Archive for the ‘neurons’ Category

030826 – Parallel processing in the mind

Tuesday, August 26th, 2003

030826 – Parallel processing in the mind

I don’t know if it originated with Grossberg, but I like the concept of complementary processing streams.  Actually, he talks about it as if it always involves a dichotomy.  Could it not also be multiple (any number) parallel streams?  Certainly, the convergence of inputs from a large number of brain areas on the amygdala indicates that it’s not just dichotomous streams.

Grossberg writes as if he is describing exactly what happens—especially with his neural circuit diagrams, but the more I read, the more they seem fanciful.  Certainly there’s something missing when the diagrams only show neurons in layers 2/3, 4, and 6.

In also seems that there’s something missing from the analysis of the visual “what” pathway.  Edge and Surface processing seem very closely tied throughout.  In visual area V1, the “blob” neurons are surrounded by “interblob” neurons and in visual area V2, the “thin stripe” the neurons alternate with the “interstripe” neurons.  Surely there is some crosstalk between (among) the channels.

Grossberg uses the term “catastrophic forgetting”.  He also talks about complementary channels of processing in the brain.  And he, among others, and talks about a “where” channel to the parietal lobe and a “what” channel to the temporal lobe.  Things then get a little confused.  Part of the point of “catastrophic forgetting” is, in effect that certain memories need to get overwritten, e.g., memories of where a particular movable object is located.  In contrast other memories should not be easily forgotten.

It is not clear that the categories “easily over writable” and “not easily over writable” (or should it be “things that change often” and “things that don’t often change”?) are the same as “where” and “what”.  It’s certainly possible from an evolutionary standpoint that what and where are sufficiently essential aspects of the environment that they should be per se ensconced in genetically determined neural structures.  Nonetheless, is reasonable to ask whether what evolution has provided is also being used in ways unrelated to its evolutionarily determined functionality.

Or alternatively, given that evolution has cobbled together mechanisms capable of recording information with differing degrees of environmental permanence, it seems reasonable to suppose that the same mechanism could show up in different places; although, I am well aware that the essentially opportunistic functioning of evolution leaves open the possibility that the same function is performed in many different ways.  Still, in our environment and the environment of our animal ancestors some things change rapidly and some things don’t.

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030730 – Synaptic plasticity (Hebbian learning)

Wednesday, July 30th, 2003

030730 – Synaptic plasticity in the brain

On average, whatever that might mean, each neuron in the brain is connected to about 10,000 other neurons via synapses.  “The human nervous system is able to perform complex visual tasks in time intervals as short as 150 milliseconds.  The pathway from the retina to higher areas in neocortex alone which a visual stimuli [sic] is processed consists of about 10 ‘processing stages’.  (Thomas Nachtschlager, December 1998.  “Networks of Spiking Neurons: A New Generation of Neural Network Models” http://www.cis.tugraz.at/igi/tnatschl/online/3rd_gen_eng/3rd_gen_eng.html).  “Experimental results indicate that some biological neural systems … use the exact timing of individual spikes,” that is, “the firing rate alone does not carry all the relevant information.”  That certainly makes sense in a system that has to deal with race conditions.

“Each cubic millimeter of cortical tissue contains about 105 neurons. This impressive number suggests that a description of neuronal dynamics in terms of an averaged population activity is more appropriate than a description on the single-neuron level. Furthermore, the cerebral cortex is huge. More precisely, the unfolded cerebral cortex of humans covers a surface of 2200-2400 cm2, but its thickness amounts on average to only 2.5-3.0 mm2. If we do not look too closely, the cerebral cortex can hence be treated as a continuous two-dimensional sheet of neurons.” (Wolfram Gerstner & Werner M. Kistler.  Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, 2002. Chap. 9.)

“Correlation-based learning is, as a whole, often called Hebbian learning. The Hebb rule (10.2) is a special case of a local learning rule because it only depends on pre- and postsynaptic firing rates and the present state wij of the synapse, i.e., information that is easily `available’ at the location of the synapse.

“Recent experiments have shown that the relative timing of pre- and postsynaptic spikes critically determines the amplitude and even the direction of changes of the synaptic efficacy. In order to account for these effects, learning rules on the level of individual spikes are formulated with a learning window that consists of two parts: If the presynaptic spike arrives before a postsynaptic output spike, the synaptic change is positive. If the timing is the other way round, the synaptic change is negative (Zhang et al., 1998; Markram et al., 1997; Bi and Poo, 1998,1999; Debanne et al., 1998). For some synapses, the learning window is reversed (Bell et al., 1997b), for others it contains only a single component (Egger et al., 1999).

“Hebbian learning is considered to be a major principle of neuronal organization during development. The first modeling studies of cortical organization development (Willshaw and von der Malsburg, 1976; Swindale, 1982) have incited a long line of research, e.g., Linsker (1986b); Obermayer et al. (1992); Linsker (1986a); Kohonen (1984); Miller et al. (1989); MacKay and Miller (1990); Linsker (1986c). Most of these models use in some way or another an unsupervised correlation-based learning rule similar to the general Hebb rule of Eq. (10.2); see Erwin et al. (1995) for a recent review.”

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