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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 ...
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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, ...
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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 ...
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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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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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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 ...
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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 ...
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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 ...
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Biological Plausibility of Back-Prop
No, the algorithm of back-prop (BP) isn't biologically plausible. However, there are other means which involve propagating the error through multiple layers of neurons in a feed-forward network which are biologically plausible. But before we evaluate these substitutions, let's review why back-prop isn't biologically ...
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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 ...
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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 identifying ...
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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 ...
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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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Wen & Chklovskii (2005) looked at exactly this question through a simulation study. They assumed that the segregation of white and gray matter was the the result of evolutionary pressure to maximize some aspect of connectivity. They tested the idea that simultaneously maximizing interconnectivity (neurons should be able to connect to all other neurons ...
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Far from being one single organ performing a single homogeneous function, the brain is actually several lobes, and each lobe is like a separate organ performing a dozen functions. Putting it in another way, the brain is not like a "thinking machine", it is more like a collection of computers, instrument panels of an aircraft, radars and sonars of a submarine,...
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Due to my newness to the field, I can only talk about comparisons of biological plausibility when discussing the Neural Engineering Framework (NEF) and functional modeling. What is missing from this answer is a purely bottom-up modelling perspective in the same vein as the Blue-Brain project, but I'll leave that to another user.
One of the claims driving ...
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In contrast to artificial neural networks, which are almost all feed-forward architectures, networks in biological brains are highly recurrent. In the networks of cortex, the majority of synaptic inputs a neuron receives come from nearby neurons in the same area of cortex (Binzegger et al. 2004). These are the "circular" connections that you are referring to....
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First off, you mention 'metals'. What is a metal?
In common speech, a metal is a shiny material that conducts electricity and heat well.
In physics, a metal is regarded as a substance capable of conducting electricity at zero Kelvin. Many elements and compounds become metallic under high pressures, for instance iodine. Reversely, the metal sodium ...
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After doing some additional research, I think the answer is yes. It just means using a fixed timestep for the continuous-time activation equation (as described here). Since this is a differential equation, implementing it in software requires implementing a numerical integration method. I recommend the Exponential Euler Method as a starting point, because it'...
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Maybe I just don't get it, but I see your question as confusing because:
1/ Your brain is capable of running multiple parallel processes. Actualy each one of tasks you've mentioned consists of number of processes that are done at the same time. Lots of your neurons and neuronal networks are being used at the very same moment.
2/ If you can do something ...
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First, let's take a look at the basic principles of NEF.
The first two principles (Representation and Computation) do seem analogous to trained ANN models. Additionally, with the hPES learning rule that I've described here, they seem to have the same learning (gradient-descent) capabilities.
Where the NEF differentiates itself the most from ANNs is when it ...
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It is very very important to note that in the brain, most neurons are receiving input from way more excitatory synapses than necessary to bring the neuron to threshold. The thing is that they are also receiving input from a huge number of inhibitory synapses as well. This means that it is not how many inputs that are active at a given time that determines ...
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While model neurons like the leaky integrate and fire may use a simplification in which the neuron forgets all previous information when it emits a spike, in a biological neuron, the synapse and the soma are relatively electrically isolated from each other, so the voltage activity of the action potential does not make the synapse "forget" the EPSP. Although ...
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You basically have 2 options:
Manually fire both neurons together that you want to pair - do this as many
times as needed to pair them. After learning, it should be
sufficient to fire only one neuron for the second neuron to fire as
desired.
Assign a starting weight to the connection between the
neurons such that firing one will trigger the other. This is
...
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A google search for neural network library will return many relevant pages, with neural network libraries written in several programming languages.
You could also look for tutorials on programming perceptrons which are among the most basic neural networks. This would teach you how to actually program the network from scratch, instead of using a pre-made ...
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