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There is a huge body of literature on axon growth cone guidance which will give you some insights into how the biology works. Unfortunately, incorporating it all into a model is probably going to make it unwieldy unless your express purpose is to model the physiology, which doesn't seem like the case.
Here are some references:
Hong K, Nishiyama M. (2010)...
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In my experience, the term "semantic knowledge" (or semantic memory or conceptual knowledge) is generally used to refer to knowledge of objects, word meanings, facts and people, without connection to any particular time or place. The neural basis of this kind of knowledge is more or less agreed to depend on a distributed network of cortical brain regions (e....
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Artificial neural networks (ANNs) are mathematical constructs, originally designed to approximate biological neurons. Each "neuron" is a relatively simple element --- for example, summing its inputs and applying a threshold to the result, to determine the output of that "neuron".
Several decades of research went into discovering how to build network ...
15
The standard complexity metric in theoretical computer science and machine learning, in particular in statistical learning theory, is the Vapnik–Chervonenkis (VC) dimension. It is of interest because it gives us a very good tool to measure the learning ability of a neural network (or any other statistical learner, in general).
A good introduction to the use ...
15
The widely quoted figure of "10% at a time" is actually overestimating simultaneous brain activity by up to an order of magnitude. As demonstrated by Lennie 2003 (Current Biology), the
number of neurons that can be substantially active concurrently
is possibly as small as 1% of the brain's neurons, due to the high metabolic cost of spiking. Generally, ...
14
Caenorhabditis elegans is probably not an ancestor to Humans. As found in Sponge proteins are more similar to those of Homo sapiens than to Caenorhabditis elegans, certain sponges were found to have more similar protein structures to humans than C. elegans suggesting the sponges are the ancestor.
For your second point, it depends what you mean. The actual, ...
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I don't know of any NN algorithms that match your definition entirely, and I have looked for them (previously and recently). Here are some papers that I think are close or in the direction that you are exploring.
Using theoretical models to analyze neural development (review)
An Instruction Language for Self-Construction in the Context of Neural Networks
...
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It's a local rule. All that it means is that the connection between two neurons gets stronger if you use that specific connection more. The specific connection (the synapse) must be used though; it doesn't apply to two random neurons that aren't connected that happen to fire at the same time.
Hebbian learning is generic term for outcome; there are ...
12
There are some important relationships between the c. elegans nervous system and the human nervous system that should be pointed out here:
Neurons in both animals communicate with each other via synapses that use special molecules called neurotransmitters to convey activity. All major neurotransmitters used in humans are also used in c. elegans (Glutamate, ...
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I'd like to add to Chuck's excellent answer; the computational approach is very well-represented in neuroscience, and actually involves a large number of very heterogeneous methods. Thus, a very different set of neuroscientists and examples have sprung to mind for me.
To my mind, the best single example of the utility of a computational approach to ...
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I'm studying computer science at KIT (Karlsruhe Institute of Technology, Germany) specializing in Machine Learning and a minor in Mathematics. I am not a biologist.
An artificial neural network is basically a mathematical function. It is built from simple functions which have parameters (numbers) which get adjusted (learned). One example of such a function ...
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There are many neuroscientists who use the techniques of advanced mathematics and statistics to analyze actual neural data for patterns.
George Gerstein, who is now retired, has been a pioneer in applying "particle" methods in analyzing neuronal interactions. The originator of the Gravity transform, he used this tool to untangle some of the stochastic ...
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Many parts of the fetus brain begin showing neural activity before the senses that feed them are sufficiently developed to provide actual sensory information. In other words, it is unlikely that spiking activity in the brain is initiated by the senses. Some of the cells that become sensory organs, however, often fire in very specific patterns similar to the ...
9
To my knowledge, with respect to the context of the question, the first neural-like model of computations capable of learning – or, for that matter, computational model of neural processing and learning – has been put forward in McCulloch/Pitts (1943), as is also acknowledged in some of the texts about Turing's unorganized machines (›A-/B-type neural ...
9
There is a passage in On intelligence about the differences between parallel processing in human versus computers :
From the dawn of the industrial revolution, people have viewed the
brain as some sort of machine. They knew there weren't gears and cogs
in the head, but it was the best metaphor they had. Somehow
information entered the brain and the ...
9
Humans actually exhibit both slow and fast learning and they have somewhat different properties. One distinction is between "declarative" memory (for example, facts like "tigers have stripes" or "Paris is the capital of France") and "procedural" learning (such as perceptuo-motor skills like riding a bike or playing a musical instrument). Declarative memory ...
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The use of neural-networks in the cognitive sciences has been around since Turing. However, many of the networks common in connectionism suffer from a lack of biological plausibility. Of these abstract ones, even the ones that try to capture some properties of biological neural networks only do some metaphorically. See for instance the limitations of cascade ...
8
A (probably incomplete) list of ongoing whole brain simulation projects can be found at http://www.artificialbrains.com/. However, based on the information reported on this site, it is sometimes hard do distinguish what already has been achieved and what is still in the planning stage. Nevertheless it gives a good overview to start with.
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The human visual processing system receives input from the eyes, and then passes it through a number of areas of the brain that break it down, process it in various different ways, recombine it, and break it down again several times. I'm assuming this question is only about the visual cortex, general theories about how information might be broken down for ...
8
Disclaimer: Quantifying the capacity of the human brain is quiet complex as you might imagine. And although in cognitive neuroscience we often compare the brain to computers this is not an exact comparison, in many ways the brain is far more complicated and encodes information in a very different way than the comparison of CPU processors and hard-drives. The ...
8
Answer
Yes, theoretically.
Now
According to my ongoing informal research, there are two sides of brain preservation innovation: 1) the preservation and mapping (building) the connectome; and 2) the reinstantiation of memories and/or creating consciousness from a connectome.
From http://www.brainpreservation.org/overview/:
[N]euroscience is now ...
8
I've read evidence for single-neuron, two-neuron, and larger loops/cycles throughout the cortex, including intralaminar, interlaminar, and interareal neural loops. But it would take me far too long to back that statement up.
Instead, I offer a list of papers to get you started. I've read all these papers, and they all provide evidence for neural loops in ...
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I think this recent paper fits your requirements. It considers biological plausibility by showing that the number of neurons required in the proposed method is within a reasonable size for the human brain, and dismisses a series of unreasonable models. Specifically, they create a neural network using the Neural Engineering Framework (NEF) and the Semantic ...
7
For the dentate gyrus, which is probably more closely analogous to a feedforward hidden layer in a memory network, here are some answers:
Axon and dendrite connectivity is essentially local and can probably be assumed to be initially random within that local region. That is, a neuron integrating into the DG at the midpoint (along the long hippocampal axis) ...
7
I think that your intuition about the lower "energy ratio" of spurious states explaining their greater susceptibility to unlearning might be correct.
In a Hopfield Network spurious states are activity patterns that have not been explicitly embedded in the synaptic matrix, but are nonetheless stable. They are in other words "unwanted" attractor states that, ...
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One way the biological plausibility of an artificial neural network could be assessed is to look at how much a neural network abstracts away from the behavior of real neurons.
For instance, it is common in psychology and machine learning to use a sigmoidal activation function to determine the output of a node. If biological plausibility is a concern, one ...
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In general, what you're looking for is a biologically plausible model of reinforcement learning and/or conditioning. I know of two publications in particular that address this.
The first is A Biologically Plausible Spiking Neuron Model of Fear Conditioning and the second is A Spiking Neural Integrator Model of the Adaptive Control of Action by the Medial ...
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My question is: are these all the rules?
No.
Some things you left out:
Plasticity (change)
Very-short-term synaptic "plasticity" (changes in synaptic strength); at least a few different forms of this (post-tetanic potentiation, short term synaptic depression, synaptic facilitation).
Very-short-term intrinsic "plasticity" (changes in spiking behavior): ...
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David Chalmers has argued against the thermostat view, suggesting that adaptation to the environment is not sufficient. John Searle also disagrees that the current state of machine learning is capable of consciousness on the grounds that information processing is not a sufficient criterion (public lecture, 2016). Both of these philosophers emphasize clarity ...
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