Tag Archives: neuroplasticity

A cocktail party in a dish: How neurons filter the chatter

Originally published on the PLOS Neuroscience Community

While dining with a friend at a noisy restaurant, you listen attentively to her entertaining account of last night’s date. Despite the cacophony flooding your auditory system, your brain remarkably filters your friend’s voice from the irrelevant conversations at neighboring tables. This “cocktail party effect,” the ability to attend to select input amidst a distracting background, has fascinated researchers since its characterization in the 1950’s. Although psychological and sensory models have offered insight into why human brains are so exquisitely equipped to perform this selective attention, researchers haven’t yet pinned down how neurons process mixed information to respond to the important and suppress the irrelevant. In their new paper published in PLOS Computational Biology, researchers from the University of Tokyo revealed that individual neurons learn to “tune in” to one input while ignoring others, offering an intriguing explanation for how rapid neural plasticity may give rise to the cocktail party effect.

Sending neurons mixed messages

Based on many earlier studies showing that neural networks can learn by changing their activity based on experience, the authors wondered whether neurons could also be trained to distinguish among sensory experiences. To test this idea, they recorded electrical activity from cultured rat cortical neurons. They electrically stimulated the neurons according to two stimulation patterns, to provide two unique hidden sources of input, simulating the cocktail party effect of hearing a mixture of voices. In some conditions both input patterns were activated, while in others one, the other, or neither input pattern was activated. They repeated variations of these stimulations for 100 trials in many samples to track how the neural responses changed over exposure to the stimuli.

Distinct patterns of neural activation simulate the cocktail party effect of hearing multiple speakers. (Isomura et al., 2015)

Distinct patterns of neural activation simulate the cocktail party effect of hearing multiple speakers. (Isomura et al., 2015)

Learning to discriminate

Over the course of training, neurons altered their likelihood of spiking to the input patterns. Roughly half of the neurons increased their response to one source and reduced their response to the other, while the other half increased responsiveness to the other source. A discrimination index used to measure preference for one input over the other showed that this bias increased across all electrodes over the training period. Even neurons exposed to the stimuli only briefly – trained on only a fraction of the trials – still demonstrated a response preference up to a day later, suggesting that neural learning occurred rapidly and was long-lasting. Although first author Takuya Isomura speculates that “this could last several days,” it’s not permanent. “We have confirmed that training with another stimulation pattern could overwrite the neural preference to the past source. That is, even cultures that have learned a pattern set could learn another one.”

Neurons increased their discrimination (DKLi) over the course of training when fully trained (red) and partially trained (white) but not when NMDA receptors were blocked (black). (Isomura et al., 2015)

Neurons increased their discrimination (DKLi) over the course of training when fully trained (red) and partially trained (white) but not when NMDA receptors were blocked (black). (Isomura et al., 2015)

But how, since biological systems can learn in various ways, did these cells so efficiently acquire and maintain this source bias? Blocking the cultures with an NMDA receptor antagonist largely prevented the neurons from developing an input preference, suggesting that learning occurred through NMDA receptor-dependent signaling, known to be important for long-term synaptic plasticity supporting memory formation. Furthermore, neurons only demonstrated discriminability if there was variance in the balance and frequency of the input patterns. This requirement for variance hinted that the neurons may follow independent components analysis (ICA)-like learning rules.

To better understand these learning dynamics, Isomura’s group examined changes at the neuronal population level. A simple Hebbian learning model predicted that connectivity should increase both within and across neuron groups. Instead, synaptic connectivity increased between neuron groups with the same source preference, but decreased between neuron groups with different source preferences. A modified model of Hebbian learning (including state-dependent plasticity) better accounted for these observations, as it allowed for competition between neurons. As Isomura explains, “state-dependent Hebbian plasticity could facilitate the neural response to the source that effectively stimulates the nearest electrode, while it could depress that to the other source. In the future, using the connection strength estimation, we might be able to predict the neural preference before the stimulations.”

As the neural networks changed, their internal and free energy decreased, whereas entropy increased. These energy changes did not occur with NMDA receptor blockade, suggesting that they are indeed attributable to learning-related synaptic plasticity. As connections strengthen between a neuronal group and its preferred source, the authors explain, mutual information increases between the neural system’s inputs and outputs, lowering its overall free energy.

How does a discriminating neuron make a discriminating brain?

Although it’s well established that neural activity changes with experience, Isomura and colleagues have shown for the first time that neurons can invoke these learning mechanisms to recognize and discriminate information. Neural networks accomplished this impressive feat by performing unsupervised learning – adhering to ICA and free-energy principles – to self-organize via activity-dependent plasticity.

So how might these findings help you stay engrossed in your friend’s tale of first date mishaps, amidst distraction? There are obvious differences between an integrated brain, which can direct its attention at will to a sound it deems meaningful and important, and a neuronal culture, which (presumably) lacks this power of guided attention. However, in both cases, a brain or neuron must decorrelate a mishmash of inputs. Although speculative, the authors propose that attentional shifts towards important information can only occur if the brain can distinguish sensory input, beginning at the level of discrimination by individual neurons. Further research will help to explain how feedback between attentional and sensory systems orchestrates this elegant goal-directed sensory filtering. Despite the sense that “tuning in” to a friend’s voice is automatic and effortless, studies have shown that this is a learned skill acquired early during life. Like other forms of learning, developing this ability likely relies on the plasticity of neurons adapting and responding to their experiences.

To Isomura, it’s “a fascinating mystery why people can learn faster than machine learning that typically needs huge training. Interestingly, some learning properties (e.g., speed) of culture networks are more similar to machine learning rather than human behavior, while they consist of living cells. Thus, a series of this kind of studies might have a potential to fill the gap.”


Bronkhorst AW (2015). The cocktail-party problem revisited: early processing and selection of multi-talker speech. Atten Percept Psychophys. 77(5):1465–1487. doi: 10.3758/s13414-015-0882-9

Cherry EC (1953). Some Experiments on the Recognition of Speech, with One and with Two Ears. J Acoustic Soc Amer. 25:975–979. doi: dx.doi.org/10.1121/1.1907229

Hohwy J (2014). The neural organ explains the mind. Open MIND. Frankfurt am Main: MIND Group

Isomura R, Kotani K, Jimbo Y (2015). Cultured Cortical Neurons Can Perform Blind Source Separation According to the Free-Energy Principle. PLOS Comp Biol. doi: 10.1371/journal.pcbi.1004643

Jimbo Y, Tateno T, Robinson HPC (1999). Simultaneous Induction of Pathway-Specific Potentiation and Depression in Networks of Cortical Neurons. Biophys J. 76(2):670–678. doi: 10.1016/S0006-3495(99)77234-6

Plude DJ, Enns JT, Brodeur D (1994). The development of selective attention: a life-span overview. Acta Psychol. 86(2-3):227–272

Tsien JZ, Huerta PT, Tonegawa S (1996). The essential role of hippocampal CA1 NMDA receptor-dependent synaptic plasticity in spatial memory. Cell. 87(7):1327–1338. doi: 10.1016/S0092-8674(00)81827-9

Image credit https://www.flickr.com/photos/dinnerseries

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How does Sports Training Restructure the Brain?

Originally published on the PLOS Neuroscience Community

The impact of regular exercise on the body is obvious. It improves cardiovascular fitness, increases strength and tones muscle. While these transformations are visible to the naked eye, changes to brain structure and function by physical activity occur behind the scenes and are therefore less understood. It’s not news that the brain is wonderfully plastic, dynamically reorganizing in response to every sensory, motor or cognitive experience. One might imagine therefore, that elite athletes–who train rigorously to perfect specialized movements–undergo robust neural adaptations that support, or reflect, their exceptional neuromuscular skills. Different sports, invoking different movements, will target unique neural substrates, but most physical activities similarly rely on regions that are key for eliciting, coordinating and controlling movement, such as the motor cortex, cerebellum and basal ganglia. In a new study published in Experimental Brain Research, Yu-Kai Chang and colleagues explored how microstructure in the basal ganglia reflects training and skill specialization of elite athletes.

Runners, martial artists and weekend warriors

The study enrolled groups of elite runners and elite martial artists, along with a control group of non-athletes who only engaged in occasional, casual exercise. Although both groups of athletes were highly trained (averaging over four hours of training daily), their uniquely specialized skills were key for determining whether basal ganglia structure varied by sport or by athletic training generally. The groups did not differ in terms of basic physical attributes, demographics or intelligence, but as expected, the athletes were more physically fit than the controls.

Measuring microstructure

The researchers focused on the basal ganglia, a set of nuclei comprising the caudate, putamen, globus pallidus, substantia nigra and subthalamic nuclei, since these structures serve critical roles in preparing for and executing movements and learning motor skills.

Structures of the basal ganglia

Structures of the basal ganglia

They used diffusion tensor imaging (DTI), which measures how water flows and diffuses within the brain. Since water diffusion is determined by neural features like axon density and myelination, it is more sensitive to finer-scale brain structure than traditional MRI approaches that measure the size or shape of brain regions. Fractional anisotropy (FA) and mean diffusivity (MD) are common metrics to assess, respectively, the directionality and amount of diffusion. Typically, higher FA and lower MD are thought to reflect higher integrity or greater organization of white matter.

Globus pallidus restructures in athletes

The basal ganglia microstructure of the athletes and controls were remarkably similar, with one exception. The internal globus pallidus showed lower FA and a trend for higher MD in the athletes than the non-athletes, but there were no differences between the runners and martial artists.

This result in intriguing for two reasons. First, it’s notable that both athletic groups showed a similar magnitude difference from non-athletes. Thus, acquiring and refining skilled movements more generally, rather than any particular movement pattern unique to running or martial arts, may restructure the globus pallidus. As study author Erik Chang explains,

“With the current results, we can only speculate that the experience of high intensity sport training, but not sport-specific factors, would trigger the localized changes in DTI indices we observed.”

This would make sense, considering the area is an important output pathway of the basal ganglia, broadly critical for learning and controlling movements. It’s likely that other regions may undergo more specialized adaptations to sport-specific training. Chang expects that future studies using a whole-brain approach with “distinctions between sport types and reasonable sample size would find cross-sectional differences or longitudinal changes in brain structure related to motor skill specialization.”

Second, although we expect athletic training to enhance regional brain structure, the reduced FA and increased MD observed in these elite athletes would commonly be considered signs of reduced white matter integrity. This is somewhat surprising in light of other studies reporting positive correlations between physical fitness and white matter integrity in non-professional athletes and children. But as Chang points out, “Professional sport experience is quite different from leisure training.” Although unexpected, this finding aligns well with similar reports that intensive training in dancersmusicians and multilinguals is associated with reduced gray or white matter volume or reduced FA. Why would this be? For starters, DTI doesn’t directly measure axonal integrity or myelination–only water diffusion. So while sports training has some clearly reorganizing effect on basal ganglia, we can’t yet infer what changes are occurring at the neuronal level. One interesting possibility is that the development of such expertise involves neuronal reorganization or pruning as circuits become more specialized and efficient. Chang cautions that their findings “could reflect the manifestation of an array of factors, including increased neural efficiency, altered cortical iron concentration in the elite athletes, or other training-specific/demographic variables.”

In the broader context, this study is a striking example of why care is warranted in interpreting neuroplasticity. Depending on the study conditions, the same intervention–here, athletic training–can apparently remodel the brain in opposing directions. This is an important reminder that although we like to assume that bigger is better in terms of brain structure, this is not always true, highlighting the need to more deeply explore exactly how and why these neural adaptations occur. Chang eagerly anticipates that future studies incorporating “HARDI (High-angular-resolution diffusion imaging) and Q-ball vector analysis, together with larger sample sizes and longitudinal design, will be very helpful in revealing finer microscopic structural differences among different types of elite athletes.”


Chang YK, Tsai JH, Wang CC and Chang EC (2015). Structural differences in basal ganglia of elite running versus martial arts athletes: a diffusion tensor imaging study. Exp Brain Res. doi: 10.1007/s00221-015-4293-x

Chaddock-Heyman L, et al. (2014). Aerobic fitness is associated with greater white matter integrity in children. Cortex. 54:179-89. doi: 10.1016/j.cortex.2014.02.014

Elmer S, Hänggi J and Jäncke L (2014). Processing demands upon cognitive, linguistic, and articulatory functions promote grey matter plasticity in the adult multilingual brain: Insights from simultaneous interpreters. Front Hum Neurosci. 8:584. doi: 10.3389/fnhum.2014.00584

Hänggi J, Koeneke S, Bezzola L and Jäncke L (2010). Structural neuroplasticity in the sensorimotor network of professional female ballet dancers. Hum Brain Mapp. 31(8):1196-206. doi:10.1002/hbm.20928

Imfeld A, et al. (2009). White matter plasticity in the corticospinal tract of musicians: a diffusion tensor imaging study. Neuroimage. 46(3):600-7. doi: 10.1016/j.neuroimage.2009.02.025

Tseng BY, et al. (2013). White matter integrity in physically fit older adults. Neuroimage. 82:510-6. doi: 10.1016/j.neuroimage.2013.06.011

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