In <erwin.722718311 at trwacs> erwin at trwacs.fp.trw.com (Harry Erwin) writes:
>Reference: Y. Yao and W. J. Freeman, 1990. "Model of Biological Pattern
>Recognition with Spatially Chaotic Dynamics." Neural Networks, 3:153-170.
>I've been going through this paper in detail. It's very interesting. They
>provide a lot of insight into how a sensory process works.
>The mammalian olfactory system consists of three major subsystems: the
>olfactory bulb (OB), with the sensory receptors synapsing onto the mitral
>cells and periglomerular neurons of the OB nucleus in the glomeruli, and
>with granule cells interacting with the mitral cells to form neural
>oscillators; the prepyriform cortex (PC), which monitors breathing,
>sends reset signals to the OB, and reports via deep pyramidal cells to the
>external capsule; and the anterior olfactory nucleus (AON), which monitors
>the patterns presented by the OB and handles pattern-related interactions
>with the OB. In isolation, each subsystem is periodic, and it is the
>interaction of the subsystems that creates the chaos. The connections are
>low-speed, and serve as low-pass filters. The OB has the highest natural
>frequency, the AON is intermediate, and the PC is lowest.
>The function of the OB is to convert the signals from the sensory neurons
>into a stable pattern that the PC can recognize. It is organized as
>content addressible memory. It reports a basal state if there is no input,
>a chaotic state if there is an unrecognizable input, and a near-periodic
>state if the input matches a pattern it knows.
>The PC monitors the breathing cycle and sends "mute" signals to the
>granule cells of the OB to pull the system into a neutral "ready" state.
>It can inhibit the AON and reports the "object" defined by the OB pattern
>to the cerebral cortex.
>The AON conducts training for the OB. This it does by sending synthetic
>sensory data to the periglomerular neurons and resetting the OB with
>"mute" signals similar to those provided by the PC. This training process
>(what I previously called "downloading") is very interesting.
>First, as Desimone demonstrated in September, neurons report not just
>their conclusions, but also the evidence for those conclusions. This turns
>out to be important. The AON can pull the OB to a neutral state by
>afferent synapses onto the granule cells. Simultaneously, it can feed
>synthetic sensory data into the mitral cells (which the sensory neurons
>synapse onto as well). Where does it get the correlation between the
>sensory data and what the OB is reporting to it? The OB reports not just
>patterns, but the evidence for those patterns. Hence the AON can learn
>how to simulate any sensory input.
Could you please elaborate on your reference to Desimone? I don't catch what
you're referring to here. Also, I'm having difficulty following this on
account of some of the terms that you're using. "...not just their
conclusions, but also the evidence for those conclusions." What do you mean by
"evidence" here? By "conclusions" do you simply refer to the spatial pattern
of output activity across the olfactory bulb, or the waveforms of these output
signals, or both? Then how is the "evidence" distinct from the "conclusions"?
>The PC feeds the cortex further downstream with pattern data. It is also
>able to detect the neutral state and the novel object states. Since both
>it and the AON receive the sensory data associated with the novel object,
>the PC can command the AON to feed it back, simultanously pull the OB
>to a neutral state, and so train the OB to recognize the pattern. The PC
>correlates the pattern with the object it was trying to train the OB on,
>using the retained sensory data. (Remember, neurons are _good_ at learning
>patterns. Visual system neurons can retain hundreds of individual features
>on one presentation each. This seems to involve synaptic switching, rather
>than synaptic growth.) These patterns don't need to be learned permanently;
>it's good enough to retain them for a period of time and then discard them.
>Hence, the lack of invariance. The AON can instead use synthetic sensory
>data to find out what the OB currently reports for a given pattern of data.
I'm lost concerning this "synthetic sensory data." I catch your drift
that there's a top-down feedback influence from the AON to the OB; what I
don't follow is how exactly this allows the system to then discard learned
patterns. Are you saying that the learning at the OB is transient, but that
there is permanent retention in the AOR? The patterns are being learned, and
stored, somewhere here, right?
MFragass at Ucs.Indiana.Edu Insert witty saying here