Kate Xie ‘10
Harvard University, Department of Organismic and Evolutionary Biology
The neuronal circuitry underlying motor sequences is poorly understood, in part due to the inability to record from and modify neuronal activity in awake, behaving animals. A head-restrained rat preparation will facilitate the application of optical and neural recording techniques to behaving animals, while also simplifying the presentation of sensory stimuli and the quantification of behavior. The following paper deals with the design of a voluntary head-restrained method for training rodents. Rats were trained to self-initiate head restraint by inserting their implanted head plates into a mounting apparatus, which would then automatically clamp the head plates in place, thus head-restraining the rat. Rats were then trained to perform timed lever tap presses for a water reward. Our results demonstrate that self-initiated restraint is feasible and stable. Rodents could maintain self-initiated head fixation up to several minutes and performed precisely timed lever tap intervals for head-fixation trials greater than ten seconds. Moreover, the head plate implants could be maintained in all rats for at least ten months. Because head-restraint is self-initiated, this process allows for future automated training of animals. The stereotyped lever tapping behavior provides a quantifiable behavioral assay that will be useful for future studies involving the effects of fine circuit manipulation on execution and learning of motor behaviors.
Characterizations of movement and quantifiable behavioral paradigms have existed for well over a century. Over the same time period, technologies for recording neuronal activity have become increasingly sophisticated. However, the identification of neuronal circuits underlying behaviors has proven to be difficult, in part due to the limited application of recording techniques to awake and behaving animals. We hope to apply current techniques for optical neural recording and manipulation to awake behaviors by training rats to perform a lever-tapping task while voluntarily head-restrained, with the goal of identifying the cortical representation underlying a precisely timed motor sequence. While we have utilized our head-restraint preparation for the study of motor control, we also note that our method of head-restraint is useful for other sensory studies on olfaction, whisking, or visual behaviors.
Current knowledge of neural motor control: primate studies
In these studies, macaques were trained to push, pull, or turn a manipulandum while sitting still, a behavior requiring years of training. As a result, the observed behavior was overtrained, and the recorded neuronal activity reflected a strongly engrained behavior. While single-unit recording experiments in macaques are useful in determining the functional organization of motor areas with regard to well-learned sequences, they are unable to identify the reorganization of neuronal networks during the learning of a task.
There is evidence that the encoding of movements in rodent motor cortex may be similar to that in primates. Graziano noted this possible similarity: prolonged stimulation of one subregion within the whisker representation of motor cortex evoked a defensive whisker retraction and eyelid closure reminiscent of defensive postures evoked by continuous motor cortex stimulation in macaques (Haiss and Schwartz 2005, Graziano 2006). Georgopoulous et al. suggested that movement in primates may be encoded by populations of neurons, as evidenced by a series of averaged single-unit recordings (Georgopoulos 1986). Chapin et al. found similar evidence for the population coding of movement in rodents, where microelectrode arrays were implanted in rats trained to move a lever to obtain a water reward. Furthermore, when lever control was switched from forepaw movements to neuronal input, population activity successfully influenced lever control (Chapin et al. 1999). Given the large number of similarities between rodent and primate motor areas, the study of motor control in rodents may elucidate the mechanisms of learning and the representation of learned movements in primate and human brains.
We propose to study the neuronal basis of motor learning in rodents, which learn quickly and are less costly than primates, thus allowing for more invasive and potentially much more informative experiments. The rodent forepaw is dexterous; Whishaw and Coles characterized the various digit and paw manipulations during food handling in rats, finding limb preferences and differential usage of digits (Whishaw and Coles 1996). Rats can easily learn a lever tapping behavior within a few training sessions, allowing for the possibility of recording neuronal activity during acquisition of a motor sequence. Lastly, a rodent model for motor control allows for future genetic manipulations of the circuitry involved in this behavior.
Previous uses of head-fixation
The focus of several rodent head-fixation techniques thus far has been to isolate movements for quantification of behavioral tasks. Studies of whisking behavior in rodents have used various methods of head fixation to accurately measure whisker movements (Wesson et al. 2009, Irina et al. 2009, Hadlock et al. 2007). The method of head fixation is typically via a screw implanted in the head, which is then affixed to a mounting device (Bermejo et al. 1998, Margrie et al. 2002).
The potential to apply methods traditionally reserved for anesthetized animals to head-fixed animals has not gone unnoticed, however. Dombeck et al. used 2-photon imaging in conjunction with head-fixation techniques in mice to optically record neural activity during behavior, allowing them to discriminate between the cortical representations of forepaw movement during movement and grooming (Dombeck et al. 2007, 2009). Mice implanted with head plates were affixed to a stationary apparatus, while their body rested on a frictionless Styrofoam ball, allowing for maneuvering of the limbs (Figure 1). Brain motion associated with the running condition was only 2-5 µm and mostly confined to the focal (x-y) plane, and could therefore be corrected by software (2007).
Recently, Isomura et al. successfully used a head-fixed method in rats to achieve juxtacellular recordings across all layers of motor cortex during voluntary movements. Following an initial head-plate implant, head-fixed rats were trained to pull and push a lever at 1 Hz for a water reward. After learning the task, the rats underwent a second surgery, where an electrode was implanted into motor cortex for juxtacellular recording on the subsequent day (Isomura et al. 2009). Juxtacellular recording had previously characterized neurons exclusively in anesthetized preparations; development of a head-fixation apparatus was necessary to achieve the stabilization to support juxtacellular recording. Furthermore, neurobiocytin filling of recorded neurons following juxtacellular recording experiments allowed for definitive histological identification of the recorded cell. As a result, Isomura et al. were able to electrophysiologically characterize excitatory pyramidal cells and small inhibitory interneurons, as well as identify the layers of cortex to which each cell belonged. In addition to characterizing movement-specific cells similar to those identified in macaques by Tanji et al., Isomura et al. also found pre-movement, post-movement, movement-off, and hold related neurons ((Isomura et al. 2009, Tanji 2001, Tanji and Mushiake 1996).
Potential applications of head-fixation: optical manipulations
Pioneering experiments suggest that photostimulation of neurons expressing channelrhodopsin is feasible, suitable for long-term circuit manipulations, and can modify behavior. Ayling et al. have used channelrhodopsin photostimulation to create a map of motor cortex in transgenic mice (2009). These maps were created from laser stimulation of anesthetized mice with sealed cranial windows. By using a scanning laser procedure, they were able to obtain cortical maps at speeds about two orders of magnitude faster than typical electrode techniques (2009). Furthermore, because they used noninvasive light stimulation in combination with sealed cranial windows, they were able to conduct stimulation experiments 7-10 days apart on the same animal (2009). Another experiment by Huber et al. combined channelrhodopsin stimulation of mouse barrel cortex with a nose-poke task (2008). Mice were trained to poke their noses into a left port if stimulated or a right port if not stimulated in barrel cortex. All animals were able to reliably perform this task, indicating that channelrhodopsin excitation can drive changes in behavior in awake animals (2008).
The potential to merge channelrhodopsin stimulation with a head-fixing method rests in its noninvasive method of photostimulation, which allows for long-term experiments without damaging the brain, speeds at which experimental manipulation can occur, and the ability to affect behavior. Head-fixing would offer further opportunities to increase the specificity of neuronal excitation: in addition to the ability to selectively express channelrhodopsin in neuronal subtypes, light stimulation can be targeted to specific areas.
Goals and objectives
The present paper aims to use head-fixation to assay the neural control and learning of a timed lever-tapping task. Used in conjunction with optogenetic techniques, we hope to correlate neural activity with behavioral output during learning. Unlike previous head-fixation techniques, rats will voluntarily head-fix themselves and perform precisely timed lever presses to obtain a water reward. Rats that voluntarily head-fix themselves will provide easy experimental access to motor cortex and an opportunity to gain an accurate representation of the functional role of motor cortex during the acquisition and learned phases of a behavior.
There are 2 specific aims of this study:
1. Design a method for voluntary head-fixation.
2. Develop a behavioral task appropriate for the head-fixed context.
Aim #1: A method for voluntary head-fixation
To distribute torque on the head implantation, we opted to utilize head-plates instead of head-screws to mediate head-fixation. Figure 2 shows two head-plate designs. One version incorporated 1 mm diameter holes to increase surface area of the plate for better adherence to the skull (Figure 2a). A large opening in the center of the head-plate allowed for access to motor cortex after the head-plate was implanted. A later version of the head-plate, shown in Figure 2b, was modified to be removable, allowing new designs to be rapidly tested. Three small holes allow the head-plate to be attached, via three screws, to brass threaded inserts permanently implanted on the rat (see Methods for details).
Two rats were implanted with permanent aluminum head-plates, while another rat was implanted with the threaded-inserts. Remarkably, all three rats retained their head-plate implants for the entire 10 month duration of the experiment. The success of our head-plate implants was likely due to a combination of several factors: maximization of head-plate mounting contact with skull area, implantation of fully grown rats, and also our method of voluntary head-fixation.
The final version of our apparatus for head-fixation required rats to insert their attached head-plates into a slot following the onset of an LED signal (Figure 3). The behavioral task is described in Figure 3a. Full extension of the head into the slot, as confirmed by image region of interest (ROI) detection (see Figure 3c), triggered the descent of air-pistons on either side of the head-plate, thus securing the head-plate in place. The amount of force on the head-plate was sufficient to constrain movement, but not so forceful as to prevent the rat from releasing itself. Rats could thus terminate a trial by removing themselves from piston clamp, otherwise the trial would automatically terminate after a specified maximum time, usually 10 s. The maximum trial length was decided by a combination of factors: the comfort of the animal, consistent reproducibility of maximum trial length, and by the amount of time necessary for the proposed optogenetic manipulations. While the rats were capable of maintaining contact with contact sensors for periods up to several minutes when trial durations were not constrained (data not shown), we were more interested in obtaining a consistent behavior to increase the number of useable trials for optogenetic stimulation experiments.
Figure 3d shows the pooled results of training for two animals. The imposition of a maximum trial length reduced long trial durations (> 20 s) while increasing intermediate trial durations (5-15 s). Trials lasting longer than maximum head-restraint duration occurred because rats would remain in the apparatus after piston release.
Figure 4 reveals the shift towards longer, more consistent head-restrained periods over the course of training for one rat, and Figure 5 shows two complete sessions, early and late in the training period, in greater detail. Some session-to-session performance variability appeared to be correlated with breaks in training periods: performance tended to decrease during the first session immediately following weekends, during which the rats were allowed access to water adbitum. Although there is some variability in performance from session to session, a trend towards increased trial durations greater than 10 s occurs, coupled with diminished intermediate trial lengths between 5-10 s, indicating that our modified behavioral paradigm gradually shifted head-restraint periods towards 10-15 s durations, which fall well within the range of time needed to stimulate neurons expressing channelrhodopsin and monitor resultant behaviors (Ayling et al. 2009).
Although the trained head-restrained behavior allotted sufficient time for optogenetic techniques, was it sufficiently stable to allow for precise targeting of cortical areas? To answer this question, we used a camera to capture head-plate motion over several head-restraint trials (results shown in Figure 6). Even though piston strength was not great enough to prevent animals from retracting themselves from piston hold, the pistons substantially limited head-plate motion. Because the resolution of our camera was equivalent to 50 μm per pixel, we can only conclude that head-plate motion was less than ± 25 μm, although head-plate motion was likely much less in actuality (Figure 6b). These results are potentially comparable to brain motion measured by Dombeck et al., which was 2-3 μm and sufficient for 2-photon imaging (2007). Channelrhodopsin stimulation of distinct cortical areas requires less precision than 2-photon imaging: the beam width of stimulation used by Ayling et al. was 170 μm wide, and dendritic arbors of layer 5 cortex neurons are estimated at 300 μm across (Ayling et al. 2009). Thus, the amount of head-plate motion in our apparatus is small enough to allow photostimulation techniques.
Aim #2. Development of a behavioral task appropriate for the head-fixed context: timed interval tapping
Training a behavior in a head-restrained context differs from training a behavior in a freely moving context in that there is minimal physical separation between behavioral task and reward. Separation of task and reward allows for a clear distinction between task trials: consecutive trials are naturally divided between reward periods. It is difficult to segment a period of head-fixation into discrete behavioral trials without an additional cue signaling the beginning of a new trial. Rather than implement a discrete sequenced behavior, such as a two tap sequence, we decided to train rats on a timed interval tapping task, which is described in Figure 7a. Rats were shaped to produce lever-taps within a range of time intervals, with maximum reward at the specified target interval. Rats were first rewarded for any lever press and then trained to produce precise intervals between taps, or inter-tap intervals, which were rewarded based on a linear reward function, described in Figure 7b. The range of rewarded inter-tap intervals was gradually narrowed throughout the course of training. Both rats trained on this task learned to tap the lever while head-restrained. Figure 7c shows snapshots of a rat simultaneously head-restrained and lever-pressing. To motivate rats to maximize time spent head-fixed for the entire duration of the trial, we shortened training sessions from hour-long sessions to twenty-minute sessions. Occasionally, rats were trained multiple times per day to obtain sufficient quantities of water.
Two rats were trained on the interval tapping task; both were initially trained with a 500 ms target interval, then one was switched to learn a 650 ms target early in the training period. Figure 8 shows the gradual shifting of median inter-tap interval throughout training. By the end of training, one rat’s median inter-tap interval approached within 100 ms of the 500 ms target. While the other rat showed a similar trend, with median inter-tap intervals greater than 400 ms at the end of training, it did not closely approach the 650 ms target interval.
Graphs plot the median inter-tap interval over several training sessions for two rats. Linear regression lines and corresponding correlation coefficients.
The mechanism of the shift towards longer inter-tap intervals was not readily apparent in the distribution of tap timings produced during a single training session (Figure 9). The distribution of inter-tap intervals appeared to be uniform within a session, although the median inter-tap interval shifted from session to session (Figure 8, compare Figure 9a and b). To further investigate mechanisms by which learning might occur, we examined the distribution of inter-tap intervals with respect to the occurrence of the first tap of a head-fixed period (Figure 10a). Figures 10b-i show the results of this analysis for two rats over the same training sessions depicted in Figure 8.
Inter-tap times tended to increase throughout a head-restraint period (Figure 10b, f). Because the distribution was skewed towards long inter-tap intervals, the median may be a better indicator of the center of the inter-tap interval distribution and is therefore shown in Figure 10c and g. Further analyses were conducted on the mean (Figure 10d, e, h, i). Inter-tap intervals that occurred early in a head-fixed trial were significantly shorter than those occurring at late trial times (Figure 10e, Student’s T-test, p<0.0001; Figure 10i, Student’s T-test, p<0.0001).
Although Rat 2 was trained on a longer target inter-tap interval than Rat 1, it performed faster inter-tap intervals overall, throughout the entirety of a trial. However, both rats shared the same minimum reward boundary. While Rat 1’s inter-tap intervals approached the target interval towards the end of a trial, Rat 2’s inter-tap intervals fell short of its target interval, but became consistently higher than the minimum reward range (Figure 10f). It is possible that the two rats use different strategies to obtain water reward: Rat 1 may have maximized reward size, while Rat 2 may have maximized number of rewards. Because a sizeable proportion of Rat 1’s inter-tap intervals fell near the target interval, it received the full spectrum of the graded reward and therefore could utilize reward size as feedback to optimize its inter-tap intervals. In contrast, Rat 2’s inter-tap interval distribution initially overlapped much less with the reward distribution. Therefore, it rarely received the maximum reward. As a result, reward size could have been construed as binary feedback rather than graded feedback. This may be a reason why the center of Rat 2’s inter-tap interval distribution was closer to the minimum reward inter-tap interval than the target interval, even during late trial times. Regardless, the significant increase in mean inter-tap interval from early to late trial times for both rats suggest that they may be using online feedback to modify their behavior within a trial (Figure 10e, i).
Our method of head-fixation will greatly facilitate targeted optogenetic stimulation experiments; our piston apparatus substantially decreases head-plate motion to levels that allow reliable stimulus targeting for optogenetic manipulations. Behaviorally, we found that rats can be trained to voluntarily initiate head-fixation and learn a timed lever-tap task while head-restrained.
Rodents as a model organism for the study of motor control
The rat has been most popular as an animal model for behavioral studies in the context of whisking and olfaction. The majority of rodent studies thus focus on sensory perception in rats (Diamond et al. 2008). We show evidence that the rat may also be a good candidate as a model organism for the study of motor control: rats were able to coordinate precise lever-tapping behavior with self head-restraint behavior. Rats can quickly acquire a lever-pressing behavior in the context of head-fixation over a period of less than a month. In contrast, primate models of motor behavior require years of training and are thereby restricted to studies of the neuronal representation of overtrained behaviors. Rodents’ faster training periods provide an opportunity for future studies investigating the changes in underlying neuronal representation during acquisition of motor behaviors.
Learning timed motor behaviors
The behavioral paradigm we presented to rats shaped a continuous rather than discrete lever tapping behavior. While initially intended to simplify the task by minimizing the number of cues needed to signal time points during a trial, our continuous interval tapping task inadvertently led to the unexpected opportunity for learning, via online feedback, to modify behavior within a single head-fixed trial. Both rats appeared to use online feedback to modulate their behavior (Figure 10), but Rat 2’s inter-tap intervals did not approach its longer inter-tap interval target as closely. To determine the reason why it may be more difficult to learn longer inter-tap intervals, further experiments can be done with an online shifting reward distribution to more strongly favor the targeted interval and also to promote gradual shifts to longer intervals.
Our method contributes additional improvements to current head-fixation studies: first, an automated head-restraint technique, and second, a new philosophy—voluntary head restraint. The automation of head restraint will allow for large-scale training of animals and minimize time expended by the experimenter to train animals. By using image ROI detection, we were able to reliably initiate periods of head restraint automatically. Because the pressure exerted on head-plates by the pistons was minimized, automated head restraint was safe, and we report a longer head-plate implant lifetime than all previous studies to our knowledge.
Our method of voluntary head-restraint introduces a novel approach to head-fixation, which has typically been accomplished by gradual acclimatization of the animal to the restraining apparatus. We extrapolated Isomura et al.’s idea of voluntarily initiated movements to include head-restraint as a voluntarily initiated movement (Isomura 2009). We thus approach head-restraint as a behavior that can be trained, rather than as an experimenter-imposed condition. Self head-restraint simply becomes one element of the action syntax that our animals are required to learn.
Future studies could expand upon our head-fixation and training techniques. We have already demonstrated that rats can initiate head-fixation, but we did not explore a full range of techniques to terminate a head-restraint period. The use of force sensors, the addition of another “pistons-off lever, or the omission of lever-tapping for a certain time period represent some means by which an animal may be trained to communicate its desire to end a head-restrained period.
Applications of our head-restraint technique
The establishment of a method of head-fixation has implications beyond the field of motor control. The majority of head-fixation studies thus far have utilized head-fixation to facilitate delivery of a sensory stimulus. Our voluntary restraint and head-plate designs offer new approaches to these head-restraint techniques, and can facilitate animal training and increase head-implant longevity.
Our head-restrained, lever-tapping behavior was optimized for targeted optogenetic manipulations of motor areas. The amount of head-plate motion observed allows for specific laser targeting for channelrhodopsin stimulation, and the 10 s trial period comfortably allows sufficient time to use this fast technique and observe subsequent changes in behavior (Ayling et al. 2009). These studies would have the potential not only to define the roles of subpopulations of neurons within motor areas, but also to provide targeted direct manipulation of neuronal circuits in awake and behaving animals.
Materials and methods
All animal protocols used in this study were approved by the Animal Care and Use Committee. All rats in this experiment were male Long-Evans rats. Animals were handled initially to become familiar with experimenter handling. After head-plate implantation and recovery, rats were water deprived for 24 hours and then trained to obtain a water reward by pressing their head-plates against metal posts, which simulated a sensation of head restraint. After several weeks, animals were placed into the cage containing the head-restraining apparatus, where they were trained to insert their head-plates into a fitted slot. Because we simultaneously trained animals and designed a head-fixing apparatus, the time courses of training periods were determined mainly by the stage of development of the head-restraining apparatus. Animals received on average 5 mL of water per day while performing the task; they would also obtain access to water following the experiment if they performed poorly. Additionally, they were given free access to water two days out of the week.
Head-plates were designed using CAD software available from an online machine shop (http://emachineshop.com), and were manufactured from 1.2 mm thick aluminum sheets. Removable head-plates were attached, via standard 2-56 screws, to threaded inserts (McMaster-Carr, USA: part number 93365A112). Both head-plate versions include a large window in the middle of the head-plates for access to motor and somatosensory cortex.
All versions of the head-restraining apparatus were built along with Adam Kampff. The apparatus was first designed using Google SketchUp (http://sketchup.google.com). Pistons used for head-restraint were purchased from McMaster-Carr (Part number 6498K115). The lever was obtained from Cherry Electronics (Part number E33-50KL). Small cameras used for ROI detection were obtained from supercircuits.com. Software used to control our experiments was programmed in LabView (National Instruments, USA).
Rats were anesthetized under isofluorane anesthesia. An incision was made longitudinally along the top of the head to expose the underlying skull. Forceps were used to remove the membrane covering the skull. Using a dental drill, ridges were created in the bone to increase surface area of skull for adhesion. The dental drill was then used to drill six holes into the skull, three rostrally and three caudally, avoiding the area overlying motor cortex. Six self-tapping stainless steel 1 mm bone screws were screwed into these six locations, and a thin layer of superglue was applied to the juncture between the skull and bone screws. It was important to minimize the size of drilled holes by one clean penetration of the dental drill for strongest affixation of the bone screws to the skull. The skull was left for twenty minutes to dry before a thin layer of luting cement (RelyX Luting Plus) was applied to the skull at areas surrounding the screws. Subsequent layers of dental cement were applied, with fifteen minutes of drying time in between each layer, until the level of dental cement matched the tops of the bone screws. At this point, either permanent head-plates or threaded inserts were cemented on top of the bone screws. Rats were administered 10 mg/kg ketofen analgesic subcutaneously immediately following surgery and every 24 hours for the first 48 hours post-surgery. A total of four days was allowed for recovery.
All behavioral analyses were performed in Matlab and Microsoft Excel. Head-plate motion analysis was conducted in ImageJ.
Asanuma, H. and A. Keller (1991). Neuronal mechanisms of motor learning in mammals. Neuroreport 2(5): 217-224.
Ayling, O. G., T. C. Harrison, et al. (2009). Automated light-based mapping of motor cortex by photoactivation of channelrhodopsin-2 transgenic mice. Nat Methods 6(3): 219-24.
Bermejo, R., D. H., H. Philip Zeigler (1998). Optoelectronic monitoring of individual whisker movements in rats. Journal of Neuroscience Methods 83: 89-96.
Boyden, E. S., F. Zhang, et al. (2005). Millisecond-timescale, genetically targeted optical control of neural activity. Nat Neurosci 8(9): 12fmt63-8.
Bremmer, F. (2005). What’s next? Sequential movement encoding in primary motor cortex. Neuron 45(6): 819-21.
Bruce, D. 1994. Lashley and the Problem of Serial Order. American Psychologist 49(2):93-103.
Carey, R.M, J. V. V., Daniel W. Wesson, Nicolás Pírez and Matt Wachowiak (2009). Temporal Structure of Receptor Neuron Input to the Olfactory Bulb Imaged in Behaving Rats. J Neurophysiol 101: 1073-1088.
Chapin, J. K., K. A. Moxon, et al. (1999). Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat Neurosci 2(7): 664-70.
Diamond, M. E., M. von Heimendahl, et al. (2008). ‘Where’ and ‘what’ in the whisker sensorimotor system. Nat Rev Neurosci 9(8): 601-12.
Dombeck, D. A., A. N. Khabbaz, et al. (2007). Imaging large-scale neural activity with cellular resolution in awake, mobile mice. Neuron 56(1): 43-57.
Fee, M. S. (2000). Active Stabilization of Electrodes for Intracellular Recording in Awake Behaving Animals. Neuron 27: 461-468.
Georgopoulos, A. P., A. B. Schwartz, et al. (1986). Neuronal population coding of movement direction. Science 233(4771): 1416-9.
Georgopoulos, A. P., H. Merchant, et al. (2007). Mapping of the preferred direction in the motor cortex. Proc Natl Acad Sci U S A 104(26): 11068-72.
Graziano, M. (2006). The organization of behavioral repertoire in motor cortex. Annu Rev Neurosci 29: 105-34.
Graziano, M. S. and T. N. Aflalo (2007). Mapping behavioral repertoire onto the cortex. Neuron 56(2): 239-51.
Gunaydin, L. A., O. Yizhar, et al. Ultrafast optogenetic control. Nat Neurosci 13(3): 387-92.
Hadlock, T, J. K., Mackinnon, S., James T. Heaton (2007). A novel method of head fixation for the study of rodent facial function. Experimental Neurology 205: 279-282.
Haiss F, Schwarz C. (2005). Spatial segregation of different modes of movement control in the whisker representation of rat primary motor cortex. J. Neurosci. 25:1579–87.
Huber, D., L. Petreanu, et al. (2008). Sparse optical microstimulation in barrel cortex drives learned behaviour in freely moving mice. Nature 451(7174): 61-4.
Irina A. Topchiy, R. M. W., BreeAnne Peterson, Jinna A. Navas , and D. M. R. Manuel J. Rojas (2009). Conditioned lick behavior and evoked responses using whisker twitches in head restrained rats. Behavioural Brain Research 197: 16-23.
Isomura, Y., R. Harukuni, et al. (2009). Microcircuitry coordination of cortical motor information in self-initiation of voluntary movements. Nat Neurosci 12(12): 1586-93.
Kleinfeld, E. J. Y. a. D. (2002). Cortical Imaging Through the Intact Mouse Skull Using Two-Photon Excitation Laser Scanning Microscopy. Microscopy Research and Technique 56: 304-305.
Lashley, K. S. 1951. The problem of serial order in behavior. In L. A. Jeffress (Ed.), Cerebral mechanisms in behavior: The Hixon Symposium (pp. 112-146). New York: Wiley.
Lu, X. and J. Ashe (2005). Anticipatory activity in primary motor cortex codes memorized movement sequences. Neuron 45(6): 967-73.
Margrie, T.W., M. B., Bert Sakmann (2002). In vivo, low-resistance, whole-cell recordings from neurons in the anaesthetized and awake mammalian brain. Eur J Physiol 444: 491-498.
Miller, G.A. 1956. The magic number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Reviews, 63: 81–97.
Pryor, K. (2009). Reaching the Animal Mind: Clicker Training and What It Teaches Us About All Animals, Sunshine Books, Inc.
Tanji J. 2001. Sequential Organization of Multiple Movements: Involvement of Cortical Motor Areas. Annual Review of Neuroscience 24(1):631‐651
Tanji J, Mushiake H. 1996. Comparison of neuronal activity in the supplementary motor area and primary motor cortex. Cognitive Brain Research 3(2):143‐150.
Tanji J, Shima K. 1994. Role for Supplementary Motor Area Cells in Planning Several Movements Ahead. Nature 371(6496):413-416.
Tomita, H., E. Sugano, et al. (2007). Restoration of visual response in aged dystrophic RCS rats using AAV-mediated channelopsin-2 gene transfer. Invest Ophthalmol Vis Sci 48(8): 3821-6.
Watson, G. P. a. C. (2007). The Rat Brain in Stereotaxic Coordinates, Elsevier Inc.
Wesson, D.W., J. V. V., and Matt Wachowiak (2008). Why Sniff Fast? The Relationship Between Sniff Frequency, Odor Discrimination, and Receptor Neuron Activation in the Rat. J Neurophysiol 101: 1089-1102.
Whishaw, I. Q. and B. L. Coles (1996). Varieties of paw and digit movement during spontaneous food handling in rats: postures, bimanual coordination, preferences, and the effect of forelimb cortex lesions. Behav Brain Res 77(1-2): 135-48.