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INTRODUCTION |
Data demonstrating that the activity of complex-spike cells in the hippocampal formation increases when a rat or a monkey is in a specific location in the environment (Eichenbaum et al. 1987
; McNaughton et al. 1983; Muller et al. 1987; Nishijo et al. 1997; O'Keefe 1976; O'Keefe and Burgess 1996; O'Keefe and Dostrovsky 1971; Olton et al. 1978; Ono et al. 1991, 1993; Quirk et al. 1990; Wible et al. 1986; Wiener et al. 1989; Wilson and McNaughton 1993, 1994), combined with memory deficit after lesion of the hippocampal formation (e.g., Bunsey and Eichenbaum 1996; Mishkin 1978; Olton et al. 1979; Zola-Morgan and Squire 1990), have contributed to the understanding of functions of the hippocampal formation (Eichenbaum et al. 1992; McNaughton and Nadel 1989; O'Keefe and Nadel 1978). These complex-spike cells are called "place cells," and areas of the environment associated with firing increment are called "place fields." Spatial correlates of place cell activity can be influenced by controlling environmental cues (Breese et al. 1989; Gothard et al. 1996; Muller and Kubie 1987; Muller et al. 1987; O'Keefe and Conway 1978; O'Keefe and Speakman 1987) that affect the place cell activity.
Place cell activity may also be modulated by movement direction, speed, and turning angle as a rat moves through a place field (Breese et al. 1989; McNaughton et al. 1983; Wiener et al. 1989). In such studies rats approached reward locations through rather restricted trajectories in an eight-arm radial maze (McNaughton et al. 1983; Muller et al. 1994) or a square chamber (Breese et al. 1989; Wiener et al. 1989). On the other hand, Muller et al. (1987, 1994) showed relatively low directional selectivity in a behavioral paradigm in which rats searched for small food pellets scattered at random in a cylindrical apparatus. This discrepancy might be attributed to differences in the testing environments, because it has been suggested that the hippocampal neurons encode only salient cues (Muller and Kubie 1987; O'Keefe and Speakman 1987), local views (Leonard and McNaughton 1990; McNaughton et al. 1991), or structure of the maze (Muller et al. 1994). Furthermore, not only these sensory and/or motor factors but also cognitive factors such as behavioral context might affect place cell activity (Knierim et al. 1995; Kubie and Ranck 1983; Markus et al. 1995; Nishijo et al. 1997). Previously, we reported that monkey hippocampal neurons responded differently to the same percept (presentation of the same spatial cues and the same object) with the same behavioral response (go/no-go responses) when the object was presented in different contexts or situations (Eifuku et al. 1995; Nishijo et al. 1993; Ono et al. 1995). However, effects of context on neuronal correlates of rat place cells to movement variables (speed, direction, and turning angle) have not yet been tested in the same environment, except in a recent study that reported changes in neuronal correlates to direction between two different behavioral contexts (Markus et al. 1995). Reward availability might be also an important determinant of place cell activity. Two studies focused on whether or not place field shifted when the location of the reward was changed, and came to different conclusions: Breese et al. (1989) recorded place fields in rats as the rats explored a platform to obtain rewards available at five locations. When reward delivery was restricted, most of these place fields shifted to a single location. In contrast, Speakman and O'Keefe (1989) demonstrated that, after relocation of the goal, only 2 of 19 place fields changed, but the others retained their locations relative to cues in a cue controlled environment.
Despite discrepancies among the studies cited above, the evidence indicates, at least, that the hippocampal formation processes diverse types of information. The diverse information is suggested to be conjunctively or relationally implicated in the hippocampal formation to represent the environment in a unified manner (Eichenbaum 1993; Eichenbaum et al. 1992; Knierim et al. 1995; McNaughton et al. 1989; Young et al. 1994). The question is how the hippocampal formation encodes the ever-changing and surprising variety of perceptions to represent the environment. There are at least two strategies. The first strategy is that representation in the hippocampal formation remains unaltered when an animal is confronted with modest changes in sensory information within an environmental or a behavioral context. The lesion studies reported that rats with lesions in the hippocampal formation or fornix were impaired when they were confronted with a new situation in the Morris water maze or a stimulus-stimulus association task, and suggest that the hippocampal formation is important for processing information effectively and flexibly (Bunsey and Eichenbaum 1996; Cohen and Eichenbaum 1993; Eichenbaum et al. 1990). Unit recording studies indicate that some hippocampal neurons could maintain their place field after darkening the experimental room (O'Keefe and Speakman 1987; Quirk et al. 1990). The rats probably switch information to be processed in the hippocampal formation from visual in light to proprioceptive and/or vestibular sensation in darkness (Knierim et al. 1995; McNaughton et al. 1991). This first strategy for the hippocampal formation to process information, i.e., flexibility in selection of information, suggests that the hippocampal neuronal correlates would be susceptible to adapting to new relevant cues if a given context changes, even in the same environment. This change in sensitivity to movement variables should be tested in the same environment, because some changes in the environment can influence place cell activity (Muller and Kubie 1987; Quirk et al. 1990). The second strategy is that representation in the hippocampal formation completely changes when an animal is confronted with a relatively large change in an environmental or behavioral context. This second strategy corresponds to creation of a new representation, or "complete remapping" (creation of a new place field) (Muller and Kubie 1987; Quirk et al. 1990) in the hippocampal formation. Theoretical studies assert that sparse and conjunctive coding enables the hippocampal formation to represent a variety of episodes with relatively little overlap (McClelland et al. 1995; McNaughton and Morris 1987; O'Reilly and McClelland 1994). Previous studies propose that the extent of remapping reflects the magnitude of the difference between two environments (Muller and Kubie 1987; Quirk et al. 1990), although a recent study reported significant effects of behavioral contexts on the place fields in the same environment (Markus et al. 1995).
Recently, we developed a protocol for the study of place neuron activity in rats that combines rewarding intracranial self-stimulation with video monitoring of the subject's movement and location (Fukuda et al. 1992). The use of intracranial self-stimulation as a reward (Shizgal 1989) offers the following advantages over food or water rewards: 1) rapid learning of a task, 2) lack of satiation, and 3) absence of visual and olfactory information regarding the reward. This protocol enables us to perform many trials under different conditions within a short time and in a well-controlled environment. In the present study, using this paradigm, we identified the place fields of hippocampal place neurons in rats as they explored a circular open field to obtain an intracranial self-stimulation reward. To investigate the two strategies in the hippocampal formation noted above, we analyzed spatial, behavioral, and reward correlates of hippocampal place neurons of rats in the different behavioral and reward tasks but in the same environment. It should be noted, however, that changes in behaviors might falsely result in behavioral correlates of place neurons due to a sampling bias (Muller et al. 1994). For example, if the rat tends to exhibit a certain behavior (type X) in the place field of a place neuron, where neuronal activity is high, and another behavior (type Y) outside the place field, where neuronal activity is low, then correlation between the neuron activity and the type X behavior would be falsely high ("distributive hypothesis" of Muller et al. 1994). Therefore we further compared actual behavioral correlates of place neurons with predicted behavioral correlates (i.e., false behavioral correlates) based on the distributive hypothesis, in which neuronal correlates to behaviors are ascribed to the difference in time spent by the rat in different portions of the firing field of the place neurons in different behaviors. Finally, we assessed the plasticity of place neuron activity under conditions in which intracranial self-stimulation was delivered only outside the place field.
Preliminary reports of some of the data in this paper have been published in abstract form (Kobayashi et al. 1992, 1996).
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METHODS |
The methods used for the behavioral aspects of the present experiments using rewarding intracranial self-stimulation were substantially the same as those described in our methodological paper (Fukuda et al. 1992). The general methods are described below.
Subjects and surgery
Seventeen male albino Wistar rats were used. The rats weighed 270-320 g at the time of surgery, and were individually housed with food and water available ad libitum. All rats were treated in strict compliance with the US Public Health Service Policy on Human Care and Use of Laboratory Animals and the National Institutes of Health Guide for the Care and Use of Laboratory Animals.
Each rat was anesthetized with pentobarbital sodium (40 mg/kg ip) and implanted bilaterally with monopolar stimulating electrodes (enamel insulated, 200 µm diam, stainless steel) aimed at the medial forebrain bundle at the level of the lateral hypothalamus (4.3 mm caudal to the bregma, 1.6 mm lateral to the midline, and 8.8-9.0 mm ventral to the skull surface) according to the atlas of Paxinos and Watson (1986). Electrodes were attached to the skull with dental acrylic and stainless steel screws, one of which also served as the indifferent electrode for stimulation.
After 1 wk of recovery, the rats were screened for self-stimulation in an operant chamber and trained to perform place-related tasks in a circular open field (see below in detail). After this training, a recording electrode assembly was implanted above the CA1 layer of the dorsal part of the hippocampal formation (3.6 mm caudal to the bregma, 3.0 mm lateral to the midline, and 2.8 mm below the skull surface) under pentobarbital sodium anesthesia (40 mg/kg ip). This coordination allowed us to record neuronal activity not only in the CA1 but also CA3 layers. The recording electrode assembly consisted of a bundle of four or eight wires (Formvar insulated, 20 µm diam, nichrome) encased in a stainless steel cannula (25-gauge), a small platform, and two screws. The recording electrode assembly was advanced slowly while the neuronal activities from all of the recording electrodes were monitored. If a complex-spike cell, indicating a pyramidal neuron, was found, the assembly was left in that position for 20-30 min to verify stability, and the two screws were then mounted on the skull with dental acrylic. The acrylic formed a shoulder in a lubricated groove in the screw to enable future adjustment of the recording electrode assembly.
Apparatus
Spatial behavior was investigated in a 150-cm-diam circular open field with a 45-cm-high wall, painted black on the inside (Fig. 1Aa). The experimenter could manually move or rotate the field on casters attached to the bottom. The open field was enclosed by a black curtain (180 cm diam and 200 cm high). The ceiling of the enclosure contained four small speakers mounted near the circumference, spaced 90° apart, four light bulbs individually mounted near the inner edge of each speaker, and a video camera (Behavioral Tracking Analyzer, BTA-2A, Muromachi Kikai, Tokyo) at the center (Fig. 1Ab). Usually a light bulb was turned on at the 3 o'clock position, and a speaker continuously emitted white noise at the 9 o'clock position. A small light bulb was mounted on the head of the rat. The video camera converted a real video image signal to a binary signal, and tracked the two-dimensional (horizontal) motion of the small bulb. A laboratory microcomputer (PC-9801, NEC, Tokyo) received the X and Y coordinates of the position of the head through an RS-232C serial port at 20 frames per second. A program delimited circular areas (reward places) in the open field, and triggered the delivery of current for intracranial self-stimulation when the rat entered the reward place. The experimenter monitored the locations of the reward place and the rat on a display screen, but no distinctive local cues marked the reward place for the rat.
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| FIG. 1.
Scheme of experimental setup. Aa: open field (150 cm diam) containing rat was viewed from top center by video camera that signaled rat's position in Cartesian coordinates. Open field was enclosed by black curtain. Under usual conditions, electric bulb at 3 o'clock position and speaker at 9 o'clock position on ceiling of enclosure were turned on. Video signal was sent to conventional TV monitor, and digital interface connected to video camera sent X and Y coordinates of miniature light bulb attached to head of rat through RS-232C serial port to microcomputer. Microcomputer plotted trail of rat, compared rat's behavior with preset criteria, and gated delivery of intracranial self-stimulation as reward to medial forebrain bundle at level of lateral hypothalamus (LHA) from stimulator when criteria were met. CCD, charge-coupled device. Ab: view of ceiling of enclosure. Four small speakers (S) and 4 electric bulbs (L) near inner edge of each speaker were mounted at 90° intervals. Video camera (C) was positioned at center. N, north. Ba: random search task [random reward place search task (RRPST)]. Computer program delimited circular reward place (thick circle) at some randomly selected coordinate. Rat was rewarded with intracranial self-stimulation when it entered reward place, which was then made inactive (changed to thin circle). After 5-s interval, reward place was moved to different location and reactivated. Bb: place learning task 1 (PLT1). Rat received rewards in 2 target areas (thick circles) when it returned to one reward place after visit to other reward place. Delay time before delivery of rewards was introduced from 0.5 to 2.0 s in 0.5-s increments. Bc: place learning task 2 (PLT2). Rat received rewards in target area (thick circle) only when return to target area was preceded by visit to place field, which was now nonreward place (thin circle). S: location of rat at start of session. Dots: locations of reward delivery. Convention of thick circle around reward place and thin circle around nonreward place is used in all figures. C: schematic representation of instantaneous direction ( n) and turning angle ( n) of movement. Arrows on curve: movement direction. Points indicated by (n  1) × 100 ms and(n + 1) × 100 ms represent sequential locations 100 ms before and after passing through observed point (n × 100 ms). Instantaneous direction of movement at each location was calculated along vector between sequential locations 100 ms before and after passing through point observed (n). Instantaneous turning angle at each location within place field was estimated as arc subtended by 2 vectors connecting that location point and points 100 ms before and after passing through point observed (n). N, north (0°).
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Behavioral training
SELF-STIMULATION SCREENING.
The rats were screened to self-stimulate in an operant chamber (30 × 30 × 33 cm) equipped with a lever on one wall. Each lever press triggered the delivery of an 0.5-s train of 0.3-ms negative square-wave pulses at 100 Hz.
The current intensity for intracranial self-stimulation was determined to produce 40-70 lever presses per minute in the operant chamber. A stimulating electrode that produced stable lever pressing at a low current intensity was selected for each rat. The rats were trained to self-stimulate in daily 30- to 60-min sessions for 5-10 days until stable lever pressing was achieved. The current intensity, which was determined in this period for each rat, ranged from 100 to 250 µA, and was used throughout the following place tasks in the open field.
PLACE TASK TRAINING.
After the implantation of the recording electrodes, the rats were trained to perform a spatial behavior in the open field. In the first condition, current for intracranial self-stimulation was delivered when the cumulative distance traveled by the rat reached a given distance. The initial distance was 80 cm, and this was increased progressively to 120 or 160 cm. The rats were usually trained for ~1 h/day for 2-4 days until they learned to travel the open field continuously. They were then trained in the following three kinds of place tasks.
1) Random search task: in this protocol a reward place (72 cm diam) was delimited; its center was chosen at random within a square circumscribed around the open field. The rat was rewarded with intracranial self-stimulation when it entered the reward place, which was then made inactive. After a 5-s interval to minimize the effect of intracranial self-stimulation per se on neuronal activity, the reward place was moved to a different location and reactivated (Fig. 1Ba).
2) Place learning task 1: two 40-cm-diameter reward places were located diametrically opposite one another in the open field (Fig. 1Bb). The rat was rewarded in both reward places when it returned to one of them after a visit to the other one. After this shuttle behavior had been acquired, the rat was trained to remain in the reward place for a predetermined time, which was gradually increased from 0.5 to 2.0 s in 0.5-s steps, before receiving the intracranial self-stimulation. This delay was intended to ensure that the rat identified the reward place as such, and was not rewarded for simply being brought into the reward place by automatic locomotion.
3) Place learning task 2: two circular areas were positioned, as in place learning task 1, but the rat received reward in only one of them, as delineated by a thick circle, only after visiting the other circular area (Fig. 1Bc). The other area thus became a nonrewarded place, delineated by a thin circle, that still had to be visited to receive a reward in the reward place.
At the start of a session, the small electric bulb on the head of the rat was turned on, and a train of current for intracranial self-stimulation was delivered to activate the rat. Each session was terminated after 50 rewards had been delivered or 10 min had elapsed, whichever occurred first.
Recording procedures and data acquisition
After a few days of recovery from the implantation, the recording electrode assembly was driven at ~20-80 µm per day in 20-µm steps in the open field. If a complex-spike cell was found, the stability of the cell recording was tested by the running place tasks for 30-60 min. Neuronal activity was passed through a high-input impedance preamplifier made of a dual-channel field-effect transistor (2SK389, Toshiba Electric), amplified by a main amplifier, and converted to standardized pulses by means of a window discriminator. During intracranial self-stimulation, neuronal activity was isolated from the stimulus artifact by the window discriminator. The microcomputer summed the spikes (standardized pulses) over 50-ms intervals and combined this data with the X and Y coordinates of the head of the rat to construct a distribution map of the mean firing rate as a function of the position of the rat.
Data analysis
NEURONAL CORRELATES TO PLACE AND INTRACRANIAL SELF-STIMULATION.
Place fields were delineated by a process used previously (Breese et al. 1989; Muller et al. 1987). Clusters of 4.5 × 4.5-cm pixels with firing rates exceeding twice the mean firing rate were identified. All pixels that did not satisfy this criterion were eliminated. A place field could be continued through any edge shared by two pixels meeting the criterion, but not through corners. If one or more neighboring pixels satisfied the criterion, the field was expanded to include the pixel(s). Each added pixel was then tested for the presence of a neighboring pixel that met the criterion. When no neighboring pixel satisfied the criterion, the limit of the field was identified. The minimum size for a place field was set at nine pixels. Boundaries of a place field were established by constructing a rectangle that had one diagonal connecting the minimum X and Y coordinates with the maximum X and Y coordinates.
For each 2.0-s period before and after onset of intracranial self-stimulation, the mean movement speed and mean firing rate were calculated every 100 ms; rates and speeds inside and outside the place field were calculated separately. To analyze neuronal correlates to reward, the periods were divided into four phases: 1) preintracranial self-stimulation (0.5 s before onset of intracranial self-stimulation) or predelay (0.5 s before delay time in place learning task 1), 2) delay for place learning task 1 (0.5, 1.0, 1.5, or 2.0 s), 3) intracranial self-stimulation (0.5 s), and 4) postintracranial self-stimulation (0.5 s after end of intracranial self-stimulation). The mean firing rates in these phases for each task were separately compared by one-way analysis of variance (ANOVA) with a significance level of P < 0.05.
NEURONAL CORRELATES TO BEHAVIORAL VARIABLES.
We examined the behavioral correlates of the place neuron activity within the place field according to the procedure described by Wiener et al. (1989). The instantaneous direction of movement at each location within the place field was calculated along the vector between sequential locations 100 ms before and after passing through the point observed (Fig. 1C, n). The direction of movement was determined only for those locations through which the rat traveled at a rate >5 cm/s. We examined the relationship between movement direction and neuronal activity by averaging firing rate samples within 45° angular bins. The directions whose firing rates were above the mean firing rate for all directions were defined as preferred directions. The instantaneous turning angle at each location within the place field was estimated as the arc subtended by two vectors connecting a location point and the points passed through 100 ms before and after the point observed (Fig. 1C, n). The turning angle was determined only for those locations through which the rat traveled at a rate >5 cm/s. We examined the relationship between the turning angle and neuronal activity by averaging firing rate samples within 45° angular bins. The turning angles whose firing rates were above the mean firing rate for all turning angles were defined as preferred turning angles. The speed of movement at each location in the place field was calculated from the distance traveled in the same 200-ms interval used for the direction determination. The firing rate at each location was calculated as the number of spikes in each 50 ms. Preferred speeds were defined as those whose firing rates were above the mean firing rate for all speeds. The relation between movement speed and firing rate was plotted in 20-cm/s bins. The statistical reliability of each movement variable was evaluated by 
2 analysis of the firing rate at various speeds, directions, and turning angles on the basis of at least five observations.
To numerically measure breadth of responsiveness (tuning) of each place neuron to each behavioral variable, entropy value was calculated on the basis of the formula by Smith and Travers (1979), which was derived from the equation for entropy based on information theory (Shannon and Weaver 1949). For each neuron, the breadth of responsiveness to each behavioral variable (i.e., speed, direction, and turning angle) was separately calculated from the following formula (Smith and Travers 1979)
where H is breadth of responsiveness, K is a scaling constant, and pi is the relative response to each parameter of speed, direction, or turning angle. The value of K was adjusted so that H = 1.0 when pi was 1/n. On the basis of this definition, H = 0 means that the activity of a given neuron is highly tuned to one specific parameter, whereas H = 1 means that activity of a neuron is broadly tuned to all of the parameters in each variable. Entropy measures of breadth of responsiveness between the random search task and place learning task 1 were compared by paired t-test (P < 0.05).
Muller et al. (1994) proposed a distributive hypothesis, in which neuronal correlates to direction are ascribed to the difference in time spent by the rat in different portions of the firing field of the place cells in different direction sectors. Because the behavioral trails during navigation were more constant in place learning task 1 than in the random search task, the changes in the entropy measurements of breadth of responsiveness from the random search task to place learning task 1 might be ascribed to "distributive errors." To analyze this issue, we calculated the predicted firing in each parameter of speed, direction, and turning angle, with the use of the data within place fields and on the basis of the formula by Muller et al. (1994) in which place neurons are supposed to have no correlation to speed, direction, and turning angle of movements. The predicted firing rate at a parameter of 
[R()] is
where Pi is the firing rate in a pixel, Ti() is the time spent at the parameter of in the pixel, and n is the number of pixels within the place field.
Histology
After the recording electrodes were advanced below the hippocampal formation, the locations of the recording and stimulating electrodes were identified histologically. The rats were deeply anesthetized with pentobarbital sodium (50 mg/kg ip), and the stimulating site was marked by an iron deposit, created by passing a 20-µA positive current through the stimulating electrode for 30 s. The recording site was marked by an electrolytic lesion, created by passing a 20-µA negative current through the recording electrodes for 30 s. Each rat was then perfused through the heart with 50 ml of 0.9% saline containing heparin followed by 200 ml of 10% buffered Formalin-containing 2% potassium ferricyanide. The brain was removed and fixed in Formalin for 
48 h. Sections (75 µm) were cut on a freezing microtome and were then stained with cresyl violet.
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RESULTS |
Identification of place fields and behavioral correlates of place neurons by the random search task
The activity of 43 complex-spike cells was recorded from the CA1 (n = 22) and the CA3 (n = 21) subfields of the hippocampal formation of rats as they performed the random search task. Thirty-seven (86.0%) had place fields (CA1, 19; CA3, 18). The remaining six (CA1, 3; CA3, 3) had no place field that met the criterion described in METHODS. Figure 2 shows an example of a CA3 place neuron. Figure 2A shows an example of the trajectory and sites of application of intracranial self-stimulation (each dot) for a recording session, and the corresponding place field of the place neuron in the random search task. The rat traveled 20165.7 cm and obtained 50 rewards in 533.5 s. The trail and intracranial self-stimulation almost uniformly covered the open field (Fig. 2A, left). The firing rate map shows a place field as a rectangle in the center of the open field (Fig. 2A, right). The mean firing rate of this neuron in the pixels inside the place field was 3.2 ± 0.2 (SE) spikes/s, and the mean firing rate outside the place field was 1.4 ± 0.1 spikes/s. The mean firing rates of >37 place neurons inside the place fields were 0.9-11.6 (3.8 ± 0.4) spikes/s, and the mean firing rates outside the place fields were 0.1-3.1 (0.6 ± 0.1) spikes/s. Figure 2B displays a curve of the average movement speed (top) and a histogram of the average firing rate (bottom) inside (Ba) and outside (Bb) the place field shown in A; these were time locked to the start of the intracranial self-stimulation. The firing rates in preintracranial self-stimulation, intracranial self-stimulation, and postintracranial self-stimulation phases were significantly different from each other inside [ANOVA, F(2,12) = 5.76, P < 0.05] but not outside [ANOVA, F(2,34) = 1.77, P > 0.05] the place field. Of 37 place neurons tested in the random search task, 34 had significant reward correlates only inside the place field as shown in Fig. 2; the remaining 3 neurons had no significant reward correlates inside or outside the place field. Because a trial was terminated if 50 intracranial self-stimulation rewards had been delivered in one session, the insignificant reward correlates in these three neurons were ascribed to low numbers of total intracranial self-stimulation rewards inside the place fields. The low numbers of total intracranial self-stimulation rewards resulted in a low degree of freedom in the statistical analysis, which in turn resulted in an insignificant reported difference. However, it should be emphasized that no place neurons consistently had reward correlates both inside and outside the place field. Furthermore, changes in movement speed before and after delivery of intracranial self-stimulation were very similar inside and outside the place field, as shown in Fig. 2B, top. These findings indicated that the effects of intracranial self-stimulation on place neuron activity were due neither to the direct effects of intracranial self-stimulation nor to the direct effect of movement speed.
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| FIG. 2.
Example of spatial, reward, and behavioral correlates of place neuron activity during random search task. A: trail of exploration (left) and map of firing rate (right). This neuron increased its activity near center of open field. Large rectangle: place field of neuron (see detail in METHODS). Dots in trail map: locations of reward delivery. B: curve of average movement speed (top) and histogram of average firing rates (bottom), which are time locked to onset of intracranial self-stimulation within (Ba) and outside (Bb) place field. Horizontal bar below abscissa: duration of reward. C: relation between neuronal activity and speed (Ca), direction (Cb), and turning angle of movement (Cc) within place field. In Cb, 0° corresponds to direction of vector extending from center to 12 o'clock. In Cc, turning angles greater than and <0° correspond to right and left turns, respectively. H, numerical measure of breadth of hippocampal neuronal responses to each behavioral variable. A-C are from same neuron.
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Figure 2C shows the activity of the place neuron shown in A and B at various speeds (Ca), directions (Cb), and turning angles (Cc) of movement inside the place field. The neuronal activity was significantly modulated by speed(2 = 86.3, df = 5, P < 0.01; preferred speed = 50-90 cm/s), direction (2 = 64.3, df = 7, P < 0.01; preferred direction = 0-135°), and turning angle (2 = 53.0, df = 7, P < 0.01; preferred turning angle = 45-45°) over a wide range. Of the 37 place neurons, 28 had significant direction-dependent responses (CA1, 13; CA3, 15), 24 had significant speed-dependent responses (CA1, 11 neurons; CA3, 13), and 21 had significant turning-angle-dependent responses (CA1, 9; CA3, 12). To quantitatively assess the breadth of responsiveness to each behavioral variable, a numerical measure of the breadth of responsiveness was introduced. Although the neuron in Fig. 2 demonstrated significant behavioral variable-dependent responses, numerical measures of breadth of neuronal response to each behavioral variable were rather high (H 0.96).
Spatial and reward correlates of place neuron activity during the random search task and place learning task 1
To compare the spatial and reward modulation of place neuron activity in different behavioral tasks, a place field was initially identified in the random search task, and the activity of the same neuron was then tested in place learning task 1, in which one of the two reward places was located in the place field. Figure 3 shows examples of the spatial and reward correlates of a CA3 place neuron activity in the random search task and place learning task 1 with 0- and 1.0-s delay. The neuron had one large and two small place fields located at 1-3 o'clock during the random search task (Fig. 3Aa). The mean firing rate of the neuron in the pixels inside the three place fields was 5.6 ± 0.3 spikes/s, and the mean firing rate outside the place field was 0.5 ± 0.1 spikes/s. The firing rates during the preintracranial self-stimulation, intracranial self-stimulation, and postintracranial self-stimulation phases were significantly different inside the large place field [ANOVA, F(2,11) = 19.01, P < 0.01], and decreased during reward delivery (Fig. 3Ab). The post hoc test indicated that the firing rate during the preintracranial self-stimulation phase was significantly higher than the firing rate during the intracranial self-stimulation phase (Newman-Keuls test, P < 0.05). However, there was no significant difference in neuronal activity among the three phases outside the place field [ANOVA, F(2,28) = 1.23, P > 0.05; Fig. 3Ac]. During place learning task 1, the activity of the neuron corresponded spatially with the activity observed in the random search task (Fig. 3, Ba and Ca). In place learning task 1 with 1.0-s delay, the same effects of intracranial self-stimulation on neuronal activity as shown in the random search task were observed. In place learning task 1 with delay, the activity of the neuron was significantly influenced by intracranial self-stimulation delivered in the reward place inside the place field [ANOVA, F(3,23) = 32.04, P < 0.05; Fig. 3Cb] but was not influenced by intracranial self-stimulation delivered outside the place field [ANOVA, F(3,23) = 0.76, P > 0.05; Fig. 3Cc]. The post hoc test indicated that the firing rate during the preintracranial self-stimulation phase was significantly higher than the firing rate during the intracranial self-stimulation phase inside the place field (Newman-Keuls test, P < 0.01). In place learning task 1 without delay, the neuronal activity was not significantly influenced by the intracranial self-stimulation inside [ANOVA, F(2,20) = 0.70, P > 0.05; Fig. 3Bb] or outside [ANOVA, F(2,20) = 2.11, P > 0.05; Fig. 3Bc] the place field. Although the effects of intracranial self-stimulation reward were not statistically significant in place learning task 1 without delay, the activity changes were very similar to those in the random search task and place learning task 1 with delay. Thus the activity of the neuron inside the place field for all three tasks (random search task, and place learning task 1 with or without delay) was higher before intracranial self-stimulation than during intracranial self-stimulation.
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| FIG. 3.
Example of spatial and reward correlates of place neuron activity during random search task and place learning task 1. A-C: trail of rat, reward locations, and spatial distribution of neuronal activities in random search task (A), place learning task 1 without delay for intracranial self-stimulation (B), and place learning task 1 with 1.0-s delay (C). During random search task, place field was located in quadrant located between 1 and 3 o'clock (Aa), and activity increased before reward delivery within place field (Ab) but not outside place field (Ac). During place learning task 1 without (B) and with (C) delay for reward delivery, neuron had spatial correlates consistent with those observed in random search task (Ba and Ca). Activity of this neuron increased before reward delivery in reward place located within place field (Bb and Cb) but not outside place field (Bc and Cc). Inside: inside place field. Outside: outside place field. Speed: speed of movement. Thin lines below abscissas in Cb: delay periods in reward place. A-C are from same neuron. Other conventions as for Fig. 2.
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The relatively high firing rates 2-4 s before and after delivery of intracranial self-stimulation outside the place field in place learning task 1 with and without delay in Fig. 3, Bc and Cc, were attributed to the rat's being located inside or near the place field. The low firing rates around delivery of the intracranial self-stimulation in Fig. 3, Bc and Cc, were due to the rat's being located outside the place field, rather than to inhibition by intracranial self-stimulation. To analyze the spatial correlates of the CA3 place neuron described above in greater detail, 2.0-s periods before and after reward delivery inside the place field were examined in four segments of 1.0 s each in place learning task 1 with 1.0-s delay; the spatial correlates in each time segment were reconstructed (Fig. 4). In the first and second segments before reward delivery, the neuron fired at a higher rate, with spatial correlates that corresponded to the place field observed in the random search task (Fig. 4, Ba and Bb). In the third and fourth segments, the rat entered the place field of the neuron, but the neuronal activity was lower than in the random search task (Fig. 4, Bc and Bd); during intracranial self-stimulation it became very low, reaching zero at one time. Thus the effects of intracranial self-stimulation on this neuron were similar in both the random search task and place learning task 1. Inside the place field, the neuronal activity gradually increased before delivery of intracranial self-stimulation, continued to increase during the delay time, and stopped increasing immediately after the reward delivery. However, no differences of neuronal activity between these phases were observed outside the place field (not shown). These results indicated that the neuron had both place and reward correlates.
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| FIG. 4.
Spatial correlates of place neuron activity shown in Fig. 3 in different phases of place learning task 1 with 1.0-s delay. A: firing rate histogram is same as that shown in Fig. 3Cb. B: trail of rat is shown as small dots, sampled at 50-ms intervals, and firing rate map is superimposed. Neuron fired at lower rate after reward delivery, although rat stayed in place field (Bc and Bd). Dashed rectangles: place field of neuron identified by random search task in Fig. 3. Other conventions as for Figs. 2 and 3.
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Figure 5 shows another type of a CA3 place neuron with different reward correlates. This place neuron had two distinct place fields located at 6 and 8-10 o'clock in the random search task (Fig. 5Aa). The mean firing rate in the pixels inside the two place fields was 4.1 ± 0.3 spikes/s, and the mean firing rate outside the place field was 0.8 ± 0.1 spikes/s. In the random search task, neuronal activity was not significantly influenced by intracranial self-stimulation reward inside [ANOVA, F(2,3) = 1.50, P > 0.05; Fig. 5Ab] or outside [ANOVA, F(2,25) = 2.41, P > 0.05; Fig. 5Ac] the place field at the 6 o'clock location. Spatial modulation of this neuron was tested by place learning task 1, in which one reward place was located in the place field at 6 o'clock. In place learning task 1 with and without delay, the neuron retained the same 6 o'clock spatial correlate as observed in the random search task (Fig. 5, B and C), but in place learning task 1 without delay the 8-10 o'clock place correlate was not evident because the rat never went to this position (Fig. 5B). In place learning task 1 without delay, intracranial self-stimulation influenced the neuronal activity in the reward place inside the place field at 6 o'clock [ANOVA, F(2,23) = 28.62, P < 0.01; Fig. 5Bb], but the neuronal activity did not significantly change outside the place field before or after the intracranial self-stimulation [ANOVA, F(2,23) = 1.00, P > 0.05; Fig. 5Bc].
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| FIG. 5.
Another example of spatial and reward correlates of place neuron activity during random search task and place learning task 1. A-C: trail of rat, reward locations, and spatial distribution of neuronal activities in random search task (A), place learning task 1 without delay for intracranial self-stimulation (B), and place learning task 1 with 1.0-s delay (C). During random search task, place neuron had place fields at 6 o'clock and at 8-10 o'clock (Aa). During place learning task 1 without (B) and with (C) delay time, neuron increased its activity during and after reward delivery in reward place within place field (Bb and Cb) but not outside place field (Bc and Cc). A-C are from same neuron. Other conventions as for Figs. 2 and 3.
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The insignificant reward correlates inside the place field in the random search task seem to be due to relatively few intracranial self-stimulation rewards inside the place field (i.e., n = 4 in this case), as described in the former section. The neuronal activities in the 15 bins of the summed histogram before, during, and after the intracranial self-stimulation were significantly correlated between the random search task and place learning task 1 without delay (Pearson's correlation coefficient = 0.601, P < 0.05). This indicated that the effects of intracranial self-stimulation reward on neuronal activity were similar in both the random search task and place learning task 1 without delay. In place learning task 1 with 1.0-s delay, intracranial self-stimulation significantly modulated neuronal activity in the reward place inside the place field [ANOVA, F(2,23) = 10.9, P < 0.01; Fig. 5Cb] but not outside the place field [ANOVA, F(2,23) = 0.72, P > 0.05; Fig. 5Cc]; this is the same as in place learning task 1 without delay.
Figure 6 shows a detailed analysis of the spatial correlates of the same CA3 place neuron shown in Fig. 5 during place learning task 1 with 1.0-s delay. The neuronal activity was very low before reward delivery and during the fourth segment, although the rat was in the place field identified during the random search task (Fig. 6, Ba, Bb, and Bd). In the third segment, activity increased during and after the intracranial self-stimulation, and the spatial correlates were similar to those observed in the random search task (Fig. 6Bc). This characteristic increase in neuronal activity during and after the intracranial self-stimulation was also observed in the other two conditions (Fig. 5Ab and Bb). Thus spatial and reward correlates of this neuron did not change among these three conditions.
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| FIG. 6.
Spatial correlates of place neuron activity shown in Fig. 5 in different phases of place learning task 1 with 1.0-s delay. A: same firing rate histogram as shown in Fig. 5Cb. B: trail of rat is shown as small dots sampled at 50-ms intervals, and firing rate map is superimposed. Neuron fired at lower rate before reward delivery, although rat stayed in place field (Ba and Bb). Dashed rectangles: place field of neuron identified by random search task in Fig. 5. Other conventions as for Figs. 2 and 3.
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Of the 37 place neurons, 21 (CA1, 10; CA3, 11) had significant reward correlates, as shown in Fig. 3 (type 1), and 15 (CA1, 9; CA3, 6) had significant reward correlates, as shown in Fig. 5 (type 2), in at least one of the three tasks (i.e., random search task, and place learning task 1 without and with delay). When the neurons had reward correlates, the effects of reward were only evident inside the place field, as shown in Figs. 3 and 5.
Behavioral correlates of place neuron activity during the random search task and place learning task 1
The neuronal correlates of the hippocampal place neurons to behavioral variables (speed, direction, and turning angle of movement) were analyzed only inside the place field identified during the random search task, and were compared within three tasks (random search task, and place learning task 1 without and with delay). Figure 7 shows the neuronal correlates of the same place neuron shown in Figs. 3 and 4 to the behavioral variables. In the random search task, the neuronal activity was significantly modulated by speed(2 = 38.3, df = 4, P < 0.01; preferred speed = 30-90 cm/s; Fig. 7Aa), direction (2 = 85.6, df = 7, P < 0.01; preferred direction = 0° and 90-180°; Fig. 7Ab), and turning angle (2 = 53.79, df = 7, P < 0.01; preferred turning angle = 45-0-180°; Fig. 7Ac) over a wide range. However, neuronal correlates to some behavioral variables became more evident in place learning task 1. In place learning task 1 without delay, the neuronal activity was significantly modulated by speed (2 = 37.8, df = 4, P < 0.01; preferred speed = 10-30 cm/s; Fig. 7Ba), direction (2 = 20.7,df = 7, P < 0.01; preferred direction = 135-180° and 315°; Fig. 7Bb), and turning angle (2 = 12.47, df = 7, P < 0.01; preferred turning angle = 45°, 135-180°, and 45°; Fig. 7Bc). A comparison of the numerical measures of the breadth of responsiveness to behavioral variables between the random search task and place learning task 1 without delay indicated that the breadth of responsiveness in speed (H = 0.80) in place learning task 1 without delay became smaller than the breadth of responsiveness in speed in the random search task (H = 0.99), whereas there were no changes in the breadth of responsiveness in direction (0.98 vs. 0.98) and only a slight increase in the breadth of responsiveness in turning angle (0.98 vs. 0.99).
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| FIG. 7.
Neuronal correlates of behavioral variables of place neuron activity shown in Figs. 3 and 4 during random search task and 2 conditions of place learning task 1 within place field. A-C: neuronal correlates of speed (a), direction (b), and turning angle (c) of movement in random search task (A), place learning task 1 without delay (B), and place learning task 1 with 1.0-s delay (C). In random search task, preferred speed, direction, and turning angle were 30-90 cm/s (Aa), 0° and 90-180° (Ab), and 45-0-180° (Ac), respectively. In place learning task 1 without delay (B), neuronal correlates of firing shifted to ~10-30 cm/s for speed (Ba), to 135-180° and 315° for direction (Bb), and to 45°, 45°, and 135-180° for turning angle (Bc). In place learning task 1 with 1.0-s delay (C), neuronal correlates of firing shifted to ~10-50 cm/s for speed (Ca), to 45-0-90° and 180° for direction (Cb), and to 45-90° and 45° for turning angle (Cc). H, numerical measure of breadth of responsiveness in speed (a), direction (b), and turning angle (c) of movement. Note that numerical measures of breadth of responsiveness in speed in place learning task 1 without delay (H = 0.80; Ba) and with 1.0-s delay (H = 0.89; Ca) became smaller than in random search task (H = 0.99; Aa), and that numerical measures of breadth of responsivenessin direction in place learning task 1 with 1.0-s delay(H = 0.95; Cb) became smaller than in random search task (H = 0.98; Ab). Other conventions as for Fig. 2. A-C are from same neuron.
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The similar reduction in the numerical measures of the breadth of responsiveness was also observed in place learning task 1 with 1.0-s delay. In place learning task 1 with 1.0-s delay, the neuronal activity was significantly modulated by speed (2 = 73.0, df = 5, P < 0.01; preferred speed = 10-70 cm/s; Fig. 7Ca), direction (2 = 119.5, df = 7, P < 0.01; preferred direction = 315-0-180°; Fig. 7Cb), and turning angle (2 = 11.62, df = 7, P < 0.01; preferred turning angle = 45-90° and 45°; Fig. 7Cc). The numerical measures of the breadth of responsiveness to speed (H = 0.89) and direction (H = 0.95) in place learning task 1 with 1.0-s delay became smaller than those in the random search task (Fig. 7, A and C). However, it should be emphasized that these changes in breadth of responsiveness were observed only inside the place field, but not outside the place field (not shown).
Figure 8 shows the neuronal correlates inside the place field to the behavioral variables of the same CA3 place neuron shown in Figs. 5 and 6. In the random search task, the neuron had no significant speed modulation (2 = 4.8,df = 5, P > 0.05; Fig. 8Aa), but the directional (2 = 46.2, df = 7, P < 0.01; preferred direction = 45-180°; Fig. 8Ab) and turning angle (2 = 10.97, df = 7, P < 0.01; preferred turning angle = 90-135° and 90°; Fig. 8Ac) modulation were significant over a wide range. In place learning task 1 without delay, the neuronal activity developed significant speed (2 = 35.3, df = 5, P < 0.01; preferred speed = 10-70 cm/s; Fig. 8Ba), directional (2 = 69.5, df = 6, P < 0.01; preferred direction = 45-135° and 225°; Fig. 8Bb), and turning angle (2 = 17.86, df = 6, P < 0.01; preferred direction = 45°, 135-180°, and 45°; Fig. 8Bc) modulations that narrowed to more limited ranges. In place learning task 1 with 1.0-s delay, the neuronal activity was significantly modulated by speed (2 = 20.0, df = 5, P < 0.01; preferred speed = 30-90 cm/s; Fig. 8Ca), direction (2 = 46.7,df = 7, P < 0.01; preferred direction = 45-180°; Fig. 8Cb), and turning angle (2 = 20.86, df = 7, P < 0.01; preferred direction = 90°, 180°, and 135°; Fig. 8Cc). Although the neuronal correlates to the behavioral variables, except for the speed in the random search task, were evident in all three conditions, a comparison of the breadth of responsiveness indicated that all three numerical measures of the breadth of responsiveness for speed, direction, and turning angle became smaller in place learning task 1 than in the random search task (Fig. 8). In accordance with the place neuron shown in Figs. 3, 4, and 7, the breadth of the neuronal responses changed only inside the place field.
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| FIG. 8.
Neuronal correlates of behavioral variables of place neuron activity shown in Figs. 5 and 6 during random search task and place learning task 1 within place field. A-C: neuronal correlates of speed (a), direction (b), and turning angle (c) of movement in random search task (A), place learning task 1 without delay (B), and place learning task 1 with 1.0-s delay (C). In random search task, neuron fired at all speeds (Aa), at all directions except for 0° (Ab), and at all turning angles, especially 90° (Ac). In place learning task 1 without delay (B), neuronal correlates of firing shifted to ~10-70 cm/s for speed (Ba), to 45-135° and 225° for direction (Bb), and to 45°, 135-180°, and 45° for turning angle (Bc). In place learning task 1 with 1.0-s delay (C), neuronal correlates of firing shifted to ~30-90 cm/s for speed (Ca), to 45-180° for direction (Cb), and to 90°, 180°, and 135° for turning angle. Note that numerical measures of breadth of responsiveness in speed in place learning task 1 without delay (H = 0.90; Ba) and with 1.0-s delay (H = 0.80; Ca) became smaller than in random search task (H = 0.99; Aa), that numerical measures of breadth of responsiveness in direction in place learning task 1 without delay (H = 0.83; Bb) and with 1.0-s delay (H = 0.79; Cb) became smaller than in random search task (H = 0.95, Ab), and that numerical measures of breadth of responsiveness in turning angle in place learning task 1 without delay (H = 0.87; Cb) and with 1.0-s delay (H = 0.85; Cc) became smaller than in random search task (H = 0.97, Ac). Other conventions as for Fig. 2. A-C are from same neuron.
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The optimal behavioral variables in speed, direction, and turning angle of the random search task, place learning task 1 without delay, and place learning task 1 with delay did not change systematically. Most hippocampal neurons tended to respond more to high-speed movement in the random search task, but not in place learning task 1 with or without delay, as shown in Figs. 7 and 8.
Effects of task difference on breadth of responsiveness
We analyzed the breadth of responsiveness of 21 place neurons (CA1, 9; CA3, 12) that were tested in three tasks in the same way. Because there was no significant difference between the mean breadth of responsiveness of the CA1 and CA3 place neurons for each behavioral variable (Student's t-test, P > 0.05), the data from both CA1 and CA3 place neurons were analyzed together. First, we compared the entropy measurements of breadth of responsiveness, calculated from actual responses to each parameter of the behavioral variables, with those calculated from the predicted responses to each parameter according to the formula by Muller et al. (1994) (Fig. 9). In all three tasks (the random search task, place learning task 1 without delay, and place learning task 1 with delay), the entropy measures of breadth of responsiveness calculated from actual responses (filled columns) were significantly smaller than those calculated from predicted responses (open columns) to each of three parameters (paired t-test, P < 0.01 or 0.05). These results indicate that actual neuronal tuning to behavioral variables in all three tasks was significantly more selective than the neuronal tuning that was predicted according to the assumption that place neurons have no correlation to speed, direction, and turning angle of movements.
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| FIG. 9.
Comparison of numerical measure of breadth of responsiveness of hippocampal place neurons to each behavioral variable among random search task, place learning task 1 without delay, and place learning task 1 with delay. A-C: mean numerical measures of breadth of responsiveness (left; filled and open columns) to speed (A), direction (B), and turning angle (C) calculated with both predicted and actual responses, and mean differences between predicted and actual breadth of responsiveness (right; hatched columns). Open columns: entropy measures of breadth of responsiveness calculated with predicted responses based on formula by Muller et al. (1994). Filled columns: entropy measures of breadth of responsiveness calculated with actual responses. Note that in all 3 tasks (random search task, place learning task 1 without delay, and place learning task 1 with delay), entropy measures of breadth of responsiveness calculated with actual responses (filled columns) were significantly smaller than those calculated with predicted responses (open columns) to each of 3 parameters (paired t-test, P < 0.01 or 0.05), and that breadth of responsiveness of hippocampal place neurons based on actual responses to speed, direction, and turning angle in place learning task 1 without and with delay was significantly smaller than that in random search task (paired t-test, P < 0.01 or 0.05). Single asterisk: significant at level of P < 0.05. Double asterisk: significant at level of P < 0.01. Hatched columns, mean differences in breadth of responsiveness between predicted and actual responses. Post hoc test after 2-way analysis of variance (ANOVA) indicated that mean difference between predicted and actual breadth of responsiveness in place learning task 1 with delay was significantly larger than that in random search task (Newman-Keuls test, P < 0.05) and larger than that in place learning task 1 without delay (Newman-Keuls test, P < 0.05). Data were sampled from 21 hippocampal place neurons tested at least with random search task and place learning task 1 without or with delay.
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Second, effects of task difference on breadth of responsiveness were analyzed with the use of actual responses (filled columns). The breadth of responsiveness to movement speed in three tasks (random search task, place learning task 1 without delay, and place learning task 1 with delay) was 0.84-1.00 (0.96 ± 0.01), 0.56-0.99 (0.87 ± 0.02), and 0.66-0.97 (0.86 ± 0.02), respectively, and the breadth of responsiveness to direction in these three tasks was 0.72-0.99 (0.92 ± 0.01), 0.47-0.98 (0.84 ± 0.03), and 0.41-0.97 (0.84 ± 0.03), respectively. The mean breadth of responsiveness to movement speed and direction in place learning task 1 with and without delay was significantly smaller than that in the random search task (paired t-test,P < 0.01; filled columns in Fig. 9, A and B). The breadth of responsiveness to turning angle in three tasks (random search task, place learning task 1 without delay, and place learning task 1 with delay) was 0.75-0.99 (0.92 ± 0.01), 0.52-0.99 (0.89 ± 0.03), and 0.54-0.99 (0.86 ± 0.03), respectively. Similarly, a reduction of the mean breadth of responsiveness to turning angle in place learning task 1 was statistically significant (paired t-test, P < 0.05; filled columns in Fig. 9C). These results indicate that the neuronal correlates to behavioral variables became more evident in place learning task 1 than in the random search task.
Third, net effects of task difference on breadth of responsiveness were analyzed when the contribution of distributive errors was taken into account (hatched columns in Fig. 9). For this purpose, difference in breadth of responsiveness between predicted and actual responses for each neuron was calculated (i.e., predicted breadth of responsiveness minus actual breadth of responsiveness; hatched columns). A two-way ANOVA (task × behavioral variable) revealed a significant main effect of task [F(2,180) = 6.625, P < 0.02]. There was no significant main effect of behavioral variable [F(2,180) = 1.028, P > 0.05] or the task × behavioral variable interaction [F(4,180) = 0.419, P > 0.05]. The post hoc test indicated that mean difference between predicted and actual breadth of responsiveness in place learning task 1 with delay was significantly larger than that in the random search task (Newman-Keuls test, P < 0.05), and larger than that in place learning task 1 without delay (Newman-Keuls test, P < 0.05). The results demonstrated significant effects of task difference on breadth of responsiveness of place neurons even after the contribution of distributive errors was eliminated. Taken together, these results indicated that neuronal correlates of movement speed, direction, and turning angle significantly increased in place learning task 1.
Plastic changes of the spatial correlates of the place neuron activity in place learning task 2
Initially, the place fields were identified by the random search task in two or three sessions. Place learning task 1 was tested with and without delay in four or five sessions, and place learning task 2, in which the reward place corresponding to the place fields was changed to a nonreward place, was tested in four or five sessions. The changes in place field were analyzed in relation to reward place. We defined the place field changes as follows: 1) if a new place field during and/or after place learning task 2 moved away from the circular area where an initial place field was located during place learning task 1, which was the reward place in place learning task 1 and nonreward place in place learning task 2; and 2) only if the new place field did not overlap with that circular area. The place fields of the place neurons were very stable during both the random search task and place learning task 1. No place neurons shifted place fields during the random search task and place learning task 1, before place learning task 2 was introduced. During place learning task 2 and subsequent place learning task 1, 6 of 31 (19.4%) place neurons identified by the random search task and place learning task 1 developed new place fields associated with a reward place located outside the place fields that were initially identified by the random search task and place learning task 1. The other 25 (80.6%) did not change place fields. It must be emphasized that there were no relations between the place field changes and total numbers of sessions for all three tasks, and that all six place neurons shifted their place fields during or after place learning task 2.
Figure 10 shows an example of a CA3 place neuron that retained its spatial correlates when the reward place contingency was changed. This neuron had two place fields, located at 1 and 9-11 o'clock, in the random search task (Fig. 10A). In place learning task 1, where areas inside and outside the place fields were associated with intracranial self-stimulation reward, the neuron fired in the 9-10 o'clock point in the place field where the rat received intracranial self-stimulation (Fig. 10B). In place learning task 2, where only an area outside the place fields was associated with the intracranial self-stimulation reward, the neuron retained its original spatial correlates in the first and fourth sessions of place learning task 2 (Fig. 10, C and D).
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| FIG. 10.
Example of spatial correlates of place neuron activity that did not change in place learning task 2. A: place field was located between 9 and 11 o'clock and subfield was located at 1 o'clock in random search task. B: spatial correlates were retained in place learning task 1, in which 1 reward place was located in place field at 9-11 o'clock, and was 1st observed in random search task. C and D: spatial correlates were not changed in 1st (C) and 4th sessions (D) of place learning task 2, where reward was delivered only in reward place at 4 o'clock. A-D are from same neuron. Other conventions as for Fig. 2.
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Figure 11 shows an example of a CA1 place neuron that developed a new place field while it retained an original place field identified in the random search task, when the reward delivery location was changed in place learning task 2. The place field of this neuron was located between 5 and 6 o'clock in the random search task (Fig. 11A). A simi
Abstract
Introduction
Abstract
![<IT>R</IT>(ϕ) = <LIM><OP>∑</OP><LL><SUB><IT>i</IT>=1</SUB></LL><UL><SUP><IT>i=n</IT></SUP></UL></LIM>[<IT>Pi × Ti</IT>(ϕ)]/<LIM><OP>∑</OP><LL><SUB><IT>i</IT>=1</SUB></LL><UL><SUP><IT>i=n</IT></SUP></UL></LIM>[<IT>Ti</IT>(ϕ)]](/content/vol78/issue2/fulltext/597/img002.gif)
