Summary: Researchers investigate how neurons work together to help us make decisions.
Source: Santa Fe Institute.
How do we make decisions? Or rather, how do our neurons make decisions for us? Do individual neurons have a strong say or is the voice in the neural collective?
One way to think about this question is to ask how many of my neurons you would have to observe to read my mind. If you can predict I am about to say the word “grandma” by watching one of my neurons then we could say our decisions can be attributed to single, perhaps “very vocal,” neurons. In neuroscience such neurons are called “grandmother” neurons after it was proposed in the 1960’s that there may be single neurons that uniquely respond to complex and important percepts like a grandmother’s face.
On the other hand, if you can only read my mind by polling many of my neurons then it would appear the decision a collective one, distributed across hundreds, thousands, or even millions of neurons. A big debate in neuroscience is whether single-neuron encoding or distributed encoding is most relevant for understanding how the brain functions.
In fact, both may be right. In research recently published in Frontiers in Neuroscience, Bryan Daniels, Jessica Flack, and David Krakauer tackle this problem using data recorded from the neurons of a macaque monkey tasked by the experimenter with making a simple decision.
In an area of the brain involved in the decision-making process, Daniels and colleagues find that as the monkey initially processes the data, polling many neurons is required to get a good prediction of the monkey’s decision. Then, as the time for committing to a decision approaches, this pattern shifts. The neurons start to agree and eventually each one on its own is maximally predictive. Hence at first the “neural voice” is heterogeneous and collective, but as the neurons get close to the decision point, the “neural voice” becomes homogenous and, in a sense, individualistic, as any neuron on its own is sufficient to read the monkey’s mind.
Daniels says a possible explanation for this odd behavior is that the system has two tasks to solve. It must gather good information from noisy data and it must use this information to produce a coherent decision. To find regularities in the input it polls many individual neurons, as the crowd’s answer is more reliable than any single neuron’s when the data are noisy. But, as Krakauer says, ultimately a decision has to be made. The neurons agree on an answer by sharing their information to come to a consensus.
This explanation echoes results in other collective systems, from animal societies to systems studied in statistical physics. Flack says this commonality suggests a general principle of collective computation: It has two phases — an information accumulation phase that uses crowdsourcing to collect reliable information and a consensus phase that allows the system to act.
Source: Jenna Marshall – Santa Fe Institute
Image Source: NeuroscienceNews.com image is in the public domain.
Original Research: Full open access research for “Dual Coding Theory Explains Biphasic Collective Computation in Neural Decision-Making” by Bryan C. Daniels, Jessica C. Flack and David C. Krakauer in Frontiers in Neuroscience. Published online June 6 2017 doi:10.3389/fnins.2017.00313
Dual Coding Theory Explains Biphasic Collective Computation in Neural Decision-Making
A central question in cognitive neuroscience is how unitary, coherent decisions at the whole organism level can arise from the distributed behavior of a large population of neurons with only partially overlapping information. We address this issue by studying neural spiking behavior recorded from a multielectrode array with 169 channels during a visual motion direction discrimination task. It is well known that in this task there are two distinct phases in neural spiking behavior. Here we show Phase I is a distributed or incompressible phase in which uncertainty about the decision is substantially reduced by pooling information from many cells. Phase II is a redundant or compressible phase in which numerous single cells contain all the information present at the population level in Phase I, such that the firing behavior of a single cell is enough to predict the subject’s decision. Using an empirically grounded dynamical modeling framework, we show that in Phase I large cell populations with low redundancy produce a slow timescale of information aggregation through critical slowing down near a symmetry-breaking transition. Our model indicates that increasing collective amplification in Phase II leads naturally to a faster timescale of information pooling and consensus formation. Based on our results and others in the literature, we propose that a general feature of collective computation is a “coding duality” in which there are accumulation and consensus formation processes distinguished by different timescales.
“Dual Coding Theory Explains Biphasic Collective Computation in Neural Decision-Making” by Bryan C. Daniels, Jessica C. Flack and David C. Krakauer in Frontiers in Neuroscience. Published online June 6 2017 doi:10.3389/fnins.2017.00313