21–30 of 32 posts
Can it feel pain? I know pain is kind of tricky to evaluate in biological insects as well but that's a discussion we will have to have at some point.
I don't think simulated brains can feel pain any more than a float variable "current_pain_level" set to 100 can "feel pain".
I don't think we have the kind of understanding of what it means to feel anything for there to be any meaningful, informed opinions on that topic.
Earlier quoted context omitted.
I don't think simulated brains can feel pain any more than a float variable "current_pain_level" set to 100 can "feel pain".
> I don't think simulated brains can feel pain I'm not sure why you say that. Does pain have some property that makes it impossible to simulate? If an organic animal can feel pain and it is possible to create a 100% accurate computational simulation of that animal, then the simulated animal must also experience pain. To me the interesting questions are at what level of simulation fidelity This whole issue of simulate…
“Opponents replied that when you modeled a hurricane, nobody got wet. When you modeled a fusion power plant, no energy was produced. When you modeled digestion and metabolism, no nutrients were consumed – no real digestion took place. So, when you modeled the human brain, why should you expect real thought to occur?” ― Greg Egan, Permutation City
Earlier quoted context omitted.
I don't think simulated brains can feel pain any more than a float variable "current_pain_level" set to 100 can "feel pain".
> I don't think simulated brains can feel pain I'm not sure why you say that. Does pain have some property that makes it impossible to simulate? If an organic animal can feel pain and it is possible to create a 100% accurate computational simulation of that animal, then the simulated animal must also experience pain. To me the interesting questions are at what level of simulation fidelity This whole issue of simulate…
Earlier quoted context omitted.
I don't think simulated brains can feel pain any more than a float variable "current_pain_level" set to 100 can "feel pain".
> I don't think simulated brains can feel pain I'm not sure why you say that. Does pain have some property that makes it impossible to simulate? If an organic animal can feel pain and it is possible to create a 100% accurate computational simulation of that animal, then the simulated animal must also experience pain. To me the interesting questions are at what level of simulation fidelity This whole issue of simulate…
Earlier quoted context omitted.
> I don't think simulated brains can feel pain I'm not sure why you say that. Does pain have some property that makes it impossible to simulate? If an organic animal can feel pain and it is possible to create a 100% accurate computational simulation of that animal, then the simulated animal must also experience pain. To me the interesting questions are at what level of simulation fidelity This whole issue of simulate…
To quote a book I hold in very high regard: “Opponents replied that when you modeled a hurricane, nobody got wet. When you modeled a fusion power plant, no energy was produced. When you modeled digestion and metabolism, no nutrients were consumed – no real digestion took place. So, when you modeled the human brain, why should you expect real thought to occur?” ― Greg Egan, Permutation City
Earlier quoted context omitted.
> I don't think simulated brains can feel pain I'm not sure why you say that. Does pain have some property that makes it impossible to simulate? If an organic animal can feel pain and it is possible to create a 100% accurate computational simulation of that animal, then the simulated animal must also experience pain. To me the interesting questions are at what level of simulation fidelity This whole issue of simulate…
To quote a book I hold in very high regard: “Opponents replied that when you modeled a hurricane, nobody got wet. When you modeled a fusion power plant, no energy was produced. When you modeled digestion and metabolism, no nutrients were consumed – no real digestion took place. So, when you modeled the human brain, why should you expect real thought to occur?” ― Greg Egan, Permutation City
The purpose of a hurricane isn't to make people wet, it's a by-product. If you wanted a simulation that made people wet then that could be arranged, either by modelling rainfall on simulated people or having a simulation that controlled physical water sprays/sprinklers suspended above physical people. I don't know what that would prove though.
And going back to Permutation City, (spolier) the whole premise of the book was that brain simulations did have real subjective experiences that were qualitatively indistinguishable from those that occur in "real" physical brains.
Can it feel pain? I know pain is kind of tricky to evaluate in biological insects as well but that's a discussion we will have to have at some point.
The headline is misleading, they built a neural network based on fly visual neurology, not a whole fly brain. And even if it were a full brain, effective societal consensus seems to be that insects don't have a right to humane treatment.
That seems generous, I think? They restricted the input data to visual acuity at the level of a fly. But it doesn't look like the neurology actually influenced the design much:
> This, combined with the discovery that the structure of their visual system looks a lot like a Deep Convolutional Network (DCN), led the team to ask: “can we model a fly brain that can identify individuals?”
Hard to tell without reading the paper.
Earlier quoted context omitted.
The headline is misleading, they built a neural network based on fly visual neurology, not a whole fly brain. And even if it were a full brain, effective societal consensus seems to be that insects don't have a right to humane treatment.
> based on fly visual neurology That seems generous, I think? They restricted the input data to visual acuity at the level of a fly. But it doesn't look like the neurology actually influenced the design much: > This, combined with the discovery that the structure of their visual system looks a lot like a Deep Convolutional Network (DCN), led the team to ask: “can we model a fly brain that can identify individuals?” H…
[0] https://journals.plos.org/plosone/article?id=10.1371/journal...
Earlier quoted context omitted.
> based on fly visual neurology That seems generous, I think? They restricted the input data to visual acuity at the level of a fly. But it doesn't look like the neurology actually influenced the design much: > This, combined with the discovery that the structure of their visual system looks a lot like a Deep Convolutional Network (DCN), led the team to ask: “can we model a fly brain that can identify individuals?” H…
Section 3[0] of the paper says that they modelled their NN based on the visual system connectome, albeit with several simplifications and omissions. [0] https://journals.plos.org/plosone/article?id=10.1371/journal...
> We implemented a virtual fly visual system using standard deep learning libraries (Keras). Our implementation uses approximately 25,000 artificial neurons, whereas Drosophila have approximately 60,000 neurons in each visual hemisphere [16]. We purposefully did not model neurons that are structurally suggestive to respond to movement, and therefore we were specifically limited to ‘modular’ neurons (with 1 neuron/column) throughout the medulla. The connections between neuronal types were extracted from published connectomes [17]. We imposed artificial hierarchy on our model eliminating self-connections between neuron ‘subtypes’ (i.e. no connections between L1 and L1, or L1 and L2), and while we allowed initial layers to feed into multiple downstream layers, we eliminated ‘upstream’ connections. The final lobula-like artificial neurons were modelled after Wu et al. [15], where the layers were ordered according to their axon penetration deeper into the system. Our ability to model Drosophila’s visual system is further limited to the connectivity, ignoring the sign (excitatory or inhibitory), as well as the neurons’ intrinsic membrane properties. The ability to create more biologically realistic simulations will increase once these properties are discovered and integrated into the connectome. The model is illustrated in Fig 2B, beside the biological inspiration (Fig 2C). S1 Table depicts a complete connection map and hierarchy, and S2 Table shows comparative performance of this model on a traditional image-classification dataset. Additional details are provided in S1 Methods.