic events recorded in a neuron from a trained rat at holding potential of 80 mV. An example of a minimal-amplitude current appears in the second from top trace. Note that most events are larger than the minimal-amplitude event. (B) The averaged median amplitude of miniature events in neurons from trained rats is markedly higher than in neurons from the two control groups, which do not differ from each other. The median was determined for each neuron from all spontaneous events. Values (mean  SE) represent the average of all cells in each group (**p < 0.01). (C) Cumulative frequency distributions of all medians in the three rat groups. Each point represents the median in one neuron. Note that while the median EPSC amplitude appears to increase in most neurons in the trained group, a subpopulation show particularly strong enhancement. Figure modified from Saar et al. (2012).

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Notably, in about a quarter of the neurons recorded from trained rats, the majority of synaptic events has amplitudes higher than the average in controls, indicating that in these particular cells most recorded synapses are strengthened after learning. The increase in the averaged amplitude of sEPSC is not accompanied by modification in the frequency of spontaneous events (Saar et al., 2012) Such dramatic increase in the averaged amplitude of sEPSC, without any apparent modification in the frequency of these spontaneous events, suggests that OD learning is accompanied by long-lasting modulation of postsynaptic AMPAR-mediated currents, rather than by increase in the probability of presynaptic release.

8 ENHANCEMENT OF GABAA-MEDIATED UNITARY SYNAPTIC CURRENTS OD learning induces dramatic increase also in the averaged amplitude of inhibitory miniature events (Fig. 6A). The averaged miniature inhibitory post synaptic currents (mIPSC) amplitude in neurons from trained rats is significantly higher compared to the averaged value in neurons from pseudo-trained and naı¨ve rats. Similar differences are observed when comparing the averaged median of amplitudes in the three groups (Fig. 6B). Similar to miniature excitatory synaptic events, the increase in A

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FIGURE 6 Learning-induced enhancement of the GABAA-mediated synaptic currents. (A) Miniature inhibitory synaptic events recorded in a neuron from a trained rat at holding potential of 60 mV. Spontaneous activity was totally abolished in the presence of the GBABA blocker, BMI (20 mM). (B) Averaged median of miniature events in neurons from trained rats is significantly higher than in neurons from the pseudo-trained groups. The median was determined for each neuron from all spontaneous events. Values (mean  SE) represent the average of all cells in each group (*p < 0.05). (C) Cumulative frequency distributions of event medians of the three groups. Each point represents the median in one neuron. Note that while the averaged sIPSC median appears to increase in most neurons in the trained group, a subpopulation of 6 neurons out of the 20 recorded show particularly strong enhancement. Figure modified from Saar et al. (2012).

8 Enhancement of GABAA-Mediated Unitary Synaptic Currents

mIPSCs’ averaged amplitude are apparent throughout the neuronal cell population (Fig. 6C). As observed for synaptic excitation, the widespread, learning-induced enhancement of synaptic inhibition results with a significant increase in the appearance of high-amplitude miniature synaptic events in the trained neuronal population. Here too, in about 25% of neurons recorded from trained rats, the majority of synaptic events had amplitudes higher than the average in control, indicating that in these cells most recorded synapses are strengthened after learning. The notable (30%) increase in the averaged amplitude of mIPSC was not accompanied by modification in the frequency of spontaneous events. The averaged frequency of spontaneous events was similar in all groups, suggesting that OD learning is accompanied by long-lasting modulation of postsynaptic GABAA-mediated currents and not by increase in the probability of presynaptic GABA release. Rule learning results with an additional long-lasting enhancement of inhibitory synaptic transmission onto proximal dendrites of these pyramidal neurons. Such enhancement is mediated by a strong hyperpolarizing shift in the reversal potential of fast inhibitory postsynaptic potentials (Brosh and Barkai, 2009). The averaged reversal potential of GABAA-mediated fast IPSPs is significantly lower after learning. Moreover, such learning-induced reduction is apparent throughout the recorded neuronal population, as shown in the cumulative frequency graph (Fig. 7).

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FIGURE 7 Learning-induced hyperpolarization of the fast IPSP’s reversal. (A) Typical recording at different membrane potentials in a neuron from a pseudo-trained rat (left) and a neuron from a trained rat (right). The reversal potential of the IPSP in the control neuron is at 69 mV and in the trained neuron at 75 mV. Numbers on left of traces note the holding membrane potential. (B) Averaged values of the IPSP’s reversal potential in the three experimental groups (N ¼ naı¨ve, T ¼ trained, P ¼ pseudo-trained). This value is significantly lower for the neurons taken from trained rats (**p < 0.01). Values represent mean  SE. (C) A cumulative frequency graph comparing the reversal potentials of the fast IPSP in neurons from controls versus trained rats. Each point represents the reversal potential in one neuron. Notably, the curve for the trained group is shifted smoothly leftward relative to the controls. Modified from Brosh and Barkai (2009).

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9 OLFACTORY RULE LEARNING-INDUCED CELLULAR MODIFICATIONS IN OTHER BRAIN AREAS Since successful performance in the OD task requires integration of information about the identity and about the reward value of odors, it is likely that additional brain areas, of lower as well as higher order than the olfactory cortex, would also be involved in rule learning acquisition and maintenance. Indeed, long-term changes in intrinsic neuronal properties and synaptic strength are found in several other key areas.

10 RULE LEARNING-INDUCED MODULATION OF INTRINSIC NEURONAL EXCITABILITY IN THE HIPPOCAMPUS AND AMYGDALA In the hippocampus, enhanced neuronal excitability is apparent in CA1 pyramidal neurons. Such enhancement occurs after beginning of training in the complex OD task, but prior to rule learning (Zelcer et al., 2006). Here too, the enhancement id is transient; it appears after the fifth day of training and lasts for 1 day after rule learning. It is apparent throughout the cell population and results from reduction in the medium and slow AHPs that control spike frequency adaptation. Interestingly, such olfactory learning-induced increased excitability in hippocampal neurons enhances the rats’ learning capability in another hippocampus-dependent task, the Morris water maze. Once OD rule learning is acquired, its maintenance is not dependent on the reduced postburst AHP in hippocampal neurons. However, the enhanced spatial learning capability of olfactory-trained rats in the water maze is diminished once the postburst AHP in CA1 pyramidal cells resumes its initial value. These data suggest that enhanced excitability of CA1 neurons may serve as a mechanism for generalized enhancement of hippocampus-dependent learning capability. In the presence of such enhanced neuronal excitability, the hippocampal network enters into a “learning mode” in which a variety of hippocampus-dependent skills are acquired rapidly and efficiently. In the basolateral amygdala (BLA), intrinsic neuronal excitability in pyramidal neurons is differentially modified by positive and negative olfactory learning (Motanis et al., 2014). As in the piriform cortex and the hippocampus, learning of the complex OD task, in which success is rewarded with drinking water, results with enhanced intrinsic excitability. Such enhancement is mediated by reduction in the slow potassium current, as shown in other brain areas. In contrast, olfactory fear conditioning, in which the animal learns to associate the odor with an electric shock, results in decreased intrinsic excitability, mediated by activation of the m-opioid-sensitive potassium current. Thus, these data suggest that positive and negative changes in BLA excitability contribute to the encoding of opposite odor-value behaviors.

12 Rule Learning-Induced Modulation

11 RULE LEARNING-INDUCED MODULATION IN SYNAPTIC CONNECTIVITY BETWEEN THE OFC AND THE PIRIFORM CORTEX The OFC is a main source of descending input to the PC and is thought to signal the desirability of the expected outcome (Schoenbaum and Roesch, 2005). After learning, OFC neurons encode neutral stimuli that have been associated with motivationally significant stimuli (Roesch and Olson, 2004; Rolls, 1996; Schoenbaum et al., 1999; Tremblay and Schultz, 1999, 2000). The OFC has strong connections with the anterior PC (Johnson et al., 2000). Thus, it is a likely candidate to have a central role in the OD task, where choosing the correct odor is rewarded with drinking water. In particular, the OFC was suggested to use information regarding motivational significance of olfactory cues, and to selection and execution of behavioral strategy that would lead to reward acceptance (Schoenbaum et al., 1999). Accordingly, firing activity in the OFC during OD training appears to reflect the value of predicted outcomes, events, or consequence; OFC neurons are affected more by the odor valance than by its identity (Schoenbaum and Eichenbaum, 1995). During odor sampling, the vast majority of cue-selective cells in the OFC neurons developed selective response to the odor cues only after accurate choice performance (Schoenbaum et al., 1999). Damage to the OFC impairs the acquisition and retention of odor discriminations (Eichenbaum et al., 1980, 1983), further strengthening the notion of its central role in OD learning. Stimulating the descending pathway (OFC–APC) results in a typical single-peak fPSP with averaged delay of 7–9 ms, suggesting that the connection is mono synaptic (Fig. 8).The descending synaptic connections (the OFC–APC pathway) are strengthened after OD learning; input–output functions for stimulus intensities versus fPSP amplitudes for a range of increasing intensities of stimulation show the amplitude of the field potential increased linearly with the increase in stimulus intensity in the three groups (naı¨ve, pseudo-trained, and trained). However, the averaged amplitudes in the trained groups were higher than in the two control groups (Fig. 8C). Thus, synaptic connectivity between the OFC and the APC is enhanced after learning. Notably, paired pulse facilitation values evoked in this pathway do not differ between trained rats and controls, indicating that learning-induced enhancement in the fPSP amplitude is maintained via a postsynaptic modification.

12 RULE LEARNING-INDUCED MODULATION IN SYNAPTIC CONNECTIVITY BETWEEN THE PIRIFORM CORTEX AND THE OLFACTORY BULB The main ascending input to the PC, originating in the OB, may also be modified after learning (Litaudon et al., 1997; Roman et al., 1987; Sevelinges et al., 2004; Wilson, 2003). Such long-lasting enhancement may further increase the excitability

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FIGURE 8 Synaptic connectivity in the ascending and descending pathways to the piriform cortex is enhanced after learning. (A, B) Typical responses recorded in layer Ib (bottom traces in B) in response to stimuli applied in the descending (left) and the ascending (right) pathways. For both pathways, a first fast response (presumably the afferent volley in the activated presynaptic axons) is followed by later synaptic responses. One such late response is evoked in the descending pathway. In the ascending pathway, the first afferent response is followed by a second response which originates from the activation of the association connections between layer II pyramidal neurons. (C) Analysis of the Input–output function in OFC–APC pathway. Learning-induced enhanced synaptic responses are evident throughout the range of stimulus intensities. Values represent mean  SE. #, significant difference from pseudo-trained groups (p < 0.05); *, significant difference from the naı¨ve group (p < 0.05). The pseudo-trained and naı¨ve groups did not differ significantly at any stimulus intensity. (D) Analysis of the Input– output function of the first synaptic response in the OB–APC pathway. The averaged amplitudes of the first synaptic response, representing activation of the afferent fibers, are significantly higher after learning, compared with the naı¨ve and pseudo-trained groups, throughout the range of stimulus intensities. Also, the averaged amplitudes of the pseudotrained group are significantly higher than the values of the naı¨ve group. (E) Analysis of the input–output function of the second synaptic response in the OB–APC pathway. The averaged amplitudes of the second synaptic response, representing activation of the intrinsic intracortical fibers, are significantly higher after learning, compared with the naı¨ve and pseudo-trained groups. The two control groups did not differ between them. #, significant difference from pseudo-trained groups (p < 0.05); *, significant difference from the naı¨ve group (p < 0.05). The pseudo-trained and naı¨ve groups did not differ significantly at any of the stimulus intensities. n notes number of rats, values represent mean  SE. Modified from Cohen et al. (2008).

References

of the PC network, thus adding weight to the role of the OFC in controlling and directing the activity of PC. Ascending (from low to high brain region) pathway (OB–APC), stimulation induced fPSP in the APC has of two synaptic components (Fig. 8). These two components were previously identified as an early monosynaptic response evoked by afferent fibers coming via the lateral olfactory tract to layer Ia, and a late disynaptic response at layer Ib, evoked by reactivation of layer II pyramidal neurons (Ketchum and Haberly, 1993). Accordingly, the averaged delay of the first synaptic response is 6–8 ms and the second synaptic response was 14–18 ms. We next examined whether long-lasting synaptic enhancement exists also in ascending pathway (OB–APC). Input–output functions for stimulus intensity versus fPSP amplitudes were recorded in response to increasing intensities of stimulation. The early synaptic response evoked by the afferent connections onto the pyramidal neurons is significantly enhanced after learning. Such enhancement is apparent throughout the range of stimulus intensities. Notably, a significant difference is also found between the pseudo-trained and naive groups (Fig. 8D). The second synaptic response, evoked by local axon interconnecting pyramidal neurons (the intrinsic fibers) within the APC, is also strongly enhanced after learning, throughout the range of stimulus intensities (Fig. 8E). In contrast to responses evoked by the afferent connections, here the pseudo-trained versus naive groups’ responses are similar. These data suggest that enhanced connectivity between the APC and its input sources is required for OD rule learning. In particular, after learning, OFC input could potentially initiate activity in APC in the absence of any odor stimulation, allowing for recall of odors and odor-related associations. The overall strengthening of the descending pathway suggests that the specificity of the evoked odor memory is achieved not by these inputs, but by specific synaptic connections that were strengthened within the piriform cortex network, during the learning process.

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Neural mechanisms of odor rule learning.

Rats that are trained in a particularly difficult olfactory discrimination task demonstrate a dramatic increase in their capability to acquire memorie...
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