Altruism and Tribalism

There has always been a puzzle in evolutionary biology as to how altruism arose. On first flush, it would seem that sacrificing oneself for another would be detrimental to passing on genes that foster altruism. However, Darwin himself thought that altruism could arise if humans were organized into hostile tribes. From the Descent of Man he notes that the tribes that had more “courageous, sympathetic and faithful members who were always ready to…aid and defend each other… would spread and be victorious over other tribes.” A recent paper in Science by Samuel Bowles presents a calculation that supports Darwin’s hypothesis.

If this hypothesis is correct, then altruism required lethal hostility to flourish and survive. Our capacity for great acts of sacrifice and empathy may go hand in hand with our capacity for brutality and selfishness. It may be why a person can simultaneously be a racist and a humanist. It may also mean that the sectarian violence we are currently witnessing and have witnessed throughout history may be as part of being human as caring for an ailing neighbor or taking a bullet for a friend. Our propensity for kindness may go hand in hand with that of bigotry and violence. It may be that the more homogeneous we become, the less altruistic we may be. Perhaps there may be an important societal role for spectator sports. Cheering for the home team may give us that sense of tribalism and triumph that we need. Maybe, just maybe, hating that cross-town rival makes us kinder in the office and on the roads. What irony that would be.

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The Hopfield Hypothesis

In 2000, John Hopfield and Carlos Brody put out an interesting challenge to the neuroscience community. They came up with a neural network, constructed out of simple well known neural elements, that could do a simple speech recognition task. The network was robust to noise and the speed the sentences were spoken. They conducted some numerical experiments on the network and provided the “data” to anyone interested. People were encouraged to submit solutions for how the network worked and Jeff Hawkins of Palm Pilot fame kicked in a small prize for the best answer. The initial challenge with the mock data and the implementation details were published separately in PNAS. Our computational neuroscience journal club at Pitt worked on the problem for a few weeks. We came pretty close to getting the correct answer but missed one crucial element.

Hopfield wanted to present the model as a challenge to serve as an example that sometimes more data won’t help you understand a problem. I’ve extrapolated this thought into the statement that perhaps we already know all the neurophysiology we need to understand the brain but just haven’t put the pieces together in the right way yet. I call this the Hopfield Hypothesis. I think many neuroscientists believe that there are still many unknown physiological mechanisms that need to be discovered and so what we need are not more theories but more experiments and data. Even some theorists believe this notion. I personally know one very prominent computational neuroscientist who believes that there may be some mechanism that we have not yet discovered that is essential for understanding the brain.

Currently, I’m a proponent of the Hopfield Hypothesis. That is not to say I don’t think there will be mechanisms, and important ones at that, yet to be discovered. I’m sure this is true but I do think that much of how the brain functions could be understood with what we already know, namely that the brain is composed of populations of excitatory and inhibitory neurons with connections that obey synaptic plasticity rules such as long-term potentiation and spike-time dependent plasticity with adaptation mechanisms such as synaptic facilitation, synaptic depression, and spike frequency adaptation that operate on multiple time scales. Thus far, using these mechanisms we can construct models of working memory, synchronous neural firing, perceptual rivalry, decision making, and so forth. However, we still don’t have the big picture. My sense is that neural systems are highly scale dependent so as we begin to analyze and simulate larger and more complex networks, we will find new unexpected properties and get closer to figuring out the brain.

The Hopfield Hypothesis

In 2000, John Hopfield and Carlos Brody put out an interesting challenge to the neuroscience community. They came up with a neural network, constructed out of simple well known neural elements, that could do a simple speech recognition task. The network was robust to noise and the speed the sentences were spoken. They conducted some numerical experiments on the network and provided the “data” to anyone interested. People were encouraged to submit solutions for how the network worked and Jeff Hawkins of Palm Pilot fame kicked in a small prize for the best answer. The initial challenge with the mock data and the implementation details were published separately in PNAS. Our computational neuroscience journal club at Pitt worked on the problem for a few weeks. We came pretty close to getting the correct answer but missed one crucial element.

Hopfield wanted to present the model as a challenge to serve as an example that sometimes more data won’t help you understand a problem. I’ve extrapolated this thought into the statement that perhaps we already know all the neurophysiology we need to understand the brain but just haven’t put the pieces together in the right way yet. I call this the Hopfield Hypothesis. I think many neuroscientists believe that there are still many unknown physiological mechanisms that need to be discovered and so what we need are not more theories but more experiments and data. Even some theorists believe this notion. I personally know one very prominent computational neuroscientist who believes that there may be some mechanism that we have not yet discovered that is essential for understanding the brain.

Currently, I’m a proponent of the Hopfield Hypothesis. That is not to say I don’t think there will be mechanisms, and important ones at that, yet to be discovered. I’m sure this is true but I do think that much of how the brain functions could be understood with what we already know, namely that the brain is composed of populations of excitatory and inhibitory neurons with connections that obey synaptic plasticity rules such as long-term potentiation and spike-time dependent plasticity with adaptation mechanisms such as synaptic facilitation, synaptic depression, and spike frequency adaptation that operate on multiple time scales. Thus far, using these mechanisms we can construct models of working memory, synchronous neural firing, perceptual rivalry, decision making, and so forth. However, we still don’t have the big picture. My sense is that neural systems are highly scale dependent so as we begin to analyze and simulate larger and more complex networks, we will find new unexpected properties and get closer to figuring out the brain.