Comments on Nervous System and Behavior

COMMENTS ON NERVOUS SYSTEM AND BEHAVIOR Stephen Grossberg Department of Mathematics Massachusetts Institute of Technology Cambridge, Massachusetts 02139

INTRODUCTION At least three important themes are illustrated by the papers of this section: (1) There exist "command neurons" which can activate integrated behavioral sequences, or sequence fragments (e.g., Bittman and Raiciulescu, Clynes, McC. NcC. Brooks); (2) There exist significant interactions between specific and nonspecific neural systems. Some of these interactions subserve learning, and some energize specific motor performance or autonomic patterns that are compatible with fluctuating drive states (e.g., Gambarian et aI, al, Gopal, McC. NeC. Brooks, WolteHolterink)j and (3) There exist mechanisms that permit various species to change the controlling cues that guide their motor behavior if this behavior does not generate Endroezi). expected consequences (e.g., Endroczi). Below are sketched some formal neural networks that illustrate mechanisms of this type in a unified setting. These networks Fields. comprise the theory of Embedding Pields. They are derived from simple psychological facts taken as fundamental postulates. The goal is to find facts that correspond to basic principles of neural design. Given specific psychological postulates, netl.,rorks one derives the minimal neural networks that realize these postulates. These networks are capable of behavior that is far more subtle and complex than the postulates seem, at first broach, to imply. As new seen, netvlOrks bepostulates are imposed, the networks come increasingly realistic, both in their cone behaviorul capabilities, and in their physiological and anatomical design. CLASSICAL CONDITIONING The first stage of the theory analyses how a conditioned stimulus (CS, s~ch such as a ringing bell), by being paired with an unconditioned stimulus (UCS, such as food), can eventually elicit the unconditioned response (UCR, such as saliva) that the UCS characteristically elicits. The main postulates at this stage are the following: Postulate I: Presentations Induce Perturpresentat~on of a bations. How is the presentat~on given behaviorally indecomposable stimulus stimulUS represented in the network?

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Postulate 11: Distinguishing Order. How does the network learn that a given UCR follOWS a prescribed CS, and not some other follows UCR? Postulate Ill: Ill: Reproducing Order. How does the learned CS ~ UCR transition elicit the proper output in response to a given CS input? Postulate IV: Independence of Lists in s First Approxlmatlon. Approxlmation. How can short l~st i~sts be learned wIthout without massive response interference from all the behavioral units that are in the network's repetoire? These postulates generate neural networks capable of discriminating, learning, remembering, and performing arbitrarily complex sequences of events ([1] - [3]), and in particular provide examples of command neurons ([3], (4)). The simplpst class of networks will be defined below, and then used to illustrate some command structures. Let n xi(t) = -aixi(t) + ~ [xk(t-'T"ki) - rki)+t3kiZki(t) IkiJ+i3kizki(t) k=l n

t) - ~ [xk(t-crki ) -0 ki ]+ J+ Y ki + Ci ((t) k=l and

( 1)

Zjk(t) = = -.o.jkZjk(t) -"'jkZjl~(t) + €jk[Xj(t-'T" jk) -rjkt -Ijkt xk(t) (2)

",0) where i, jj,, k = 1,2, •.• ... ,n and [tr] ['!.] = max( m'lx(I",O) for any real number ~. Xi (t) denotes the stimulus trace (Short-term memory trace, average membrane potential) at time t of the cell body (popc;h tion) vi' and Zjk(t) (pop~l~tion) denotes the memory trace (long-term memory trace, associational strength, or excitatory transmitter production activity) at time t of the synaptic knob (population) N'k NO k that J fo'md at the end of the axon (population) is found a iXi in (1 e jk from Vj v j to v k '• The term --aiXi (1)) represents exponential de~ay de~ay of potential. The term [xk(t-'T"ki) - !ki) T"ki J t3i3 ki in (1) is proportional to the spiking frequency released into e ki in the time interval

'iki + dt]. rlki [t - 'iki' 'rki' t - 'rki ki is the spiking threshold, ~ki is proportional to the excitatory axonal connection strength from v to N , and 'r'i is the time required for ki ki k spikes to travel from v k to Nki • The term rk~l[xk(t rk~l[xk(t - 'r'iki) rki]+~iZki(t) in (1) is ki ) - Iki]+~iZki(t) the total excitatory input from other cells to vi at time t . At an excitatory synapse (~ki > 0), spiking frequency couples multiplicatively to transmitter zki(t) to release transmitter that perturbs xi(t), and all vi. such signals combine additively at vi' The term )"5"k~l [xk (t - 0ki) - rl('lki] + Yki is the total ki ]+Y inhibitory input from other cells to Vi vi at time t, with Yki the inhibitory axonal convi. The term nection strength from v k to Vi' Ci(t) is the experimental input (or stimulus) to vi at time t • In (2), (2) , the memory trace cross-correlates the presynaptic spiking frequency which reaches N. N' from v. at time t with the value Jk J xk(t) of average potential at v k at this time. Passive exponential decay of memory, due to the term -~·kz - ~'kz .k' 'k' can also occur. J J' Other decay laws have also been ana.lyzed Othe r [2],[4].

The simplest network of this type that can learn by classical conditioning is an outCS -a ctivated-cerl star ([3], [4]). Let one CS-activated-cerl (Cerl population) v l send equal signals Signals to its synaptic knobs (knob populations) N Nli li which abut the UCS-activated cells (cell populations) A = [vi: i = 2,3, ••• ,n} ,n } • Mathematical Mathemat ical analysis of the outstar reveals the following properties, among others [4]. v can learn and perform at B a spatial patt~rn' t~rni that tha t is a UCS input to P of the form Ci(t) Ci(tj = 9i C(t), C(t) , where 89i is the fixed, but otherwise arbitrary, relative pattern intensity at Vi and C(t) is i s the total pattern intensity, \Vhich 'vhich can fluctuate wildly in n time. In particular, 8i ~ Jk~2 89k = 1~ 0 and ?k=2 l. The relative memory trace n zlk )-1 is attracted toward 2 li = zli(~k=2 211 ("encodes") the pattern weight 89 at a rate i that depends on CS and UCS input rate, intensity, relative timing, and related factors. The sizes of the absolute memory traces zli also depend on these factors.

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The relative memory traces 2Z = (2 (Z12,Z13' 12 ,2 13 , ••• ,2 ) are attracted toward the pattern 'Zln) 1n weights 89 == (8 (9 2 ,8 ,9 , ••• ,8 ,9 n ) only at times 3 when the synaptic knobs N receive CSNli li activated spikes from v l • This is the property of "stimulus sampling" in an outstar: v samples the patterns playing on A A l by emitting signals at prescribed times. The relative memory traces Z, 2, which form a probability distribution at each time t, are the "stimulus sampling probabilities" of an outstar [3 ]. Whenever v l samples B, A, the memory traces in its synaptic knobs begin to learn the spatial pattern playing on 8A at this time. If a sequence of patterns (that is, a space-time pattern) plays on P while v l is sampling, then vl's synaptic knobs learn a weighted average of all the patterns, rather than any single spatial pattern. Thus if an outstar samples B A while a long sequence of spatial patterns reaches A, then after sampling terminates, the sam8, pling probabilities 2Z can be different from anyone of the spatial patterns. On recall trials, a CS input to v l creates equal signals in the axons e • These signals flow li down to the Nli • In Nli , the signal interacts with the memory trace zli to reproduce at the cell Vi an output proportional to 2Zli. 1i • In this way, recall trials reproduce at 8A the weighted average of sampled patterns that was encoded on learning trials. Thus the unit of long term memory is a spatial pattern. Also, the property of stimulus sampling permits a given spatial pattern to be sampled by a cell only ",hen when the knobs of the cell are activated. These properties can be used to synthesize synthes ize the co~~and cell, or avalanche. simplest co~~and An avalanche is a cell which can encode and ritualistically perform an arbitrary spacetime pattern. patte rn.

Let v have a long axon. Suppose that at l regular intervals along the axon, clusters

can be derived from further, but still simple, postulates postUlates about classical conditioning. These postulates postUlates are the following. Postulate V: Practice makes perfect. Postulate VI: The time lags between CS cs and UCS on successive learning trials can differ. Postulate VII: After learning has occurred, the UCR can be elicited by the CS alone on recall trials. Postulate VIII: A given CS cs can be conditioned to any of several drives (for example, bell ~ salivation if the ues ues is is food, or bell ~ fear if the ues UC S is js a shock). Postulate IX: Amount and/or rate of responding is influenced by the state of deprivation.

of axon collaterals are sent out to the = [vi: (vi: i€I}, where UCS-activated cells A = I is a finite set of indices. Let an arbitrary nonnegative continuous vector = (Ci(t) (C i (t) : i€I) iEr) perturb A function J(t) = while a brief signal is emitted by vI VI • As this signal sequentially activates the axon collateral clust~rs, clust~rs, they sequentially sample the weights (ei(t) (9 i (t) : i€I), where 9i (t) = = Ci(t) Ci(t)[rk€ICk(t)]-l. ei(t) [rk€ICk(t))-I. Since the sampling signal is brief, each cluster samples weights Heights which are approximately constant during the sampling interval. Thus, the avalanche samples a space-time pattern as a sequence of spatial patterns. Performance by an avalanche is ritualistic; once VI fires, the entire space-time pattern has to be performed, even if more important environmental demands occur during performance. To prevent this, more important demands must be able to prevent the activation of the clusters clust ers further down the avalanche axon. A minimal way to do this is the following. Let each cluster be fed by a different cell v l excite v 2 ' which body. Let cell body VI excites v ' and so on until cell vvm m is 3 reached. Denote the set [V (V : j =I,2, =1,2, ••• ,m} j by (7. Suppose that each v j ' j = 2, ... ,rn, can fire only if it receives an input from no~specific input that is dev. 1I plus a nonspecific .JJlivered to all the Vi' Suppos e also that Vi' Suppose u v l can only fire if it receives a CS input VI plus a nonspecific nonspecifi c input. input . Let the source inpu t be a cell (popuof the nonspecific input lation ) W. H. Withdrawal Hithdrawal of arousal from W H lation) can now stop sequential performance by the avalanche [3]. Hhat Hha t environmental events are important enough to cause withdrawal s tage of arousal? This question leads to Stage two of the theory.
INSTRUHENTAL CONDITIONING INSTRill·ffiNTAL punisl'unents, drives, and How do rewards, punisD~ents, motivations influence learning and performance? Some mechanisms for these concepts

eonsider Consider the typical situation in which a spatial pattern to be learned is embedded embe dded in a space-time pattern presented to P, and the space-time pattern can be different on successive learning trials. Alternatively, one could let the ues ues be the space-time pattern, and could consider the problem of learning a particular spatial pattern of the ues UCS perfectly by practicing the ues UCS several times. How is a particular event event s picked out as sigin a stream of events nificant and learned? To simplify our notation, we suppose that the same spacetime pattern is presented on each trial. 9(1), e(2), 9(2), Thus, on each trial a sequence e(l), 9(3) , ••• , e(N) 9(N) of spatial patterns is the e(3) ues delivered to P, i = 1,2, .•. .•• , N. In this thi s ues situation, an outstar anatomy does not suffice to achieve Postulate V if Postulate VI also holds; that is, a given sampling cell 0 cannot learn a definite spatial Vi in ~ p~ttern e(m) 9 (m) chosen from the ues UCS sequence p~ttern CS alone can fire v. on successive if the es J learning trials. To see this, consider sampling by vVjj of e(l) 9(1) for definiteness. briefl y v. can learn e(l) only if v. fires briefly 9(1) on aJfixed time before the ons~t ~~ e(l) every trial, and if the signals from v. v. 9 (1) plays on B. ThJis reach 0r.> only when e(l)plays Th.Jis

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will not happen if the CS alone can fire v. while Postulate VI holds, since signals J

from vVj will reach p on successive trials j while spatial patterns a(k) other than a(m) play on P.

Thus the stimulus sampling pro-

Z. babili ties 2.

J

=

(Z .. : id) (2 i€I) will learn a Jl (k) rather

weighted average of the patterns 89 9(1). than 8(1).

To avoid noisy sampling, the outstar must be embedded in a larger network.

vVj must j

be prevented from firing unless it simultaneously receives a CS input and an input controlled by the ues vlhich which signals that the ues will arrive at p a fixed time interval later. This is accomplished in two steps: let the ues activate axons leading to v.

m

J

that deliver an input to vVj a fixed time j before the ues arrives at P; and set the V.IS common spiking threshold J. r.J of all v.ls J axon collaterals so high that v. can fire J only if it simultaneously receives large UCS-controlled inputs. es and UeS-controlled

Then, on every

trial, v. can fire and begin to sample the ~attern 8(1) R(l) as it arrives at R, if spatial ~attern also the CS has been presented.

trials, Postulate VII is invoked on recall trials. After learning has taken place, the CS alone can elicit performance on recall trials, Thus the CS alone can fire cells trials. But,,! cells can in "I on recall trials. But"l only fire if inputs along two axon paths converge simultaneously on them. The UCS ues is not available on recall trials to actipaths, Only the CS is vate one of these paths. Ho\'l does es-ues CS-UCS pairing on available. How learning trials enable the CS to gain control over the UCS ues ~ cl pathway on recall trials? This dilemma imposes the concept of "conditioned arousal", which can be specialized as "conditioned incentive motivation", Namely, CS - UCS motivation". ues pairing during learning trials allows the CS to gain control over the nonspecific arousal chan(i.e" by nel via Pavlovian conditioning (i.e., cross-correlating presynaptic spiking frequencies and postsynaptic potentials at suitable synaptic knobs). Conditioning of nonspecific arousal at these synaptic knobs takes place while specific motor patterns are learned in the "I ~ synaptic knobs. Consequently, on recall trials, the CS can activate two input channels: unconditioned specific inputs to "I and condi"!. tioned nonspecific arousal inputs to "I. At cells in "I where these two inputs converge, the cell potential can be driven above its spiking threshold. These cells ,,! ~ m can fire, yielding signals along "I ,,! ~ ~ synaptic axons which activate the "I knobs and reproduce at '!J? the patterns encoded in these knobs. In this way, a CS can acquire UCS ues properties, and thus aspects of higher-order conditioning emerge as a consequence of Postulates VI and VII.

The paper

[5] discusses an inhibitory mechanism that guarantees brief vVj outputs in response to

After a CS can activate the arousal pathway, it has UCS ues properties; it can serve as the UCS ues for a new CS in a later learning experiment. The transition from CS to UCS ues in these networks is effected by an alteration (not necessarily a strengtheningt) of extant pathways, rather than by the creation path\'lays. Thus both CS and UCS of new path\oJays. ues inpath\'lays puts are processed in parallel pathways ("path equivalence"), except possibly the primary UCS ues input (e.g., taste of food) on Hhich which a chain of conditioning experiments can be built. In particular, "higherorder" UCS ues inputs, as well as es inputs, ,,! • are delivered to "I

j

even prolonged es plus UCS ues inputs; sampling 9(2) occurs can therefore terminate before 8(2) Et e.t PR •

nehlOrk which can sample R All cells in the network UCS-activated axons, for the reasons receive UeS-activated given above. In other words, there exists a UeS-activated nonspecific arousal of esactivated sampling cells. These cells are polyvalent cells, or cells which are influenced by more than one modality, such as the sound of a bell (CS) and the smell of food (ues). (Ues). The polyvalent cells fire only if the sum of CS and ues inputs is sufficientThe cells 0(7 at which conditioning of arousal ly large. The paper [ 6] reviews physiotakes place are neither J cells nor ~ cells. logical data relevant to this concept. This is because the J cells must be aroused before they sample the activity of '!J? cells, Some suggestive terminology is now introand '!J? cell activation must await the onset duced by denoting sampling cells (l ~ generiof sampling -- and(!?us prior firing -- by cally by "I, for "sensory cells" or "sensory "I cannot be learned. ,,! cells, or else 89 representation", and sampled cells R by ", for "motor cells" or "motor representation". Similar arguments have been used to prove that at least two successive cell sites are This distinction, of course, has no absolute needed in each sensory representation. The significance since both (l ~ and R contribute to sensory and motor processing. It is none- first site receives the CS input and theretheless convenient. ~pon sends Signals signals to a and to the second LCpon site. The second site can fire to '!J? only if it also receives a feedback signal from a.

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The a cells are eventually interpreted as network analogs of hypothalamus, hippocampus, reticular formation, and related brain regions that are implicated in arousal and reinforcement tasks. At first, these analogs are at best rudimentary versions of their in vivo counterparts, but as the derivation continues, they become increasingly realistic ([5] - [8]). The main point for present purposes is that the seemingly innocent Postulates V, VI, and VII imply the existence of interactions between specific and nonspecific systems that include learning and the energizing of motor performance by arousal sources. Postulates VIII and IX clarify this latter point.

o

cells include drive-activated cells. For example, when a bell (CS) (CS ) is conditioned to elicit salivation (UCR), (UCR) , it activates the a cells corresponding to hunger. Now invoke Postulate VIII. Postulate Vln VIII directs us to further expand the minimal network to include several subsets of a~ cells, such that each subset subserves a different "drive". These (1 subsets can overlap if their correspor.ding corresponding drives are not mutually independent; cf., hunger and thirst. For convenience of representation, however, we draw dra\'i them as individual points. By Postulate VIII, a given sensory event can be conditioned to any of several drive cl cell in the contingencies. Thus, each cl minimal construction will send axons to several subsets of a cells. Each (1 subset, sUbset, in turn, sends axons nonspecifically to J~ cells; otherwise the several drives could arous al signals not control nonspecific arousal from a~ to ,I capacle of releasing signals in particular ,D ~ ~ pathways.

~ .1

and from internal drive sources can fire an a cell. Now (J ~ cells are also "sensory" cells, but their sensory inputs describe the internal state of the network rather than the external state of the world. The papers ([ 6] - [ 8]) develop those ideas and cite relevant data. Noteworthy is the possibility of learning to push a lever persistently to deliver electric shocks to a (consummatory) drive representation without reducing the internal drive input (no "drive reduction"), as Olds and his collaborators have reported [ 6 ] • Various psychological terms can be used to describe (7 They supply "incentive (1 cells. motivation" in support of learned l earned sensorymotor acts encoded in ~ ~ ~ They 7.': pathways. resemble the "amplifier" elements of Estes, the "Go" mechanism of Neal t·liller, Hiller, and the "Now Print" mechanism of Livingston ([9] [11]). ATTENTION, PREDICTION, AND DISCRIMINATION LEARNING LEARN ING

The above derivation can be continued to enable the network to pay attention to beha'lior that can those cues Nhj.ch Nhich control behavior ca.n achieve expected consequences [12]. These developments cannot be summarized in the present limited space, but three qualitaqual itative remarks can be made: (1) They require a careful analysis of how hON multidimensional sensory information can be processed in parallel channels without overloading the entire network. The crucial postulate here aims at preventing the spurious simultaneous di fferent drive presentation of cues with different significance from quickly quickl y cOlmterconditionPostulate IX I X imposes a new constraint on in~ the drive significance of each Cll.e. the firing of a~ cells. If an a~ cell can (2) They depend critically on the existe~ce existe~ce feedbac k loops within the netof internal feedback always fire in response to conditioned rec u rrent: There The re work. These loops are recurrent: arousal inputs from ~ cells alone, then an exist cells which receive inputs from other a~ cell can always elicit (say) hungercells to which they have sent signals. For specific motor activity, even if the netexample, one such loop passes from cue re,Fork work is not hungry, whenever food is prepresentations to drive representations. sented. This property would kill the netAt the latter, the dominant combi!1ation of At dorninar.t combination vlork. d ifficul ty is formally analogous work. The ~he difficulty inp~ts to allowing an ~ cell to fire in the absence conditioned reinforcer plus drive inputs dbac}-: channels assoof its CS input. Maladaptive a~ cell firing inhibits all the fee feedbac~ k ind can be easily prevented, prev ented, just of this kind ciated with other drives. The dominant as in the J~ cell case. In the ~ cell case, feedback strengthens the representations c l cell can fire to ~ only if it simulof compatible cues, which thereupon s~p­ s~p­ an cl representation s of other c~es. c ~es . taneously receives a nonspecific input from fro~ press the representations n~ and input . ~~equire equire a~d a s;ecific sensory input. Attention Atte~tion is hereby drawn to the compatible analogously that an n cell can fire only if cues. (3) They use rec recurren~ ur rent on-center it simultaneously receives re ceives a nonspecific off-surround anatomies having shunting in(e . g ., a conditioned conditione d inp'lt inp'l t input' from ,p ,P (e.g., teractions in a crucial way; that is, anfro~ ~ or a primary prinary liES input ) and a specfro~ UES input) p:Jpulation excites excite s atomies in Hhich a given population senso ry input. In tr.e the n cell case, .ific sensory itself and a~d inhibits other populations usir,g ~enso r y inpJt input is interpreted to be a the ~ensory passive membrane elements to build up the drive input whose source is within the netpopu18.tions [13]. These Tr.ese structures suppress populations in~ut indicates work. The size of this in~ut proce ss ing of input network noise, permit processing ve l of this drive in-the network the le level natterns at high intensities without saturre st r~ction on r through time. This restr~ction cell ation, store significant patterns in shortsp i k ing firing is achieved by setting the spiking term memory after contour-enhancing contour-enhanci~g them, ~ ~ .D cxons so high that onlv threshold of r; and regulate the total activity in the net~et­ the sum of sufficiently large inputs from' froi:\' work so that it does not fire cells whose

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of features that are stored in short term proper decision criteria for firing should memory [13], and varying incentive-motivadepend on inputs from more than one source tion feedback can change the pattern of fea(SUCh as conditioned reinforcer plus drive (such tures to which attention is drawn [12]. Aninputs). ---other example is the construction of posiTOWARDS THE FUTURE popu1ations tion codes, or fields of neuron populations that control the asymptotic position of motor The above networks are capable of sophisorgans in response to prescrived sensory inticated nonstationary predictions, includinc1udputs; e.g., eye saccade in response to a ing complex real-time motor reactions to fluctuating multidimensional input patterns. peripheral spot ([12], [13]). How are these fields activated by arousal? For example, These predictions are continually revised based on how well they generate expected con- how is the decision to reach out with the sequences. In other words, the networks can right hand vs. the left hand made in response to an eye fixated on an interesting object. spontaneously reprogram their internal comIs it so that once the position code for each mand structures based on how well these possibility is anatomically coded once and prescr.ibed goals. Sevstructures achieve prescr.1bed for all--in era1 parts to the problem of synthesizing al1--in pattern variables--then the deeral cision is effected by determining which such networks require further development. neuronal field will be aroused? Two of these are the following: (1) Sequential Short Term Memory Buffer: How are lists, REFERENCES REFERENCES or more complex sensory inputs presented 1. Grossberg, S. (1971). proceedin~s proceedin~s of the sequentia11y in time, transferred into a sequentially National Academy of Sciences, 6 , 828. short term memory store, where they interact with a hierarchically organized sensory fil2. Grossberg, S. (1972). In "Delay and ter and long term memory processes? How do Functional Differential Equations and decending commands from long term memory their Applications." (K. Schmitt, Ed.), storage and ascending sensory feedback and p. 121, New York: Academic. other sensory data interact during performance? Avalanches provide rUdimentary ex3. Grossberg, S. (1973). In "Progress in amples of such command structures, but one Theoretical Biology." (R. Rosen & F. Snell, Eds. needs networks whose command units code sub4. Grossberg, S. (1970). Studies in Applied sequences of events whose sampling strength Math., 49, 135. is modifiable by sensory feedback. (2) Factorization of Information Pattern and (197 0 ). J. of Theoret. 5. Grossberg, S. (1970). Ener y Arousal. The network un t 0 ong BioI., Z7, 29I. 291. erm memory s a spatial pattern, or the 6. Grossberg, S. (1971). J. of Theoret. relative input sizes that are sampled by a BioI., 33, 225. given population. The total input size governs such factors as learning rate. 7. Grossberg, S. (1972). Math Biosci., 15, Throughout the theory, such a division into 39. pattern and total activity prevails. In S . (1972). Math Math Biosci., 15, pattern discrimination, for example, one can 8. Grossberg, S. construct networks whose output cells ex253. ( say) color constancy [14]. [14 ]. These hibit (say) 9. Estes, W.K. (1969). In "Punishment and cells are sensitive to the pattern of waveAversive Behavior." (B.A. Campbell and lengths after various normalizations of R.M. Church, Eds.). New York: Appletonl uminance in the several input channels have luminance Century- Crofts. Century-Crofts. taken place. Such cells are insensitive to Miller, N. E. (1963). In "Nebraska Symluminance fluctuations. Another channel N.E. 10 . Miller, 10. (M. R. Jones, Ed.), posium on Motivation." (M.R. must carry this information. Thus a bifur6 5, University of Nebraska Press. cation of paths into "chromaticity" and p. 65, " luminosity" channels is effected. The "luminosity" (1967 ) . In "Neurosciences R.B. Livingston (1967). selectivity of the chromaticity channels is 11. Research (F. O. Symposium Summaries." (F.O. preserved further downstream in network proSchmitt, T. Melnechuk, G.C. Quarton, and cessing, and the luminosity channel contriG. Adelman, Eds.). Vol. 2, Cambridge: butes to arousal channels. Eventually the M.LT. Press. two types of channels again converge, to " I nternational decide when particular features (such as 12. Gros sberg, S. (1974). In "International Neurobiology ." (J.R. (J. R. Smythies, Review of Neurobiology." colors) should be able to sample new patterns or to perform old patterns. Achieving selecEd. ) tivity seems, in this case, to require a temS . (1973). Contour Enhance13 . Grossberg, S. porary elimination of information in some ment, Short-Term Memory, and Constancies channels: a channel exhibiting color conin Reverberating Neural Networks. Substancy eliminates luminosity information. mitted to J. of Math. Psych. Further study of the feedback loop between such channels is required especially aimed 14. 14 . Grossberg, S. (1972). Kybernetik, 10, 10 , 49. at the question: how do these interactions change the pattern of activity in a neuronal field of feature detectors. For example, varying arousal level can change the pattern

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