Wednesday, February 27, 2019

Smarter Parts Make Collective Systems Too Stubborn
By Jordana Cepelewicz
[[Waking up from naive "scientific" fantasies.....The bold font below is my addition. D.G.]] 
The poet John Donne immortalized the idea that “no man is an island,” but neither, it turns out, are most other species. Many natural and artificial systems are characterized by the collective, from neurons firing in sync and immune cells banding together, to schools of fish and flocks of birds moving in harmony, to new business models and robot designs operating in the absence of a single leader. “Collective systems are more the rule than the exception,” said Albert Kao, a researcher at the Santa Fe Institute who models decision-making in such systems.
Whether biological, technological, economic or social, collective systems are often considered to be “decentralized,” meaning that they lack a main control hub for coordinating their individual components. Instead, control is distributed among the components, which make their own decisions based on local information; complex behaviors arise through their interactions. That kind of setup can be advantageous, in part because it is resilient: If one part isn’t working properly, the system can continue to function — in stark contrast to when a central brain or leader stops doing its job.
Decentralization has ridden a wave of hype, particularly among those hoping to revolutionize marketplaces with blockchain technology and societies with more dispersed governments. “Some of this stems from political ideology having to do with a preference for bottom-up governing styles and systems with natural checks on the emergence of inequality,” Jessica Flack, an evolutionary biologist and complexity scientist at the Santa Fe Institute, wrote in an email. “And some of it stems from engineering biases … that are based on the assumption these types of structures are more robust, less exploitable.”

But “most of this discussion,” she added, “is naive.” The line between centralization and decentralization is often blurry, and deep questions about the flow and aggregation of information in these networks persist. Even the most basic and intuitive assumptions about them need more scrutiny, because emerging evidence suggests that making networks bigger and making their parts more sophisticated doesn’t always translate to better overall performance.
In a paper published earlier this month in Science Advances, for instance, a team led by Neil Johnson, now a physicist at George Washington University, demonstrated that a decentralized model performed best under Goldilocks conditions, when its parts were neither too simple nor too capable. That finding echoes other results from complexity research about the optimal use of information and the tradeoff between independence and correlation. The new insights could help to point out the strengths and limitations of decentralized designs for robots, self-driving vehicles, medical treatments and corporate structures — and might even help to explain aspects of natural evolution.
From the Marketplace to the Lab
Johnson’s investigation began as an attempt to understand the feedback loops in financial systems: Traders, each trying to maximize their profits while obeying certain rules, make decisions that contribute to a general outcome — say, some change in stock price — which in turn influences the traders’ subsequent decisions.
Then one day, while he was still on the faculty at the University of Miami, Johnson’s attention was caught by the work of a colleague toiling over something seemingly unrelated to Wall Street: the movements of fly larvae. A larva crawls automatically to positions that are neither too hot nor too cold, but it doesn’t rely on its brain to guide this journey. Instead, each of its body segments responds to signals from temperature-sensing neurons by compressing on one side or the other. The collective movement of all the segments causes the larva to turn. The resulting trajectories toward heat sources reminded Johnson of the financial models he worked with — so he decided to use them to search for principles common to all decentralized systems.
He and his team built a model that mimicked how the larvae behaved. Like the larva’s segments, the collection of agents in the model shared a common goal but had no way to communicate and coordinate their activities. Each agent chose repeatedly to move either left or right, based on whether past decisions had led the entire system to head closer to or farther away from a specified target. To guide its decision, each agent used a strategy drawn from a subset of possibilities assigned to it. If a given strategy performed well, the agent continued to use it; otherwise, it turned to a different one in its arsenal.
The researchers observed that when the agents could remember only one or two outcomes, fewer strategies were possible, so more agents responded in the same way. But because the agents’ actions were then too correlated, the collective movement in the model took it along a zigzagging route that involved many more steps than necessary to reach the target. Conversely, when the agents remembered seven or more past outcomes, they became too uncorrelated: They tended to stick with the same strategy for more rounds, treating a short string of recent negative outcomes as an exception rather than a trend. The model became less agile and more “stubborn,” according to Johnson.

In an experiment, the movements of decentralized systems hinged on the independent decisions of their components, which had memories of some past outcomes. After the memory size reached a certain threshold, the systems performed worse.
The trajectories were most efficient when the length of the agents’ memory was somewhere in the middle: for about five past events. This number grew slightly as the number of agents increased, but no matter how many agents the model used, there was always a sweet spot — an upper limit on how good their memory could get before the system started to perform poorly.
“It’s counterintuitive,” said Pedro Manrique, a postdoctoral associate at the University of Miami and a co-author of the Science Advances paper. “You would think that improving the sophistication level of the parts, in this case the memory, would improve and improve and improve the performance of the organism as a whole.”
A Second Wave
Kao sees a striking connection between Johnson’s and Manrique’s findings and his own work on the behavior of crowds. Over the past few years, he and others have found that medium-size groups of animals or humans are optimal for decision-making. That conclusion runs contrary to the standard beliefs about the “wisdom of crowds,” Kao said, “where the larger the group, the better the collective performance.” Success lies in achieving the right balance between coordination and independence among the system’s components.
“It’s like a second wave of this kind of research,” Kao said. “The first wave was naive enthusiasm for these collective systems. Now, it feels like … we’re questioning a lot of the assumptions we made initially and finding more complex behaviors.”
Further research is needed on how the sophistication of components, their interconnectivity and other parameters affect the overall robustness and limitations of a network. Johnson and others plan to study how the availability of information affects phenomena as diverse as the formation of opinions among voters, the behavior of better robots, and potential mechanisms of recovery from neurological diseases. They also hope the work could help explain why natural evolution has made organisms a mix of centralized and decentralized systems (these kinds of results, Johnson said, could help to justify “why we aren’t just fantastic larvae”).
Ultimately, complexity in nature isn’t simply a story of its emergence from entirely uncorrelated groups of dumb parts — and probing that story could one day yield more universal principles about cooperation, coordination and collective information processing.

Tuesday, February 26, 2019

Neuroscientists Say They've Found an Entirely New Form of Neural Communication

[[Just when some people think that everything is understood - sigh!]]

Scientists think they've identified a previously unknown form of neural communication that self-propagates across brain tissue, and can leap wirelessly from neurons in one section of brain tissue to another – even if they've been surgically severed.
The discovery offers some radical new insights about the way neurons might be talking to one another, via a mysterious process unrelated to conventionally understood mechanisms, such as synaptic transmissionaxonal transport, and gap junction connections.
"We don't know yet the 'So what?' part of this discovery entirely," says neural and biomedical engineer Dominique Durand from Case Western Reserve University.
"But we do know that this seems to be an entirely new form of communication in the brain, so we are very excited about this."
Before this, scientists already knew there was more to neural communication than the above-mentioned connections that have been studied in detail, such as synaptic transmission.
For example, researchers have been aware for decades that the brain exhibits slow waves of neural oscillations whose purpose we don't understand, but which appear in the cortex and hippocampus when we sleep, and so are hypothesised to play a part in memory consolidation.
"The functional relevance of this input‐ and output‐decoupled slow network rhythm remains a mystery," explains neuroscientist Clayton Dickinson from the University of Alberta, who wasn't involved in the new research but has discussed it in a perspective article.
"But [it's] one that will probably be solved by an elucidation of both the cellular and the inter‐cellular mechanisms giving rise to it in the first place."
To that end, Durand and his team investigated slow periodic activity in vitro, studying the brain waves in hippocampal slices extracted from decapitated mice.
What they found was that slow periodic activity can generate electric fields which in turn activate neighbouring cells, constituting a form of neural communication without chemical synaptic transmission or gap junctions.
"We've known about these waves for a long time, but no one knows their exact function and no one believed they could spontaneously propagate," Durand says.
"I've been studying the hippocampus, itself just one small part of the brain, for 40 years and it keeps surprising me." [[My emphasis.]]
This neural activity can actually be modulated - strengthened or blocked - by applying weak electrical fields and could be an analogue form of another cell communication method, called ephaptic coupling.
The team's most radical finding was that these electrical fields can activate neurons through a complete gap in severed brain tissue, when the two pieces remain in close physical proximity.
"To ensure that the slice was completely cut, the two pieces of tissue were separated and then rejoined while a clear gap was observed under the surgical microscope," the authors explain in their paper.
"The slow hippocampal periodic activity could indeed generate an event on the other side of a complete cut through the whole slice."
If you think that sounds freaky, you're not the only one. The review committee at The Journal of Physiology – in which the research has been published – insisted the experiments be completed again before agreeing to print the study.
Durand et al. dutifully complied, but sound pretty understanding of the cautiousness, all things considered, given the unprecedented weirdness of the observation they're reporting.
"It was a jaw-dropping moment," Durand says, "for us and for every scientist we told about this so far."
"But every experiment we've done since to test it has confirmed it so far."
It'll take a lot more research to figure out if this bizarre form of neural communication is taking place in human brains – let alone decoding what exact function it performs – but for now, we've got new science that's shocking in all kinds of ways, as Dickson adroitly observes.
"While it remains to be seen if the [findings] are relevant to spontaneous slow rhythms that occur in both cortical and hippocampal tissue in situ during sleep and sleep‐like states," Dickson writes, "they should probably (and quite literally) electrify the field."
The findings are reported in The Journal of Physiology.

Thursday, February 7, 2019

Against Karl Popper's method of falsification

Anyone who still takes Popper seriously must read this:

Why POPPER's logical negativism fails (2019) (Slides)