One day, I decided to make muffins. Unable to find a hot-pad when they were done, I grabbed a towel to protect my hand from the hot pan. I misjudged the thickness of the towel and quickly found my hand a bit too warm for comfort. My first reaction was "HOT HOT HOT! Drop it now!" followed by "No! Don't drop the muffins!" I had a few options: drop the pan and save my hand but lose the muffins, save the muffins and burn my hand, or try to find some sort of compromise that would save both. Rather hungry, I instinctively went for the latter. Unfortunate for me, my brain's best solution was to switch the pan to my other, completely unprotected, hand, giving me a double scalding. At least I had some tasty muffins...
Our nervous system is constantly confronted with these sorts of choices. Granted, the above anecdote is rather complicated by motivational issues and more complex decision making processes. But we regularly choose between possible movements. Even if we know where we want to move, we still have to decide how to execute that movement since our limbs have an incredible number of degrees of freedom. How do we narrow down the infinite possible movements to just one?
Paul Cisek and John Kalaska’s 2005 Neuron paper (“Neural Correlates of Reaching Decisions in Dorsal Premotor Cortex: Specification of Multiple Direction Choices and Final Selection of Action”) studied the motor processing stream to examine how the brain plans movements when presented with 2 possible options. They found that the motor planning-execution stream acts as a funnel of sorts. Premotor dorsal cortex (PMd, commonly thought of as a ‘higher-level’ motor planning area of the brain) appears to plan movements to all potential targets, but once a decision is made, only information for the executed reach is passed on to the primary motor cortex (M1) to be implemented.
Cisek and Kalaska compared PMd and M1 activity when a monkey was presented with one or two potential reaching targets. In the one-target task, the monkey was briefly shown a single target, whose location he had to remember during a short delay period. A colored disk (appearing at the center of the screen) cued the monkey to make a reach to this remembered target location. In the two-target task, the monkey saw two differently colored potential reach targets. The color of the go cue told him which to choose. The researchers then examined the activity of neurons during these different task epochs.
This study focused on one particular property of motor cortex neurons known as tuning. Many neurons in the motor cortex seem to fire a lot when an animal makes or plans a movement in one direction (say right), and very little for the opposite direction (left). The firing for movement directions in between falls off as you get further from the neuron’s ‘preferred’ direction. Tuning describes these direction-preferences. Neurons in the motor cortex have preferred directions that roughly span the full movement space, and the so-called ‘depth of modulation’ (i.e. how strongly tuned it is) can vary from neuron to neuron. Also, these direction preferences may only appear during certain points in the movement planning-execution process.
Cisek and Kalaska recorded from several hundred neurons in PMd and ~40 neurons in M1 to compute their tuning functions as the monkey performed both tasks. Interestingly, this large collection of cells clustered into 4 categories, which seem to represent different stages of movement planning.
Potential response (PR) neurons: During the one-target task, these neurons showed tuning as soon as the movement target was presented. That is, they fired a lot when the targets were presented near their preferred direction, and this selective firing started as soon as the target was shown. The firing lasted through the delayed memory period until the movement was executed. But more interestingly, during the two-target task, PR neurons showed this tuning to both potential movement targets. Once the final movement target was cued, the activity shifted to represent only this location. PR neurons seem to help plan all potential reaches. Fittingly, these neurons represented nearly half of the recorded neurons in PMd and were only rarely found in M1.
Selected response (SR) neurons: Next-in-line in on the movement planning stream, SR cells only showed tuning after information about the final reach target was shown to the monkey (i.e. after the single target cue in a one-target task, or after the correct choice was cued in the two-target task). That is, they represented, and perhaps help plan, reaches to the selected movement direction. Again, these planning-oriented neurons were found primarily in PMd.
Build-Up (BU) and Movement (M) neurons: These cells both seemed linked to executing the planned reach. BU cells slowly developed a tuning for the final movement direction once the color cue was shown, gradually ramping up their activity as the monkey prepared to reach to the decided target. M neurons, however, only showed tuning after the go cue (i.e. once the monkey knew where he was reaching and started the movement). These cell types made up the majority of M1 neurons, and are not particularly new—several studies before this have found these sorts of movement-tuned neurons.
The four cell types Cisek and Kalaska found make a nice schematic picture of movement planning. We represent each potential movement target until we decide. After some choice is made, only the final movement information is passed down-stream. Finally, execution-related cells gear up to make the winning movement.
Movement planning results like this have interesting applications for brain-machine interfaces. There are two schools of BMI techniques: one based on decoding movement execution-related neural activity, and another focused on detecting this sort of higher-level planning information. If we can detect where someone is planning to reach, then we can drive a robot/cursor in that direction. Krishna Shenoy at Stanford, for example, has had some amazing success with this approach. These sorts of signals, however, don’t really allow for online corrections. Online feedback plays a huge role in our dexterous movements. Without it, we tend to get a bit inaccurate—think about making a really fast movement, where you can’t correct it, versus slowly reaching. Though less suited for motor BMI, this sort of ‘discrete’ BMI does lend itself incredibly well to other types of decoding, such as speech neuroprostheses. But, I think planning information is still potentially very useful for motor applications. For instance, using PMd activity to act as a prior, in your execution decoding could help reduce errors.
This study suggests that we plan several movements at once until we've decided, a very intuitive idea. It surely helps reduce our reaction times, and explains some common motor goofs. Ever done some weird combination of two movements when you had trouble deciding how to do something? Or said a mish-mash of two words you'd considered using?
But one huge lingering question is how motor cortex cells pick the ‘winning’ movement. How does the information transition between PR and SR neurons? This is where things surely get messy, with inputs from higher cognitive areas related to motivation, goals, visual processing, and so on all come in to play. I’m not signing up to try to decipher that sort of information-soup, but I’m glad others are on the case.
[Apologies for the long delay between posts. I caught a ridiculous head cold that knocked me out of commission for 3 weeks. I'm only now catching up.]
3.07.2009
1.30.2009
The Mind-Body Disconnection
Our brains and bodies are linked. Anatomically, the spinal cord and nerves provide conduits for information to travel between the two. Sensory information about the environment and current state of our bodies is relayed from receptors up to the brain; information and “commands” are transferred from the brain down to our muscles to generate movements. Psychologically, our state of mind can have profound effects on our physical well-being. Stress has a myriad of manifestations in our bodies, from difficulty sleeping to a racing heartbeat and difficulty breathing. Yet more mysterious are things like the placebo effect, where a false perception of treatment can actually yield physical improvements in health, which highlight the complex interaction between our brains and bodies.
But how hard-wired is this link? Can we dissociate the activity of our brains from our actions and physical selves? One clear example of this disconnect is motor imagery. Researchers have found that imagining movements involves the same regions of the brain that would be used to execute those actions [See Jeannerod and Frak Current Opinions in Neurobiology 1999 for a review]. Yet, we can easily think about movements without so much as a muscle twitch. Clearly, there is some amount of flexibility in our brain-body links—a toggle switch of sorts.
The disconnect between neural activity and our bodies begs the question of volitional control of the brain. Can we ‘disconnect’ the actions of our neurons and the muscles they normally control at will? Is it possible to make movements without activating the neurons once associated with that action? If so, what are the limits and resolution of our control? Perhaps we can only control large regions of the brain and in very simple ways; maybe we have precise control of individual neurons and can orchestrate complex activity patterns.
In 1971, Eberhard Fetz and Dom Finocchio addressed some of these questions with a unique experimental paradigm often credited as one of the first brain-machine interfaces. Their Science paper (“Operant Conditioning of Specific Patterns of Neural and Muscle Activity”) was among the first to highlight the incredible flexibility of the motor cortex. The researchers recorded the firing rate of individual neurons in the motor cortex along with EMG activity from several muscles on the animal’s arm. The monkey was given visual feedback of a weighted sum of these signals via a simple level-meter. Their experiments then used operant condition to link different combinations of activations with reward. That is, when the monkey achieved an activity level above a set threshold (i.e. moved the meter all the way to the top), he was given a bit of juice as a treat. By manipulating the weights in their summed average, the researchers could shift the goal of the task. For instance, with zero weights on muscle activity and a positive weight on the neural activity, the monkey would get rewarded for increasing the firing rate of the neuron with no restrictions on muscle activity. Negative weights on the EMG signals, however, require him to increase his neural activity without contracting his muscles.
Fetz and Finocchio explored how well monkeys could volitionally control neural and muscle activity using this set up. They tested 4 different reinforcement conditions: 1) increased neural activity with no restrictions on EMG, 2) activating single muscles (while suppressing all others) without restricting neural activity, 3) increased neural activity with suppressed EMG and 4) contracting single muscles while suppressing neural activity. The researchers found that the monkeys could, with time, learn all of these associations. They were able to successfully control their neural and muscle activity to obtain rewards. What’s more, in conditions 3 and 4, the monkeys learned to dissociate the two.
As might be expected, the disconnect conditions (3 and 4) were harder for the animals to learn than the individual muscle or neuron control conditions (1 and 2). Indeed, the researchers were only able to demonstrate muscle control without neural activity in one monkey that showed extremely adept control abilities. But that this dissociation could occur at all is an impressive demonstration of our central nervous system’s flexibility. More amazing still is that before the conditioning training, these neurons’ firing was often correlated with the contraction of one or more of the recorded muscles. The monkeys were able to overcome these preexisting connections. Not only could they control individual neurons, they could disconnect their brains and bodies.
This work (and a collection of other Fetz papers in the early 1970’s that further explore this paradigm) adds new complexity to neuroscience efforts to understand the activity and computations of motor cortex. Much of motor cortex research focuses on simultaneously recording movement-related variables (EMG signals, joint positions, etc.) and firing rates of neurons. A temporal correlation between the two is often interpreted as a causal link—the neuron is thought to encode activation of a particular muscle or joint location. The evident flexibility between cortical activity and the periphery, however, makes such conclusions less concrete. As Fetz and Finocchio point out, correlations are “necessary but never sufficient evidence for a causal relation.”
Fetz’s demonstrations of neural malleability also raise profound questions in the field of brain-machine interfaces (BMIs). The ultimate goal of BMIs is to use neural activity to control an external device like a prosthetic arm (decoding), and to relay sensory information back to the brain via neural stimulation (encoding). Current research in the decoding-half of BMI has taken a very biomimetic approach—experimenters use the existing correlations between neural activity and movement variables to create decoding algorithms. They try to tap-in to the motor cortex’s control scheme. If successful, these approaches would allow users to move a prosthetic arm with the same neural control they would have used for their own limb. But, as we’ll be exploring in later posts, deciphering the neural activity of motor cortex is anything but simple. Developing accurate decoding algorithms that capture the intricate detail of our motor control requires significant advances in our understanding of motor cortex.
Given that our brains are inherently flexible, should BMI decoding take advantage of this instead? We are able to control individual neurons, independent of their original motor functionality. Rather than BMIs attempting to decode natural neural activity into movements, perhaps the relationship should be switched—our brains could learn to control a neuroprosthesis. Indeed, there’s growing evidence that the brain is adapting to the decoders used in BMI demonstrations. For example, the relationship between neuron firing and movement direction often shifts between natural limb movement and neural control of a BMI [see Carmena et alPLoS Biology 2003]. The decoding algorithms are based on existing neural circuitry, yet the brain appears to shift its activity significantly in order to achieve the task.
Perhaps these shifts are simply compensating for inaccuracies in neural decoding. After all, almost any BMI researcher will admit the inadequacies of current decoding methods. Or, maybe it is easier for the brain to learn some new, artificial relation between movement and neural activity. My intuition is that the ideal BMI approach will probably lie somewhere in between the completely artificial and biomimetic. Further research at both extremes is still needed to answer these lingering questions. The incredible flexibility of our brains adds yet another level of complexity to the motor cortex and brain-machine interfaces.
[Fetz Journal of Physiology 2007 is an excellent review of neural volitional control research and its applications to brain-machine interfaces. I highly recommend it to those interested in exploring this topic further.]
1.22.2009
Dealing With Instability
We encounter unexpected and unpredictable forces acting upon our bodies every day. Whether it’s a sudden bump on the subway or picking up an empty bottle you thought was full, we move in highly unpredictable environments. Through experience, we learn to anticipate some of these disturbances. For example, we estimate an object’s weight before we pick it up to properly counter the force. This process occurs almost imperceptibly—you probably only notice when your internal calculations are off and you’re suddenly raising that empty bottle above your head.
This educated-guess-and-check approach, however, will only work in predictable, stable situations. Many tasks are inherently unstable. Imagine using a screwdriver. If you don’t push perfectly straight, the screwdriver slips off of the screw head. You don’t know these interaction forces in advance, and you fail the task without compensating for them. Yet, humans use screwdrivers (and a host of other tools) every day without grievous injury. We’re impressively dexterous, all things considered.
How does our nervous system accomplish such an impressive feat? One obvious possibility is by using reflexes. By sensing unwanted or potentially harmful changes in our joint positions and quickly opposing them, we overcome unexpected forces to continue on our way. But these compensation mechanisms only go so far. As information travels from sensory receptors in muscles and joints, up to the spinal cord to compute the necessary motor response, and back out to the muscles, relay delays build up. For highly unstable environments, like using a screwdriver, reflexes aren’t enough. By the time they’ve kicked in, the screwdriver has slipped and is scratching your nice wood desk.
Etienne Burdet, Reiko Osu and colleagues explored how humans navigate highly unstable environments in their 2001 Nature paper (“The Central Nervous System Stabilizes Unstable Dynamics by Learning Optimal Impedance”). The group simulated screwdriver-like dynamics in the lab by applying an unstable force field to human subjects’ arms. They asked subjects to make center-out reaches (starting close to their body, and reaching straight ahead of them to a peripheral target) while holding onto the handle of a robotic device used to implement the force field. The researchers applied a divergent force field (DF), which enhanced movement errors perpendicular to the subjects’ movement. If the subjects reached perfectly straight, no forces were applied to their limb. If their hand drifted away from the straight path, however, the DF would expand these errors and push the subject’s hand away—just like the slip of a screwdriver.
The researchers asked subjects to make reaches with and without the force field. Initial reaches in the DF were unsuccessful; their hands were quickly pushed away. Over time, though, subjects were able to complete reaches in the DF despite its unpredictable dynamics. What had changed in their motor strategy?
Burdet et al hypothesized that limb impedance was a likely mechanism for overcoming this challenging DF. Though it is a familiar concept in electronics, impedance can be a bit difficult to grasp in the context of your limbs. Consider the difference between writing your signature, a well-practiced task that most of us are rather sloppy about, and printing on your tax forms, something that requires careful precision. One major difference between these two writing tasks is the impedance of your arm. In the former case, you’re very relaxed; in the latter, you’re more tense. Limb impedance describes the limb’s ability to resist external forces; a limb with a high impedance will move very little when pushed. So, to increase the accuracy of your writing, you ‘stiffen’ your arm, giving you more control. Furthermore, since our limbs move in a three-dimensional space, impedance is a 3D quantity. Your arm can be stiff in one direction, while remaining loose in others.
One way we can control our limb impedance is by contracting/relaxing agonist-antagonist muscle pairs. Our muscles are rather similar to springs; the shorter (i.e. more contracted) they are the more force it takes to change their length. By contracting muscles that have equal and opposite effects on the limb (e.g. your biceps that flex your elbow and triceps which extend them), the limb won’t move—equal and opposite forces—but the muscles will be shorter, and thus stiffer. The net effect is an increase in the stiffness of the limb. The interesting aspect of limb impedance is that it can be changed independent of your movement kinematics. I can reach for my coffee cup with a stiff or loose arm. The position and velocity of my joints will be the same in both cases, but the dynamics (my limb’s response to external forces) will be very different. This property of limb impedance makes it a likely candidate for dealing with unstable dynamics like the DF. By increasing the impedance of their arms, subjects can make their arms less sensitive to the perturbing forces of the DF while still making the same center-out reaches.
To test their hypothesis that impedance plays a key role in navigating unstable dynamics, Burdet et al measured subjects’ limb impedance during their normal reaches and those made after they’d learning to navigate the DF*. They found that, indeed, subjects’ arms were stiffer during movements in the DF compared to free movements. Interestingly, their limb impedance only increased significantly in the direction of the environmental instability (i.e. the direction that DF forces were applied). Since the DF only causes instability along one direction, changes in limb impedance along other axes are not necessary to successfully complete the task. The researchers showed that subjects were able to optimize their limb impedance to match the specific task requirements.
Stiffening your limb requires a lot of energy since it involves contracting many muscles. Your arm quickly tires after only a page or two of filling out tax forms for precisely this reason—your muscles are in over-drive. Considering its high metabolic cost, Burdet’s finding that subjects optimize limb impedance as much as possible isn’t shocking. The intrigue of this result is that the central nervous system appears to have incredibly precise control of limb impedance. Indeed, this group later replicated the experiment using DFs with other orientations (e.g. pushing the hand along a 35 degree angle relative to the direction of motion) and found that subjects’ limb stiffness again changed to closely match the DF instability [See D.W. Franklin et al Journal of Neuroscience 2007]. This precision impedance control requires manipulation of a myriad of muscle pairs. Given our complex limb geometry, this optimization is not trivial. The CNS solves this computationally intensive problem with apparent ease.
This research demonstrates one way our motor system deals with the unpredictability of the world. However, how the CNS implements such exacting control remains to be explored. Do spinal cord reflexes primarily govern impedance control, or is the motor cortex also involved? If the cortex plays a role, how is impedance represented and encoded? Our brains are solving problems that an engineer with several computers might be able to solve in a few hours in a matter of milliseconds. Its methods remain an exciting mystery.
* Measuring limb impedance is a non-trivial and rather technical problem, the details of which are not particularly interesting from a neuroscience perspective. As such, I’ve omitted them here. See Burdet et al Journal of Biomechanics 2000 for details of the stiffness measurement methods used in these experiments.
This educated-guess-and-check approach, however, will only work in predictable, stable situations. Many tasks are inherently unstable. Imagine using a screwdriver. If you don’t push perfectly straight, the screwdriver slips off of the screw head. You don’t know these interaction forces in advance, and you fail the task without compensating for them. Yet, humans use screwdrivers (and a host of other tools) every day without grievous injury. We’re impressively dexterous, all things considered.
How does our nervous system accomplish such an impressive feat? One obvious possibility is by using reflexes. By sensing unwanted or potentially harmful changes in our joint positions and quickly opposing them, we overcome unexpected forces to continue on our way. But these compensation mechanisms only go so far. As information travels from sensory receptors in muscles and joints, up to the spinal cord to compute the necessary motor response, and back out to the muscles, relay delays build up. For highly unstable environments, like using a screwdriver, reflexes aren’t enough. By the time they’ve kicked in, the screwdriver has slipped and is scratching your nice wood desk.
Etienne Burdet, Reiko Osu and colleagues explored how humans navigate highly unstable environments in their 2001 Nature paper (“The Central Nervous System Stabilizes Unstable Dynamics by Learning Optimal Impedance”). The group simulated screwdriver-like dynamics in the lab by applying an unstable force field to human subjects’ arms. They asked subjects to make center-out reaches (starting close to their body, and reaching straight ahead of them to a peripheral target) while holding onto the handle of a robotic device used to implement the force field. The researchers applied a divergent force field (DF), which enhanced movement errors perpendicular to the subjects’ movement. If the subjects reached perfectly straight, no forces were applied to their limb. If their hand drifted away from the straight path, however, the DF would expand these errors and push the subject’s hand away—just like the slip of a screwdriver.
The researchers asked subjects to make reaches with and without the force field. Initial reaches in the DF were unsuccessful; their hands were quickly pushed away. Over time, though, subjects were able to complete reaches in the DF despite its unpredictable dynamics. What had changed in their motor strategy?
Burdet et al hypothesized that limb impedance was a likely mechanism for overcoming this challenging DF. Though it is a familiar concept in electronics, impedance can be a bit difficult to grasp in the context of your limbs. Consider the difference between writing your signature, a well-practiced task that most of us are rather sloppy about, and printing on your tax forms, something that requires careful precision. One major difference between these two writing tasks is the impedance of your arm. In the former case, you’re very relaxed; in the latter, you’re more tense. Limb impedance describes the limb’s ability to resist external forces; a limb with a high impedance will move very little when pushed. So, to increase the accuracy of your writing, you ‘stiffen’ your arm, giving you more control. Furthermore, since our limbs move in a three-dimensional space, impedance is a 3D quantity. Your arm can be stiff in one direction, while remaining loose in others.
One way we can control our limb impedance is by contracting/relaxing agonist-antagonist muscle pairs. Our muscles are rather similar to springs; the shorter (i.e. more contracted) they are the more force it takes to change their length. By contracting muscles that have equal and opposite effects on the limb (e.g. your biceps that flex your elbow and triceps which extend them), the limb won’t move—equal and opposite forces—but the muscles will be shorter, and thus stiffer. The net effect is an increase in the stiffness of the limb. The interesting aspect of limb impedance is that it can be changed independent of your movement kinematics. I can reach for my coffee cup with a stiff or loose arm. The position and velocity of my joints will be the same in both cases, but the dynamics (my limb’s response to external forces) will be very different. This property of limb impedance makes it a likely candidate for dealing with unstable dynamics like the DF. By increasing the impedance of their arms, subjects can make their arms less sensitive to the perturbing forces of the DF while still making the same center-out reaches.
To test their hypothesis that impedance plays a key role in navigating unstable dynamics, Burdet et al measured subjects’ limb impedance during their normal reaches and those made after they’d learning to navigate the DF*. They found that, indeed, subjects’ arms were stiffer during movements in the DF compared to free movements. Interestingly, their limb impedance only increased significantly in the direction of the environmental instability (i.e. the direction that DF forces were applied). Since the DF only causes instability along one direction, changes in limb impedance along other axes are not necessary to successfully complete the task. The researchers showed that subjects were able to optimize their limb impedance to match the specific task requirements.
Stiffening your limb requires a lot of energy since it involves contracting many muscles. Your arm quickly tires after only a page or two of filling out tax forms for precisely this reason—your muscles are in over-drive. Considering its high metabolic cost, Burdet’s finding that subjects optimize limb impedance as much as possible isn’t shocking. The intrigue of this result is that the central nervous system appears to have incredibly precise control of limb impedance. Indeed, this group later replicated the experiment using DFs with other orientations (e.g. pushing the hand along a 35 degree angle relative to the direction of motion) and found that subjects’ limb stiffness again changed to closely match the DF instability [See D.W. Franklin et al Journal of Neuroscience 2007]. This precision impedance control requires manipulation of a myriad of muscle pairs. Given our complex limb geometry, this optimization is not trivial. The CNS solves this computationally intensive problem with apparent ease.
This research demonstrates one way our motor system deals with the unpredictability of the world. However, how the CNS implements such exacting control remains to be explored. Do spinal cord reflexes primarily govern impedance control, or is the motor cortex also involved? If the cortex plays a role, how is impedance represented and encoded? Our brains are solving problems that an engineer with several computers might be able to solve in a few hours in a matter of milliseconds. Its methods remain an exciting mystery.
* Measuring limb impedance is a non-trivial and rather technical problem, the details of which are not particularly interesting from a neuroscience perspective. As such, I’ve omitted them here. See Burdet et al Journal of Biomechanics 2000 for details of the stiffness measurement methods used in these experiments.
1.16.2009
Welcome, Brains!
I am a 2nd year graduate student in bioengineering, broadly interested in the neural basis of human motor control. My interests tend to lean towards neuroscience with potential medical and rehabilitative applications (e.g. brain-machine interfaces). But in general, I am fascinated by our central nervous system's ability to dexterously maneuver our ungainly bodies through ever-changing environments.
I shifted into bioengineering and neuroscience from a physics background, leaving me with a bit of catching up to do. The literature on the motor cortex and motor control, as with many areas in neuroscience research, is vast--and sometimes very discordant. The lack of solidified understanding is both exciting and daunting for a newcomer like me. I've started this blog as an effort to organize some of my thoughts on important scientific papers I read during my studies. Comments and dialogue with readers could also aid in the idea-digestion process. I plan to post one summary and discussion of a particular paper each week.
I also hope to use this blog to improve and expand my science writing skills. I've long had an interest in writing about science and explaining complex ideas to the general public. It is a non-trivial and very important skill, indeed. As such, each post will be aimed to a science-literate audience at large, rather than neuroscience and bioengineering researchers. If you find any ideas unclear, or have suggestions for improved explanations/examples, please consider these aspects open for discussion too.
I've titled this blog after one of my favorite philosophical (and somewhat silly) questions in neuroscience: Can we humans use our brains to reverse-engineer and fully understand the operations of our brains? This idea conjures up cartoon images of a scientist discovering The Truth of the brain's inner workings, and his brain promptly imploding.
While I don't plan to address this question, I invite you to engage your brain to explore the fascinating world of motor control and neuroscience with me.
I shifted into bioengineering and neuroscience from a physics background, leaving me with a bit of catching up to do. The literature on the motor cortex and motor control, as with many areas in neuroscience research, is vast--and sometimes very discordant. The lack of solidified understanding is both exciting and daunting for a newcomer like me. I've started this blog as an effort to organize some of my thoughts on important scientific papers I read during my studies. Comments and dialogue with readers could also aid in the idea-digestion process. I plan to post one summary and discussion of a particular paper each week.
I also hope to use this blog to improve and expand my science writing skills. I've long had an interest in writing about science and explaining complex ideas to the general public. It is a non-trivial and very important skill, indeed. As such, each post will be aimed to a science-literate audience at large, rather than neuroscience and bioengineering researchers. If you find any ideas unclear, or have suggestions for improved explanations/examples, please consider these aspects open for discussion too.
I've titled this blog after one of my favorite philosophical (and somewhat silly) questions in neuroscience: Can we humans use our brains to reverse-engineer and fully understand the operations of our brains? This idea conjures up cartoon images of a scientist discovering The Truth of the brain's inner workings, and his brain promptly imploding.
While I don't plan to address this question, I invite you to engage your brain to explore the fascinating world of motor control and neuroscience with me.
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