40 Hz RIP (Neil Todd )

Subject: 40 Hz RIP
From:    Neil Todd  <todd(at)HERA.PSY.MAN.AC.UK>
Date:    Fri, 23 May 1997 15:08:28 +0100

On May 23rd (May 19th) Peter Cariani wrote:- > Once we admit the possibility of time playing a role -- > there being functionally-significant temporal > (or spatial) microstructure to our inputs, > either through synchrony or through common time pattern -- > then much more flexible modes of association become > possible. Dear Peter This is exactly the point which I have been making for some time (see message of 21st) and is the basis for the model which I have developed (e.g. Todd (1996) Network: Computation in Neural Systems 7, 349-356.) and which I presented in Montreal last year (see proceedings of ICMPC96) at which you were present (I know that since I remember you gave me a bit of a hard time). This is also the basis of the difference I have had with the so called "oscillatory framework" which it seems to me is a case of putting the oscillator cart before the signal horse. The information for binding is already there is the common temporal structure of the inputs from receptive fields which are activated by a common source. Having a whole load of "oscillators" on top of this is, quite frankly, massively computationally redundent. I simply cannot believe that natural selection would come up with such a scheme. One important thing which many people seem to forget about the visual system is that if an image is actually stabilised against the retina then the image fades away within a few seconds, almost as if it were an after-image. This fading is prevented in normal vision by constant movements of the eyes. These movements are of three kinds: (a) saccadic movements rate every .2 - 1 sec disp. 20 deg (b) micro-saccades rate every 1 sec disp. 5-10 mins arc (c) micronystagmus rate 40-50 Hz disp >=1 min arc In the case of the micronystagmus the rate of 40-50 Hz is within the limits of temporal contrast senstivity (i.e. ability to detect flashing lights) and the displacement of >= 1 min of arc is within the limits of spatial contrast sensitivity (spatial frequency of a grid) both depending on luminance. The implication is that the pattern of activity in retinal and higher level neurones is likely to be modulated by these eye movements. Clearly then RFs which are stimulated by a single object are likely to have inputs with a common temporal structure, co-modulated by micronystagmus. This I would suggest is the origin of the so called 40 Hz oscillations. This could easily be tested since it would be luminance dependent. Those who are advocates of the "oscillatory framework" have searched in desperation for some evidence of "40 Hz oscillations" in the auditory system, but to no avail. The only stuff that is cited are evoked potentials which sometime appear to show signs of oscillation. However, these can be explained by the latency between the MGB and cortex, i.e. is a superimposed Pa response (Pa is a component of MLR). Remember all the evoke potential experiments are done with repetitive stimuli to get an average (approx. 512). As you say Peter, if you talk to vision people about this they are becoming increasingly sceptical about the Singer et al results, at least in part because it has been difficult to replicate. Perhaps now the 40 Hz oscillator business can be finally put out of its misery, but somehow I think it may require some more anaesthesia before it may finally rest in peace. However, the question then is, if binding is triggered by common temporal structure of inputs, (a) how is that temporal information represented in the cortex and (b) by what mechanism is this temporal information compared so that RFs with common inputs may pool togther. The modelling approximation solution that I have proposed is the following. (a) Temporal information is represented spatially in the form of kind of AM wavelet transform. Following the peripheral filter bank the brain-stem/mid-brain (ICC) level is represented in a highly simplified manner as an array of linear band-pass filters forming a 2D cochleotoptic/periodotopic map. Although this ignores the massive complexity of the cochlear nucleus, it is consistent the idea of ICC cells as coincidence detectors. The medial geniculate body (MGB) is modelled as 2D array of low-pass filters with a cut-off about 200 Hz. This again is a great simplification but approximately consistent with the physiological data showing a decrease in temporal resolution from thalamus to cortex. The outputs of the "MGB" filters are downsampled to 1,000 Hz and input to a simulation of monaural processes in the cortex which is modelled as an array of columns. A proportion of the cortical cells within a column are also modelled as linear band-pass filters. Overall, then, periodicity pitch is primarily associated with subcortical processing whereas time and rhythm are primarily associated with the which acts functionally as a three dimensional filter-bank: (i) cochlear approx. 30 Hz - 10000 Hz (timbre); (ii) ICC approx. 10 Hz - 1000 Hz (periodicity pitch); and (iii) cortex approx. 0.5 Hz - 100 Hz (time and rhythm). Although the temporal resolution of the cortex is less than pitch periods, both cochlea and periodicity pitch information are present in the cortical response, since both are spatially represented in the form of the image of the inferior colliculus. Note that the first two dimensions have been demonstrated by Gerald Langner. The third dimension is still somewhat speculative, but theoretically is very attractive. Since the time-constants of the cortex are relatively long, its response to an ICC input lasts long after the ICC input has ceased. The cortical response thus embodies a form of sensory memory. The ouput of this stage can be represented as a kind of AM spectrogram or wavelet transform. Clearly such an architecture is quite speculative. However, it does successfully predict the general shape of AM detecability detectability curve which shows two points of maximum sensitivity, one at about 3 Hz and another about 300 Hz (Kay, 1982). The model here provides a natural explanation in terms of two populations of cells which are tuned to different ranges. The model also provides an account of time discrimination . It has been well established that when making judgements about time intervals, that we have maximum sensitivity to time intervals of around 300 ms (Friberg and Sundberg, 1995). Sensitivity drops off fairly rapidly for intervals less than about 200 ms and similarly, but less steeply, for intervals greater than about 1000 ms. The model predicts this particular shape because of the distribution of BMFs of the cortical units. For fundamental periods shorter than about 300 ms, the harmonics become attenuated, thus reducing sensitivity, and for fundamental periods longer than about 1000 ms, the fundamental becomes attenuated, thus also reducing sensitivity. (b) RF inputs are compared with a cortical cross-correlation mechanism. As a first approximation one can model this as a simple product moment correlation between cortical "columns". This can be applied to both sequential and simultaneous grouping with some success, although more testing is required (currently underway). Certainly, it provides a very natural acount of grouping by common AM and why particular rates are more effective than others. For example, Yost and Sheft (1993) have shown that rates of 5 - 25 Hz are effective for segregation. The fact that typical AM rates coincide with cortical BMFs suuports the view that the cross-correlation mechanism may be cortical in origin and take its input from cortical AM sensitive cells. However, clearly such a product moment matrix is an abstraction. The next question is how may the nervous system carry out a form of cross-correlation. In the model I have proposed a simple hypothetical circuit which, at least is computationally effective. The basic idea of this circuit is that the cortex is organised into an array of columns receiving input from the thalamus. Each column represents the temporal pattern of its input "spatially" in the form of an AM spectrum. At any point in time it is possible for a column to compare its input with that of other columns by comparing the spatially distributed pattern of activity - effectively a temporal correlation (note the power spectrum in the fourier transform of the autocorrelation function) - via an extensive network of inhibitory and excitatory cortico-cortical connections. Those columns which have coherent inputs form a pooled representation. For details of this circuit see Todd 1996 above. In case you think this was conjured out of thin air, in fact the idea of a column as a processing unit was proposed by the late Sir John Eccles [Eccles, J.C. (1984) The cerebral neocortex: A theory of its operation. In E. Jones and A. Peters (Eds.) Cerebral Cortex. Vol. 2 Functional Properties of Cortical Cells. Plenum: New York. pp 1-32.]. There is fact some similarities between the architecture of the above circuit and that of some of the earlier temporal correlation models. Indeed, a linear modulation filter has a damped oscillatory impulse response. However, to reiterate my first point, in this model the signal is in the driving seat and the function of the modulation filters is to represent the temporal structure of the signal spatially in order to make a cross-correlation possible. A spatial/topographic representation makes as lot of sense from a neuronal connectivity point of view since it does away with the need for delay lines. By now I can tell that I might have caused a few more apoplexys. Cheers Neil Todd PS Since I mention Eccles, may I recommend his book "The evolution of the brain: Creation of the self" [1989, Routledge, London], particularly the chapter "Linguistic communication in hominid evolution" which Edward Burns may find of interest.

This message came from the mail archive
maintained by:
DAn Ellis <dpwe@ee.columbia.edu>
Electrical Engineering Dept., Columbia University