Synaptic adaptation and odor-background ... - Semantic Scholar

Neurobiology of Learning and Memory 87 (2007) 352–360 www.elsevier.com/locate/ynlme

Synaptic adaptation and odor-background segmentation Christiane Linster a

a,*

, Lauren Henry b, Mikiko Kadohisa b, Donald A. Wilson

b

Department of Neurobiology and Behavior, Cornell University, Ithaca, NY14853, USA b Department of Zoology, University of Oklahoma, Norman, OK 73019, USA Received 26 July 2006; revised 22 September 2006; accepted 27 September 2006 Available online 1 December 2006

Abstract Habituation is a form of non-associative memory that plays an important role in filtering stable or redundant inputs. The present study examines the contribution of habituation and cortical adaptation to odor-background segmentation. Segmentation of target odorants from background odorants is a fundamental computational requirement for the olfactory system. Recent electrophysiological data have shown that odor specific adaptation in piriform cortex neurons, mediated at least partially by synaptic adaptation between the olfactory bulb outputs and piriform cortex pyramidal cells, may provide an ideal mechanism for odor-background segmentation. This rapid synaptic adaptation acts as a filter to enhance cortical responsiveness to changing stimuli, while reducing responsiveness to static, potentially background stimuli. Using previously developed computational models of the olfactory system, we here show how synaptic adaptation at the olfactory bulb input to the piriform cortex, as demonstrated electrophysiologically, creates odor specific adaptation. We show how this known feature of olfactory cortical processing can contribute to adaptation to a background odor and to odor-background segmentation. We then show in a behavioral experiment that the odor-background segmentation is perceptually important and functions at the same time-scale as the synaptic adaptation observed between the olfactory bulb and cortex. 2006 Elsevier Inc. All rights reserved. Keywords: Olfactory; Learning; Synaptic plasticity; Computational modeling

1. Introduction Habituation is an important form of non-associative memory serving to reduce responsiveness to stable, repetitive or non-significant stimuli. Habituation thus allows greater attention and/or processing resources to be devoted to dynamic or biologically more significant inputs. In the olfactory system, recent evidence suggests that odor habituation and sensory cortical adaptation may also play a role in figure-ground segmentation (Kadohisa & Wilson, 2006). Figure-ground segmentation is an issue faced by all sensory systems. In the natural world, target stimuli (signals originating with food, predators, potential mates, etc.) usually appear against a background of other, generally unrelated stimuli. Sensory systems must parse this montage into

*

Corresponding author. Fax: +1 607 254 4308. E-mail address: [email protected] (C. Linster).

1074-7427/$ - see front matter 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.nlm.2006.09.011

individual objects (often themselves composed of many features) with specific spatial and temporal relationships, allowing recognition of a specific object stimulus (i.e., figure) in the midst of background clutter. Stimulus characteristics such as spatial location, coherent or apparent movement, and temporal contiguity facilitate this separation in the visual and auditory systems. In olfaction, identification of stimuli from background is equally critical, yet olfactory stimuli lack the spatial dimension so important for background segmentation in other systems. Most natural odors are complex mixtures, yet on any given inhalation, the vertebrate olfactory system must be able to identify those components that belong to a target odor as distinct from those that are present in the background. Individual odorant features activate specific olfactory receptor neurons, which in turn create odor-specific spatial and temporal patterns of second-order neuron (mitral/tufted cell) activity within the olfactory bulb. Complex mixtures evoke olfactory bulb spatial

C. Linster et al. / Neurobiology of Learning and Memory 87 (2007) 352–360

activation patterns comparable to the sum of their individual components (Lin, Shea, & Katz, 2006; Tabor, Yaksi, Weislogel, & Friedrich, 2004) Thus, the output of the olfactory bulb largely reflects the odor me´lange present in the nasal cavity. In this situation, how can one subset (i.e. target odor) of this output activity drive behavior while the remaining subset (i.e. background) be ignored/filtered? Recent work in the piriform cortex provides a potential mechanism for odor-background segmentation—rapid cortical adaptation to stable inputs. Single neurons in the anterior piriform cortex rapidly, and selectively adapt to odors despite relatively maintained input from olfactory bulb mitral/tufted cells (Wilson, 1998a). This rapid cortical adaptation is associated with homosynaptic depression of mitral/tufted cell input to cortical pyramidal cells (Best & Wilson, 2004; Wilson, 1998b). Blockade of pre-synaptic group III metabotropic glutamate receptors on mitral cell axons prevents the synaptic depression and cortical odor adaptation (Best, Thompson, Fletcher, & Wilson, 2005), as well as habituation of simple odor evoked behaviors (Best et al., 2005; Yadon & Wilson, 2005). Furthermore, single-units in piriform cortex appear to respond to target odors presented against a stable background as if the target odor alone was present (Kadohisa & Wilson, 2006). Together, these results suggest that cortical afferent synaptic depression may be sufficient to account for odor-background segmentation of dissimilar odors. The present work utilized a computational model of the olfactory system to further test the role of cortical afferent synaptic depression for odor-background segmentation. In addition, we determined whether odor-background segmentation could be utilized by rats in an odor discrimination task. Our results show that cortical afferent synaptic depression can be sufficient to account for the behavioral observation of odor-background segmentation.

2. Methods 2.1. Computational modeling We used a computational model of olfactory sensory neurons, olfactory bulb and piriform cortex. The individual elements of this model have been described in detail before (Linster & Cleland, 2002, 2004; Linster & Hasselmo, 2001; Linster, Maloney, Patil, & Hasselmo, 2003) and have been interconnected for present purposes (Fig. 1A). Olfactory sensory neurons and olfactory bulb mitral cells exhibited odor responses similar to those described experimentally (Fig. 1B) and the olfactory bulb model also exhibited weak odor-evoked oscillations in the gamma-frequency range. Novel parameters for the simulations presented here were the distribution of projections between olfactory bulb output neurons and piriform cortex pyramidal cells as well as the synaptic adaptation that has been described between these two structures (Wilson, 1998a, 2003, 2001). These two parameters were adjusted to yield pyramidal cell odor responses and adaptation properties representative of those described experimentally. Briefly, in a model simulating 50 mitral and 50 pyramidal cells, a uniform probabilistic projection pattern in which each mitral cell had a 0.06 probability to connect to any pyramidal cell in the model yielded pyramidal cell odor responses similar to those described experimentally (Fig. 2A). Activity dependent synaptic adaptation between mitral and pyramidal cells was

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adjusted in such a manner that pyramidal cell odor responses decreased to approximately 20% of their original value after synaptic adaptation (Fig. 2B). Both olfactory bulb and piriform cortex models are simplified representations of known olfactory circuitry constructed pursuant to the goal of determining the functional role of activity-dependent short term synaptic depression of the synapses between olfactory bulb mitral cells and piriform cortex pyramidal cells. Hence, these models omit a number of established anatomical and pharmacological details which would not materially affect the outcome of the present simulations. The model includes five categories of neurons: olfactory sensory neurons (OSNs), mitral cells, periglomerular cells, granule cells, pyramidal cells and two types of PC local interneurons connected as depicted in Fig. 1A and detailed in the legend and in Table 1. All neurons were represented as single compartments except for mitral cells, which were represented by two compartments (the primary dendritic arborization and the soma with secondary dendrites). Each compartment was characterized by a membrane time constant that can be regarded as the mean product of the membrane capacitance and the membrane input resistance. Consequently, the evolution of the membrane voltage over time is described by a first order differential equation: s

dvðtÞ þ vðtÞ ¼ I ext ðtÞ; dt

where s is the charging time constant of the neuron and Iext(t) is the total input at time t. The input from a particular presynaptic neuron at time t is computed as a function of the synaptic strength wij, the conductance change g(t) due to a presynaptic event xi(t0) at time t0, and the difference between the Nernst potential EN,ij of the associated channel type and the current membrane potential vj(t) of the postsynaptic neuron: X wij gðxi ðt0 ÞÞ½EN;ij vj ðtÞ: I j;ext ðtÞ ¼ i

The time course of g(t) is described by a double exponential function: s1 s2 ðeðtt0 Þ=s1 eðtt0 Þ=s2 Þ gðtÞ ¼ xi ðt0 Þgmax s1 s2 All neurons in the model produced discrete spikes of unit amplitude for output, computed according to the instantaneous spiking probability, a continuous, bounded function of the membrane potential with a threshold hmin and a saturation value hmax. When networks are built, all parameters are chosen randomly ±10% around the mean values indicated in Table 1 to ensure that the results are not based solely on a specific combination of parameters. All graphs that show average simulation results have been obtained with at least ten different network realizations with randomly chosen parameters. In the simulations presented here, based on experimental evidence showing activity-dependent short term depression of these synapses (Best & Wilson, 2004), we varied the impact of mitral cell outputs onto pyramidal cell dendrites in an activity-dependent manner. Synaptic strengths were first calculated from the parameters given in Table 1, and responses to simulated odorants were obtained. These responses correspond to the pre-exposure responses in electrophysiological experiments described elsewhere (Best & Wilson, 2004; Wilson, 1998a, 2003, 2001). To simulate synaptic depression due to prolonged exposure to an individual odorant, the network was stimulated with an odorant and an activity dependent plasticity rule was implemented to simulate synaptic depression due to prolonged exposure: wpost ¼ wpre ij ij a=

N X

xj ðtÞ

t¼1

where wij is the synaptic strength between the presynaptic mitral cell j and the postsynaptic pyramidal cell i, a is the rate of depression and xj(t) is the output of the presynaptic mitral cell. Short term synaptic depression thus depends only on presynaptic activity and is synapse-specific, as has been shown experimentally. Prolonged odor exposure is simulated by calculating the post-exposure synaptic weight during a 0.5 s odor exposure, with a set to 0.2. Note that longer exposure times, as used in the electrophysiology experiments, with lower rate of depression would lead to the same degree of depression.

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Fig. 1. Network architecture and neural odor response profiles. (A) Schematic illustration of model architecture. Each OSN in the model represents a class of OSNs expressing the same olfactory receptor and projecting to a common glomerulus. Within each glomerulus (glom), OSNs excite mitral cells (mi) and local interneurons called periglomerular cells (pg). Mitral and periglomerular cells reciprocally interact within each glomerulus. Mitral cells also reciprocally interact with a second class of local interneurons, granule cells (gr) in a widespread lateral fashion. Each mitral cell projects with equal probability p to all pyramidal cells (pyr) and feedforward local interneurons (ff) in the PC. Feedforward local interneurons inhibit a small number of surrounding pyramidal cells. Pyramidal cells make excitatory connections with a second class of local interneurons, feedback interneurons (fb) as well as with all other pyramidal cells in the model. Parameters are given in Table 1. (B) Examples of individual odor response profiles in the model. Odor response profiles to chemically similar odorants representing straight chain aliphatic odorants were simulated. Each graph shows the evolution in time of the membrane potential and action potentials of one simulated neuron in response to five different odorant stimuli representing aliphatic odorants with three, four, five, six and seven carbons. The time of odor stimulation is indicated by the dark line (Bi). OSNs have relatively broad odor response profiles and respond with increased firing to a range of odorants (Bii). Mitral cells are driven by OSNs and have more restricted odor response profiles because of local inhibitory processes (Biii). Pyramidal cells receive excitatory input from a distribution of mitral cells and also respond to a range of odorants. (C) Examples of individual response profiles of simulated OSNs, mitral and pyramidal cells. Each graph show that average activation (y-axis) in response to all simulated odorants (x-axis) for several individual neurons. Note that for ease of illustration only, odorants are arranged on a single axis in such a manner that all odorants activating any given OSN are located next to each other (Ci). Because OSNs excite mitral cells in a single glomerulus, the same ordering is reflected in the mitral cell response profiles (Cii). Mitral cells connect to pyramidal cells in a random, unordered fashion, consequently, pyramidal cell response profiles are not ordered accordingly in this representation (Ciii). (D) Average number of odorants OSNs, mitral and pyramidal cells respond to as a function of the connection probability between mitral and pyramidal cells. The average number of odorants each OSN and mitral cell responded to depended on parameters chosen to match available data (see Section 2). Each mitral cell could connect to each pyramidal cell in the model with an equal probability p. As this probability increases, the number of odorants each pyramidal cell responds to increases. p = 0.06 was chosen in these simulations to best match the available data on pyramidal cell odor responses.


Fig. 2. Effects of synaptic depression. (A) Example of odor responses in a modeled pyramidal cell before and after prolonged exposure to one odorant. The graph shows the evolution of the membrane potential and action potentials over time in response to simulated odor stimulation indicated by the black lines. This particular pyramidal cell responds to both odor A and B before exposure to B. The model was then exposed to odor B and synaptic depression was computed and applied. After exposure, this pyramidal cell did not respond to odor B but responded to odor A in a manner similar to before exposure. (B) Average effects of simulated exposure. The graph shows the average number of spikes (±SE) emitted by responsive pyramidal cells in response to stimulation with odors A and B before (pre) and after (post) exposure to odor B. Note that while the responses to odor B are significantly reduced (p < 0.001, t-test), those to odor A are not (p > 0.5, t-test). (C). Effect of synaptic depression on population response to odorants. The graph shows the percent change in overall response (summated number of spikes of all pyramidal cells in response to an odorant) to all simulated odorants due to prolonged exposure with one odorant (arrow). Simulated odorants each activate a subset of OSNs in the model. For simplicity, odorants were modeled as single ‘‘odotopes,’’ i.e., molecular features defined by their theoretical binding to a single group of similarly tuned olfactory receptors. Each model OSN had a normally distributed (r = 2) response profile centered on one of these odotopes. This representation, while artificial, leads to odor response profiles in OSNs resembling those described experimentally in response to aliphatic odorants with varying carbon chain lengths.

2.2. Behavioral testing Odor-background segmentation was tested behaviorally in Long Evans hooded rats bred in the University of Oklahoma colony, housed and handled according to National Institutes of Health guidelines and using protocols approved by the University of Oklahoma Institutional Animal Care and Use Committee. Rats were housed in polypropylene cages, with

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food available ad lib and light on a 12/12 light/dark cycle. During training and testing, rats were restricted to 30 min free access to water per day in addition to the water received during training (26 ll drops per trial). Body weight was monitored and remained stable through the testing. Rats (n = 4, 210–360 g) were trained in a Go, No-Go odor discrimination task for water reward. Odors were presented with a flow-dilution olfactometer (5 LPM continuous clean air to which 0.5 LPM odorized air was added) through the trial initiation/odor sampling port. Odorants were made fresh each day by adding drops to filter paper placed in glass vials over which the 0.5 LPM flow was directed. The Go odor (B) was eugenol (120 ll) and the No-Go odors were acetic acid (A: 9 ll), or a binary mixture (AB) of eugenol (40 ll) and acetic acid (3 lL). These odorants and concentrations were derived from human psychophysical data on mixture component analysis (Laing & Francis, 1989), These odorants and concentrations were derived from human psychophysical data on mixture component analysis (Laing & Francis, 1989); similarly to the procedures used in those experiments, the concentrations of the individual odorants were adjusted in such a manner that the components and their mixture evoked roughly similar stimulus intensities according to three human observers. A trial was initiated when the animal broke a photobeam in the odor sampling port and odor was presented. The odor was presented as long as the rat maintained its snout in the port, although this was generally less than 500 ms. The rat then had 3 s to approach the water port (on a Go trial) and break a photobeam to receive the reward (26 ll drops of water per trial). On No-Go trials, no water reward was delivered and animals could initiate a new trial in 1 s. There was no reward or punishment for entering the water port on a No-Go trial. Training sessions lasted 30 min/day and resulted in 75–150 self-generated trials each session. After the rats were trained to show a significant discrimination between the odors (>75% correct), background segmentation testing was performed. On testing days, the 30 min session was divided into several phases. First, normal training occurred for 10–15 min with randomized Go and No-Go trials. Then, a phase of AB only (unrewarded No-Go trials) occurred until the rat had initiated at least 5 trials. After a return to normal testing for 5–10 min, background exposure began with presentation of acetic acid continuously through the odor sampling port, and all test stimuli were the AB mixture (unrewarded No-Go trials). The background odor was maintained for approximately 6 min and responses to the binary mixture quantified during the initial (0–3 min) and later phases (3–6 min). Responses to the binary mixture were again assessed 24 h’s later after full recovery from adaptation to ensure that there was no long-term change in mixture quality to the rats. Rats were tested on multiple (2–4) days to increase the number of total testing trials in each phase (mean = 29 ± 7 trials in each unrewarded AB test phase) and average scores determined for each animal. Data presented are the probability of a Go response to eugenol (B) during randomized Go, No-Go trials, and Go responses to the mixture (AB) during the three AB testing phases (AB only, AB with A background initial and AB with A background late). Statistical comparisons between the probability of Go-responses across the different stimuli conditions (B only, AB only, AB with A background early and AB with A background late) were made with ANOVA and posthoc tests.

3. Results 3.1. Odor receptive fields and odor response habituation in pyramidal cells To simulate odor processing in piriform cortex, we used a computational model of the olfactory bulb and piriform cortex (see Section 2 and Fig. 1A for details). The details and parameter choices for the individual models have been described previously (Linster & Hasselmo, 2001, 2002, 2003, 2004); novel parameters here include the projection

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Table 1 Model parameters Olfactory sensory neurons (OSN) Periglomerular cells (PG) Mitral cells (Mi) Granule cells (Gr) Afferent, OSN to PG Afferent, OSN to Mi Intraglomerular inhibitory, PG to Mi Intraglomerular excitatory, Mi to PG Interglomerular inhibitory, PG to Mi (±5 neighboring glomeruli) Secondary dendrites, Mi to Gr (20 neighboring granule cells; wMi–Gr decays linearly with distance) Feedback inhibitory, Gr to Mi (10 neighboring mitral cells) Feedback inhibitory, Gr to Gr (5 neighboring granule cells) Mitral cell to pyramidal cell Pyramidal cell association fibers Feedforward inhibition Feedback inhibition

pattern between olfactory bulb mitral cells and piriform cortex pyramidal cells as well as short-term synaptic suppression in response to prolonged odor exposure. With odor response profiles of OSNs and mitral cells approximating those described experimentally (Fig. 1B and C; Araneda, Kini, & Firestein, 2000; Egana, Aylwin, & Maldonado, 2005; Fletcher & Wilson, 2003; Imamura, Mataga, & Mori, 1992; Malnic, Hirono, Sato, & Buck, 1999; Nagayama, Takahashi, Yoshihara, & Mori, 2004; Sato, Hirono, Tonoike, & Takebayashi, 1994; Wilson, 2000), the odor response profiles of pyramidal cells depended on the connectivity between mitral and pyramidal cells. In the absence of solid experimental data on details of these projection patterns, we used a uniform projection probability between mitral and pyramidal cells, in which each mitral cell had an equal probability p to contact each pyramidal cell. We adjusted the model in such a manner that response profiles of pyramidal cells were similar to those described experimentally in in vivo recordings (p = 0.05; Fig. 1Ciii and D; McCollum et al., 1991; Wilson, 2000, 2003, 2001). The effects of prolonged odor exposure were simulated by presenting a 500 ms odor stimulus with short-term synaptic depression active between mitral cells and pyramidal cells (see Section 2 for details). Note that a longer stimulation, as done experimentally, in conjunction with a reduced rate of depression a would result in the same degree of synaptic depression in these simulations. Synaptic depression is synapse specific; only those synapses for which the presynaptic mitral cell is responsive to a given odor undergo depression. In the model presented here, pyramidal cell odor responses to the specific odor used for simulated prolonged exposure are reduced (Fig. 2). Fig. 2A shows the spiking response of an individual cell to two odorants A and B before, and after exposure to odorant B. Note that the response to odorant B is drastically decreased post exposure, whereas the response to odor A is not affected, replicating what has been reported experimentally (Wilson,

s = 5.0 ms; hmin = 0.0; hmax = 1.0; s = 2.0 ms; hmin = 0.2; hmax = 2.0; s = 10.0 ms; hmin = 0.01; hmax = 8.0; s = 4.0 ms; hmin = 0.01; hmax = 4.0; wOSN–PG;= 0.003; EN,OSN–PG = +70; t1 = 1.0; t2 = 2.0; wOSN–Mi = 0.014; EN,OSN–Mi = +70; t1 = 1.0; t2 = 2.0; wPG–Mi = 0.0001; EN,PG–Mi = 5; t1 = 4.0; t2 = 8.0; wMi–PG = 0.0001; EN,Mi–PG = 70; t1 = 1.0; t2 = 2.0; wPG–Mi = 0.0015; EN,PG–Mi = 5; t1 = 4.0; t2 = 8.0; wMi–Gr = 0.006; EN,Mi–Gr = 70; t1 = 1.0; t2 = 2.0; wGr–Mi = 0.0015; EN,Gr–Mi = 5; t1 = 4.0; t2 = 8.0; wGr–Gr = 0.014; EN,Gr–Gr = 5; t1 = 4.0; t2 = 8.0; p = 0.1; wMi–Pyr = 0.028; EMi–Pyr = 70; t1 = 1.0; t2 = 2.0; p = 0.1; wPyr–Pyr = 0.0001; EPyr–Pyr = 70; t1 = 1.0; t2 = 2.0; wff–Pyr = 0.0001; Eff–Pyr = 5; t1 = 4.0; t2 = 8.0; wfb–Pyr = 0.0015; Efb–Pyr = 5; t1 = 4.0; t2 = 8.0; wPyr–fb = 0.0015; EPyr–fb = 70; t1 = 1.0; t2 = 2.0;

2003, 2001). Fig. 2B shows the general effect of prolonged exposure to one odorant on pyramidal cell odor responses. Pyramidal cell responses to two odorants, A and B, to which they strongly responded were first recorded (number of spikes in response to odor stimulation emitted pre-exposure). Subsequently, the model was exposed to odor A for 500 ms with synaptic depression turned on, and the responses of these same cells to odors A and B were recorded post-exposure (with synaptic plasticity turned off). The graph shows the average responses (±SE) of pyramidal cells responding strongly to these two odorants during 10 stimulations before (pre) and after (post) prolonged exposure to odor B. Each simulation was run with a newly constructed network using randomized parameters (see Section 2). These results show that synaptic depression alone, in absence of other phenomena, can reproduce the experimentally obtained results. In the simulations presented here, we were interested in the functional role of short term synaptic depression. To test whether synaptic depression in response to a single odorant (simulating the prolonged exposure to a background odorant) can be sufficient to allow for odor-background segmentation, we next looked at the population response, rather than individual neuron responses, of piriform cortex. Fig. 2C shows the effect of prolonged exposure to a single odorant on the population response—summated number of spikes in all pyramidal cells in the model in response to an odorant—to all simulated odorants. Briefly, all odorants were presented to the model for 120 ms each and the total number of spikes were recorded. The network was then exposed to one odorant (arrow) for 500 ms with synaptic plasticity turned on, and subsequently stimulated with all odorants for 120 ms each again. The graph shows the response to each odorant after exposure expressed as the percentage of the pre-exposure response. Note that the response to the exposed odor is significantly (p < 0.001; t-test) less than that to all other odorants. Also


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Fig. 3. Behavioral odor-background segmentation. (A) Schematic depiction of the behavioral apparatus. Both the discriminative stimuli and background stimuli were delivered via the odor sampling port. A nose poke into the sampling port initiated a trial. A nose poke into the water port was required to deliver water reward on Go trials. (B) Rats could discriminate eugenol (B) from a binary mixture of eugenol and acetic acid (AB). With acetic acid present in the background, this ability was reduced and after approximately 3 min of background exposure (late) rats treated the eugenol + acetic acid as eugenol alone. Points marked with an asterisk are significantly different from other points but not different from each other. Both mean (±SE) and individual rat data points are shown.

note that a decrease in response, albeit smaller than that to the exposed odorant, can be seen for several odorants. This is due to the fact that each mitral cell in the model has a given responsive field to odorants (see Fig. 1B and C) and that synaptic depression in response to a given odorant will consequently affect the responses to some other odorants. For illustration purposes only, odorants are ordered on a single axis in such a way that odorants activating a common subset of receptors are next to each other on this axis. These results show that prolonged exposure to one odorant, simulating a constantly present background odorant, results in a significantly decreased overall response of the piriform cortex network in response to that odorant. 3.2. Behavioral experiments show odor-background segmentation The mechanism modeled above could contribute to basic odor habituation, but may also allow odor-background segmentation. Using a simple Go, No-Go odor discrimination task, we next determined whether rats could solve an odor-background segmentation task. As shown

in Fig. 3, rats could discriminate eugenol from the binary mixture of eugenol and acetic acid. Rats had been shaped to retrieve a water reward in response to eugenol (B alone), and to withhold the response when the mixture eugenol/ acetic acid was presented (AB). Acetic acid was then added to the behavioral chamber as a background odorant. Initially, the presence of acetic acid in the background had no impact on responses to the eugenol/acetic acid mixture (AB with A background initial). However, within approximately 3 min, the rats began to respond to the mixture as if it was eugenol alone (AB with A background late). A one-way ANOVA using the probability of a Go response across the different testing conditions (A only, AB only, AB with A background early and AB with A background late) as main effect revealed a significant effect of odor on the probability of a Go response (F(3,15) = 33.36, p < 0.001). Post-hoc Fisher tests revealed that the probability of a Go response to eugenol alone or to the mixture against the acetic acid background during the late phase of exposure was significantly higher than to the mixture alone or to the mixture against background during the initial background exposure. The responses to eugenol alone

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and the mixture during late background exposure were not significantly different from each other. Rats thus, can segment an odor from a continuous background and treat that odor as if it was presented alone. 3.3. Synaptic adaptation alone can underlie behavioral observations In the behavioral experiments described above, rats were first trained to respond to an odor B (eugenol) and to withhold response to a combination of two odors A + B (eugenol/acetic acid). Subsequently, odor A was continuously present in the testing chamber. When rats were presented with the stimulus AB, they now responded as if presented with B alone. We then asked if the synaptic depression

described experimentally and implemented in our computational model can suffice to explain these behavioral observations. To test this, we presented the model with an odor B and recorded the average firing rates of individual pyramidal cells in response to this odorant, presented the model with odors A and B simultaneously and recorded the average firing rates of individual pyramidal cells. Each odorant evoked a unique, distributed pattern of activation across the pyramidal cell model network. Fig. 4Ai shows the pattern of activation evoked by odor B in these simulations. The pyramidal cell network is represented as a 5 · 10 two-dimensional network and levels of activation are color coded, with hot colors representing higher activation values. Fig. 4Aii shows the network’s response to the simultaneous presentation of A and B. We compared the response

Fig. 4. Simulation of behavioral experiments. (A) Activation patterns in response to odors B and the binary mixture AB. Each graph shows the activation (number of spikes during 120 ms odorant stimulation) of each pyramidal cell. Cells are represented in a 5 · 10 network and warm colors indicate high activation values. Each image is a graphical representation of the network response to one particular odorant. Ai and Aii show the response patterns to odors B and AB in a naı¨ve network. Aiii and Aiv show the response patterns to odors B and AB in the same network after prolonged exposure to A. (B) Effect of prolonged exposure on dissimilarity between odors B and AB. The graph shows the average dissimilarity (Euclidean distance, ±SE) between the patterns evoked by odor B and the binary mixture AB before (pre-exposure) and after (post-exposure) prolonged exposure to odor A. Note that the representation of the binary mixture AB is highly similar to that of B alone after prolonged exposure to A.


patterns to odors A and AB by calculating the normalized euclidean distance between the activation patterns, as has been done previously for olfactory bulb odor mapping and in modeling studies of piriform cortex (Cleland, Morse, Yue, & Linster, 2002; Hasselmo & Bower, 1993; Hasselmo, Wilson, Anderson, & Bower, 1990; Johnson, Farahbod, Xu, Saber, & Leon, 2004; Johnson & Leon, 2000; Linster & Hasselmo, 1997; Linster et al., 2003). In naı¨ve networks (i.e. those which have not undergone synaptic depression), the distance or dissimilarity between odor B and the binary mixture AB was substantial (0.61 · 0.04); this dissimilarity would explain the fact that rats can rapidly learn to respond differentially to the component and the binary mixture (Linster & Hasselmo, 2001). We then submitted the computational model to a prolonged exposure with odor A, simulating the background presence of odor A in the behavioral experiments. After exposure of the model to A, accompanied by synaptic depression, the model was exposed to odors B and AB again and the evoked activation patterns were compared by calculating the Euclidean distance as was done previously. Fig. 4Aiii and Aiv show the post-exposure responses to odors B and AB, respectively. One can easily see that the response evoked by the mixture AB resembles that evoked by B substantially more after exposure to A. Indeed, the average distance or dissimilarity between the patterns evoked by B and those evoked by AB decreased to 0.19 ± 0.01 after the network had been exposed to A (Fig. 4B). The overall piriform cortex response to AB now resembles that of odor B, explaining why rats respond to the presentation of AB with the acquired response to B. In other words, after prolonged presentation of A accompanied by synapse-specific synaptic depression, rats are no longer able to distinguish odors B and AB. 4. Discussion The present results demonstrate that cortical afferent synaptic depression could account for an odor selective adaptation which could directly contribute to odor-background segmentation. As shown here, odor-background segmentation can be expressed by rats in a Go, No-Go task. Together with previous work (Kadohisa & Wilson, 2006), these findings suggest that odor-background segmentation may rely on the temporal structure of odor scenes, wherein early onset, stable stimuli become filtered, leaving responsiveness to, and discriminability of novel, later onset odors intact. This temporal parsing occurs relatively quickly, and directly allows identification of odors against background despite the fact that molecular components of both target and background odors are present within single inhalations. The simulation results suggest that synapse—specific short term suppression is sufficient to account for behavioral odor-background segmentation. The model shows that when projections between olfactory bulb and piriform cortex are arranged in such a manner as

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to statistically reproduce piriform cortex response profiles, prolonged exposure to one odorant selectively decreases cortical responsiveness to that odorant while leaving responses to other odorants intact. The model also shows that if one component of a binary mixture is presented for an extended amount of time, the binary mixture subsequently resembles the second component, leading to a decrease in discriminability between B and the mixture AB. These results suggest that the metabotropic glutamate receptor mediated homosynaptic depression of mitral cell input to the anterior piriform cortex may not only contribute to odor habituation (Best et al., 2005; Yadon & Wilson, 2005), but also allows a rapid filtering of stable odor input that contributes to odor-background segmentation. This short-term synaptic plasticity operates over the time course of seconds to minutes (Best & Wilson, 2004), creating an on-line adjustment in the strength of afferent synapses and resulting in a piriform cortex maximally responsive to dynamic stimuli. The present simulations show that short-term afferent synaptic depression alone could underlie behaviorally observed odor-background segmentation. Acknowledgment This project was funded by NSF award IOB 0388981 to C. Linster and D. Wilson. References Araneda, R. C., Kini, A. D., & Firestein, S. (2000). The molecular receptive range of an odorant receptor. Nat. Neurosci., 3, 1248–1255. Best, A. R., Thompson, J. V., Fletcher, M. L., & Wilson, D. A. (2005). Cortical metabotropic glutamate receptors contribute to habituation of a simple odor-evoked behavior. J. Neurosci., 25, 2513–2517. Best, A. R., & Wilson, D. A. (2004). Coordinate synaptic mechanisms contributing to olfactory cortical adaptation. J. Neurosci., 24, 652–660. Cleland, T. A., Morse, A., Yue, E. L., & Linster, C. (2002). Behavioral models of odor similarity. Behav. Neurosci., 116, 222–231. Egana, J. I., Aylwin, M. L., & Maldonado, P. E. (2005). Odor response properties of neighboring mitral/tufted cells in the rat olfactory bulb. Neuroscience, 134, 1069–1080. Fletcher, M. L., & Wilson, D. A. (2003). Olfactory bulb mitral-tufted cell plasticity: odorant-specific tuning reflects previous odorant exposure. J. Neurosci., 23, 6946–6955. Hasselmo, M. E., & Bower, J. M. (1993). Acetylcholine and memory. Trends Neurosci., 16, 218–222. Hasselmo, M. E., Wilson, M. A., Anderson, B. P., & Bower, J. M. (1990). Associative memory function in piriform (olfactory) cortex: computational modeling and neuropharmacology. Cold Spring Harb. Symp. Quant. Biol., 55, 599–610. Imamura, K., Mataga, N., & Mori, K. (1992). Coding of odor molecules by mitral/tufted cells in rabbit olfactory bulb. I. Aliphatic compounds. J. Neurophysiol., 68, 1986–2002. Johnson, B. A., Farahbod, H., Xu, Z., Saber, S., & Leon, M. (2004). Local and global chemotopic organization: general features of the glomerular representations of aliphatic odorants differing in carbon number. J. Comp. Neurol., 480, 234–249. Johnson, B. A., & Leon, M. (2000). Odorant molecular length: one aspect of the olfactory code. J. Comp. Neurol., 426, 330–338.

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