45

It is not meaningful to talk about your brain processing something as 'right-side up"' or 'upside-down'. The 'images' in your brain are just collections of neural activations, and not actual pictures. Thus they cannot have an orientation. The only meaningful way to test your question is to try flipping the input the brain receives and seeing if it can cope. ...


28

The major neural models of consciousness at the moment roughly fall into two camps: cognitive and phenomenological. They are defined by controversy surrounding what types of experience qualify as concious. Cognitive models On the one hand there are strong cognitive models of consciousness, such as the one proposed by Stanislas Dehaene, where consciousness ...


21

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)...


20

I think part of what makes this question confusing is the use of expressions like "what the eye sees", "what the brain sees" and "what the frog's eye tells the frog's brain". Nobody sees anything except the experiencing subject. When one stops thinking that the brain (or some visual-system part of the brain) observes the image on the retina, then the ...


18

When performing certain tasks, people’s inferences approximate Bayesian inference to a remarkable degree. For example, when people receive both haptic and visual information about the size of an object, they combine this information in a manner that very closely resembles Bayesian inference, taking account of the uncertainties associated with the visual and ...


17

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....


15

Answering the question in the manner that you are asking for would require quite an exhaustive list. However, a fundamental concept in all of this is having a "leak" channel. NALCN is the only nonselective channel found in the 24-TM channel family and is equally permeable to Na+, K+, and Cs+. [1] The majority of the ions transported by the channel are ...


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 ...


13

One of Koch's collaborators, Francis Crick (yes, that Francis Crick, much later in his career), put forth an interesting theory with Koch that while perhaps is a bit far fetched, it's worth mentioning for sake of a slightly different perspective. Crick and Koch posited the claustrum (see diagram below) as one of the seats of consciousness in the brain. ...


13

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 ...


12

The fact that the image does not appears upside-down has to do with the way visual information is processed in the brain. In his book, Jeff Hawkins argues that the low-level visual features on the retina (being upside down, distorted, and changing rapidly) are lost in the process of forming invariant representation. And it's those representations that we ...


12

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 ...


12

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

This very much depends on what, exactly, you're trying to do. EEG measurements tend to be extremely reliable, but the inferences one may draw on mental state are not necessarily so. EEG-driven BCI overwhelmingly relies on machine learning to correctly classify signals into a finite number of categories and act upon them. Typically, you'll do something ...


11

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 ...


11

Very many references may easily be found with a Google search for "mathematical model memory". Probably the most classic and iconic reference is Atkinson and Shiffrin (1965), which is also described on Wikipedia. Its three components and their relationships are nicely encapsulated in this figure: Many other, lesser-known mathematical models of memory exist, ...


11

As you have already hinted at, the issue is controversial. I could leave it at that and say "no, there is no consensus", and it would be a true answer, but it wouldn't be satisfying, wouldn't it? Instead, I'll briefly define the topic, give a few examples, and then a few recent criticisms. My answer will be weighted somewhat towards "cognition" instead of "...


10

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 ...


10

Apparently your question is on backward masking, which means that the masker follows the stimulus (probe) in time. Backward masking generally occurs at higher levels, typically the cortex. In case of visual stimuli this can be the primary visual cortex, or V1 (Mace et al. 2005). Ongoing processing of the probe is then thought to be interfered with by the ...


10

Rao et al. 2014 claims to be the first demonstration of a brain-brain interface in humans, using EEG and TMS. Abstract We describe the first direct brain-to-brain interface in humans and present results from experiments involving six different subjects. Our non-invasive interface, demonstrated originally in August 2013, combines ...


10

As far as I know, it is not possible for a neuron to change which type of neurotransmitter it releases. However, it is the case that the neurotransmitter GABA changes from excitatory to inhibitory over the course of development. This is occurs because GABA activates Cl- (chloride) channels. The chloride concentration gradient across the cell membrane ...


9

Closely related to random firing: Neurotransmitter-filled vesicles are released not only en masse when a neuron fires but also individually at random intervals. Nobel laureate Bernard Katz, who studied NMJs, observed: In the absence of any form of stimulation, the end-plate region of the muscle fibre is not completely at rest, but displays electric ...


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 ...


8

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

Have you ever seen IBM's Watson? Watson is composed of a cluster of ninety IBM Power 750 servers, each of which uses a 3.5 GHz POWER7 eight core processor, with four threads per core. In total, the system has 2,880 POWER7 processor cores and has 16 terabytes of RAM. It must be kept in a (very) large refrigerated room. Watson is a question answering (QA) ...


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 ...


8

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 ...


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 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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