Evolving Artificial Mind and Brains

8/8/2019 Evolving Artificial Mind and Brains

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chapter 5

Evolving artifcial minds and brains

P Mndik, Mik Collins nd Alx Vsgin

W xli snionl onn by ddssing ow snions x-

lin inllign bvio mig b quid oug osss o Dwinin vo-luion. W sn suls o omu simulions o volvd nul nwok

onolls nd disuss similiy o simulions o l-wold xmls o

nul nwok onol o niml bvio. W gu ousing on simls

ss o volvd inllign bvio, in bo simuld nd l ognisms, vls

volvd snions mus y inomion bou us nvion-

mns nd u n do so only i i nul ss oily isomo-

i o nvionmnl ss. Fu, s inomionl nd isomoism l-

ions w kd by onn ibuions in olk-syologil nd

ogniiv sini xlnions o s inllign bvios.

1 Introduction

Many kinds o explanations o intelligent behavior make reerence to mental repre-

sentations, that is, they explain an organisms ability to behave intelligently in virtue

o an organisms having mental representations. Te existence o such explanations,

representational explanations or short, raises many questions, o which two willbe the ocus o this paper. Te rst is the question o whether representational expla-

nations o intelligent behavior are the best explanations o intelligent behavior or i

we might instead do better with explanations that make no reerence to mental rep-

resentations. We will argue that we can do no better than representational explana-

tions. Te second question that arises is the question o what representational expla-

nations are reerring to when they reer to representations. What arerepresentations?

We demand not just an account o what representations are, but additionally we

demand an account that explains how representations can be the sorts o things thathelp explain intelligent behavior. We will sketch such an account. Te goal o this

paper then, is twoold: to argue that we need representations to explain intelligent

@@ is il onins low soluion imgs.@@

Auo: ls suly ig soluion vsion


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Mnl Ss

behavior and to sketch an account o what sorts o things representations must be i

they are to explain intelligent behavior.

Svl oonns o snionl xlnions v buil i ss bysing wi simls xmls o inllign bvio nd ming o dm-

ons in su xmls, no snions o b ound nd us, no

snions nd b d o in od o xlin bvios nd. Tis

is sgy ollowd, o xml, by oboiiss nd iil inllign -

ss su s Books (1991) nd B (1990) in i gumns o os-

sibiliy o inllign wiou snion. W will mloy simil sgy

bu w will b dwing din onlusions. W will xmin som o simls

ss o inllign bvio nd dmons in s ss bvio

nd is bs xlind in ms o snions. Fu, ou oun o -snions will b ully lis nd duiv. o sy oun is lis is o

sy ibuions n uly insumnl wys o sking as i -

us d snions. I is insd o ik ou ss o us would b

indndnly o ou sking o m. o sy o ou oun i is du-

iv, w will b idniying snionl ss in wys sigowd-

ly xlibl in ms o ss o us nvous sysms nd lions bwn

i nul ss nd nvionmnl ss.

One way to examine the simplest examples o intelligent behavior is to examinethe simplest examples o organisms that behave intelligently. Tis strategy coners

the ollowing advantage. Te simpler the creature the easier it will be to keep track

o the creatures internal structures, the structures o the creatures environment, and

the relations between the two kinds o structure in virtue o which the ormer count

as representations o the latter. Further, dealing with extremely simple cases will al-

low or tractable computer simulations o creature behavior as well as simulations o

the evolutionary orces that contribute to the emergence o such behaviors.

Ou moiv o ing bou voluiony bkgound o simls og-

niiv bvios mgs om ollowing sumions. W sum, nd

unlikly lon in doing so, simls oms o inllign bvios

div. T is, inllign bvios, ls o simls viis, ovid

biologil bns o ognisms om m. W sum lso jus

s ws im in isoy o univs w no biologil o-

gnisms, ws im in isoy o univs w no ogn-

isms oming inllign bvios. Sin abiogenesis is m ing o

yosizd mgn o li om non-living m, w oin m apsycho-

genesis o o yosizd mgn o inllign om non-inllignsysms. Wn, in isoy o univs did biognsis nd syognsis

ou? No on knows, bu w doub syognsis dd biognsis.

Ty i oinidd o biognis oud s. Howv, l oion


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C . Evolving iil minds nd bins

siks us s mo lusibl o wo. Adding o ou gowing lis o sum-

ions, w u sum oblm o undsnding syognsis is

bs undsood in onx o n voluiony mwok. Tus w ld osk: W ssus lid o non-inllign ognisms yildd lis nd

simls oms o inllign? I mnl snions o undwi inlli-

gn bvio, n qusions o volvbiliy o inllign will b losly -

ld o qusions o volvbiliy o mnl snions.

W will kl ois o inllign, snion, nd voluion by x-

mining omu simulions o volvd div bvios. T simuld o-

gnisms, bvios, nd nvionmns will b siml noug o mk bl

qusions onning lions onsiu snion nd ols -

snions ly in div inllign bvios.T suu o s o is s ollows. Fis w will biy xmin

w ss in wi snions invokd o xlin inllign bv-

ios o umns nd non-umn nimls. T gol will no b o x

dniion o snion om s xmls bu insd o only no w ky

us o ols snions ly in su xlnions. Fomuling d-

niion o snion is gol o b ivd (o ls oximd) owd

nd o nd no suosiion o b md is ous. Following

xminion o s sml xlnions, w will dsib bsi inllignbvio o osiiv moxis nd iglig wys in wi oblm

moxis oss o ognisms n b solvd in viy o wys involving -

snions. Nx w dsib mmil nd omu modls o osiiv

moxis. T modls inomd by nuonomil nd nuoysiologil

d om l nimls. Finlly w disuss w oun o snion sms

bs suod by modls.

2 Mental representations in explanations o intelligent behavior

L us k bi look olk-syologil xlnion o i o inllign

bvio. Consid Gog. Gog is oning igo. Wy? W xln-

ion is vilbl o is ion? A olk-syologil xlnion will dv o

ollion o syologil ss joinly onsiu us o Gogs bv-

io. An xml ollion o su ss would inlud dsi, ion, nd

mmoy. On xlnion o Gogs bvio n would dv o Gogs

desire o dink som b, Gogs visulperception is igo inon o im, nd Gogs mmoy u som b in igo

dy bo.


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Mnl Ss

T w usul oins o no bou is xlnion. Fis, syo-

logil ss no individully suin o us bvio, bu mus in

on. A bli is b in on o you will onibu o using you omov owd i i ombind wi dsi o b nd will onibu o using

you o mov wy om i i ombind wi o b. Similly, dsi o

b will onibu o using you o mov owd i ombind wi bli

b lis d nd us you o mov in som o diion i ombind wi

som o bli. In summy, syologil ss onibu o uss o

bvio by ing in on.

A sond usul oin o no bou is so o xlnion is syo-

logil ss idnid in by i snionl onn nd in by

w iud son is king owd onn. In s o Gogsmmoy u som b in igo, snionl onn o

mmoy is Gog u som b in igo nd iud is on

o mmbing. Din ys o iud n b kn owd on nd sm

onn (.g. rememberingbuying b;planning on buying b) nd on nd

sm iud y n b kn owd din onns (.g. iving that

there is a beer in ront o me, iving that there is a slice o pizza in ront o me).

In summy, syologil ss uss o inllign bvios dmi o

disinion bwn i snionl onns nd iud is knowd os snionl onns.

A id usul oin o no bou s sos o xlnion is w n

mk ibuions o onn wiou xlii knowldg o w, in gnl, -

snionl onn is. W onsu su xlnions on y wiou know-

ing, o xml, w ig oy o onn is o vn ving oy o

onn in mind. W ln o xloi is in w ollows. W will sn livly

l ss o syni ognisms bv in wys xlinbl in ms o -

snionl ss nd w will do so bo oing dniion o w sn-

ions o w snionl onn is. Tis lvs on o miil invs-

igion w bs ouns o snion nd onn s oosd o

m mus b sld a priori bo su invsigions k l.

I is wo noing ow o snionl xlnion is no simly

som soy w ll ouslvs nd o susind by ou own (ossibly mis-

kn) viws o ouslvs. On wy o i ow o su xlnions is

o i m in onx o xlining bvios o non-umn ni-

mls. T liu is lld wi su xmls. W biy mnion jus w.

Consid imssiv s o mz lning xibid by s. A Mois wmz is lld wi w ndd oqu o obsu lom will o

n o s wiou ving o d w. Wn ld in mz o s

im, will xlo nd vnully nd lom. Wn is


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und o sing osiion, dos no xlooy sgy

bu insd swims sig o mmbd loion o lom. A-

nly, ul inus gind duing xloion w uilizd o om-u sig-lin o lom. T s bvio is us xlibl in

ms o syologil ss su s ions nd mmois nd omuions

o ov m. Mu mo dil n b givn, o b su, bu o now ou

min onn is only o ll s sos o xlnion o ds nion.

Mu mo dil onning, o insn, nul undinnings o -

ion, mmoy, nd omuion, will b sulid l. Gllisl (1990: 2) dsibs

no su xml:

Evy dy wo nuliss go ou o ond w som duks ovwiningnd sion mslvs bou 30 yds . E is sk o bd unks.

E dy ndomly osn on o nuliss ows unk vy s-

onds; o ows vy 10 sonds. A w dys xin wi is

dill, duks divid mslvs in ooion o owing s; wiin 1

minu ons o owing, wi s mny duks in on o

nulis ows wi o o. On dy, owv, slow

ow ows unks wi s big. A s duks disibu mslvs wo

o on in vo o s ow, bu wiin minus y dividd y-

y bwn wo oging s. Ducks and other oraging animals can

represent rates o return, the number o items per unit time multiplied by the average

size o an item.(msis ous)

In bo ss o s nd duks, ulim xlnion lld o is

going o qui mnion o som livly subl mnisms insid o ni-

mls snsiiv o ois o nvionmn. o g l o w

mig b lld o, ons wy in wi w would xlin, on on nd,

movmns o owd lom o duk owd bd nd,

on o nd, ok lling owd . T oks movmn is x-

lind by di l o undmnl o o nu onsius -

ion bwn siv msss o nd ok. Su di

l o undmnl o will no xlin s movmn o lom.

Tis is no o sy, o ous, soming non-ysil is nsiing bwn

nd lom. T is o ous ngy owing bwn wo

ims in wys ulimly xlin is bvio. Bu unlik s o

ok, nsn o ngy om lom o will only v n im

on s bvio inso s is bl o nsdu inomion id

by ngy ino od n b uilizd by inomion ossing m-nisms in is nl nvous sysm. Su mnisms will b bl o so ino-

mion in om o nodd mmois nd mk omisons bwn n-

odd mmois nd un snsoy inu o omu ous o ion owd


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gol s. Going ino u dil o ow nvous sysm o n niml

mig nod su inomion nd om su omuions n g qui

omlid. Bo oding i will b usul o un ou nion owd nv-ous sysms mu siml n os o vbs.

3 Modeling the simplest orms o intelligence

Chemotaxis directed movement in response to a chemical stimulus is one o the

simplest orms o organism behavior. It is an adaptive behavior as when, or exam-

ple, positive chemotaxis is used to move toward a ood source or negative chemo-

taxis is used to move away rom a toxin. Te underlying mechanisms o chemo-taxis are relatively well understood and amenable to modeling and simulation

(Mandik 2002, 2003, 2005). Chemotaxis is appropriate to regard as cognitive. As we

will argue below, it constitutes what Clark and oribio (1994) call a representation

hungry problem. o appreciate the inormational demands that chemotaxis places

upon an organism, it is useul to consider the problem in the abstract. Te central

problem that must be solved in chemotaxis is the navigation o a stimulus gradient,

and the most abstract characterization would be the same or other taxes such as

thermotaxis or phototaxis. o ocus on a simplied abstract case o positive photo-taxis, imagine a creature traversing a plane and utilizing a pair o light sensors one

on the lef and one on the right. Activity in each sensor is a unction o how much

light is alling on in it in such a way that the sensor closer to the source o light will

have a greater degree o activation. Tus, the dierence in the activity between the

two sensors encodes the location o the light source in a two-dimensional egocen-

tric space. Inormation encoded by the sensors can be relayed to and decoded by

motor systems responsible or steering the creature. For example, lef and right op-

posing muscles might have their activity be directly modulated by contralateral

sensors so that the greater contraction corresponds to the side with the greatest

sensor activity, thus steering the creature toward the light.

Consider now the problem o phototaxis as conronted by a creature with only

a single sensor. Te one-sensor creature will not be in a position to directly perceive

the direction o the light since activity in a single sensor does not dierentiate rom,

say, light being three eet to the lef or three eet to the right. O course, the creature

might try to exploit the act that the sensor is moving and make note o changes in

sensor activity over time, but such a strategy will be available only to creatures that

have some orm o memory. Exploiting the change o sensor activity will require ameans o comparing the current sensor activity to some past sensor activity.

No olk-syologil xlnion o ow umn would solv

oblm o on-snso xis. o imgin you in gdin i will do o


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imgin you lilly in og so dns wil you n sin ow

dns i is w you , you nno sin in wi diion og gs

mo dns nd in wi diion i gs lss dns. Howv, wlking o wil you noi og is mu lss dns n i ws viously. By om-

ing you un ion o lss dns og o you mmoy o mo dns

og gins bkgound o you knowldg you v bn wlking, i is

sonbl o you o in you moving ou o o gs onn-

ion. Convsly, i you un ion vls g onnion o

og n mmbd, i is sonbl o you o in you sould un ound

i you wn o g ou o og.

T svl oins w sould g om bov disussion. T s is

inomionl dmnds o on-snso moxis n b dily idom oin o viw o olk-syologil xlnion. T sm oin o viw

llows us o onsu ossibl soluions o oblm o on-snso moxis: A

u is bo bl o iv un onnion nd mmb

s onnion is us in osiion o mk n inn bou w o

k moving d o un in od o dsid loion in gdin.

On-snso moxis is omlisd by nul ognisms. On iu-

lly wll sudid xml is nmod wom Caenorhabditis Elegans (C. Ele-

gans). Dsi ving ou mosnsos, i in d nd i in il, good sons o bliv wom s on-snso, no ou-sn-

so, moxis (F & Loky 1999). Fis o, woms bl o

moxis vn wn i il snsos movd. Sond, wo snsos in

d oo los og o o b n ibl din bwn

iviy in o m in sons o lol onnion o n. Tid,

wn nviging mil gdins on ivly wo-dimnsionl su o

Pi dis, woms osiiond on i sids wi i o d snsos

oogonl o gdin. Fou, iil nul nwok onolls insid

by nuoysiology oC. Elegans wi only singl snso inu bl o

oxim l moxis bvios in simuld woms. Ts simulions

silly insing o xmin in som dil.

We next briey review work done in simulating C. Elegans chemotaxis in

Shawn Lockerys lab at the University o Oregon Institute o Neuroscience. In par-

ticular we ocus here on work reported in Ferre and Lockery (1999: 263277) and

Dunn et al. (2004). Ferre and Lockery construct a mathematical model o the con-

trol o C. Elegans whereby the time-derivative o the chemical concentration is

computed and used to modulate the turning rate o the worm in the gradient. Oneo our purposes in reviewing this work is to point out how it, at best, supplies only

a partial explanation o how the actual nervous systems o C. Elegans regulates

chemotaxis. Ferre and Lockery begin by constructing a model network that makes


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many simpliying assumptions about the neuroanatomy and neurophysiology o

the relevant circuits in C. Elegans. Tey hypothesize that the worm must assess the

gradient by computing the temporal derivative o concentration as it moves throughthe chemical environment and that the behavioral upshot o this assessment is that

the worm attempts to keep its head pointed up the gradient. Teir model network

consists o ve neurons whose various states o activation model voltage. Te single

sensory input has a state o activation that reects the local concentration o the

chemical attractant. wo output neurons model the voltages o dorsal and ventral

motor neurons whose relative voltages determine the worms neck angle. Te re-

maining three neurons are interneurons. Each o the ve neurons is connected to

every other neuron by both eed-orward and eedback connections thus making a

recurrent network. Ferre and Lockery optimized network parameters by using asimple simulated-annealing training algorithm to maximize a tness unction de-

ned in terms o the change o chemical concentration. Te optimized network

resulted in simulated worm behavior similar to that o real worms: oriented move-

ment up the gradient and persistent dwelling at the peak. However, Ferre and

Lockery point out that it is not obvious how the networks are eecting these behav-

iors: Simple inspection o the parameters does not necessarily lead to an intui-

tive understanding o how the network unctions, however, because the neural ar-

chitecture and optimization procedure ofen avor a distributed representation othe control algorithm. o derive an intuitive mathematical expression or this al-

gorithm they manipulated the analytic solution to the linear system o equations

that comprise their mathematical model. Te analytic solution or the linear recur-

rent network is an equation wherein the rate turning is equal to the sum o a turn-

ing bias and the cumulative eect o chemosensory input on the rate o turning.

Tis equation produces exactly the same response to chemosensory input as the

original optimized network. In order to urther improve our intuition about

chemotaxis control in this model, Ferre and Lockery produce a aylor expansion

o the equation in time-derivatives o the input. Te extracted rule or chemotaxis

control equates rate o turning with a sum whose rst term is a turning bias, the

second term is the zeroth time derivative o chemical concentration, the third term

is the rst order time derivative o chemical concentration, the ourth term is the

second order time derivative o chemical concentration, and so on. Next they com-

pared simulated behavior wherein only some o the terms are kept. With just the

turning bias and the zeroth order term, the resultant behavior was not chemotaxis

but instead just a circular motion around the starting position. Adding the rst

order term resulted in chemotaxis as did adding the rst and second order terms.Likewise adding the rst order but omitting the zeroth order term.

F nd Loky dsib i omlismn s ollows: Using nlyi-

l niqus om lin sysms oy, w xd omuionl uls


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dsib ow s lin nwoks onol moxis (F & Loky 1999:

276). Howv, w nd suln mmil descriptions unsisying inso-

s y do no onsiu explanations o ow nwoks moxis.And y do no onsiu xlnions bus oo lil s y bn sid bou

w undlying mnisms nd ow i is y unioning. Wn

w sy y do no suly oml oun o mnism, by m-

nism w innd i in sns o Cv (2001: 8): Mnisms ollions

o niis nd iviis ognizd in oduion o gul ngs om s

o s u ondiions o nis o minion ondiions (S lso Cv & Ddn

2001; Mm, Ddn & Cv 2000; Bl & Ridson 1993).

o g l o w w ink is sill missing, ll li disussion b-

wn din bwn wo-snso moxis nd on snso moxis.In s o wo-snso moxis, din in iviy bwn l

nd ig snsos n b sigowdly xloid by sing mnism

would guid niml ig u gdin. Fo xml, l nd ig sing

musls ould b onnd o snsos in su wy g iviy in

ig snso will sul in g onion in ig sing musl us

uning d o wom owd diion o gs onnion.

I woms d is oind dily in diion o gs onnion

n iviy in l nd ig snsos will b oximly qul s will b moun o onion in l nd ig sing musls, us king

wom on ous. In is dsiion o wo-snso s, w v ls

sk o w mnisms undlying moxis . W no in om-

bl osiion y wi F nd Lokys mmil dsiion. T

omuion ul lls us that im diviv o onnion is bing

omud, bu w no y in osiion o s how i is bing omud. W

know noug bou undlying mnisms o know is suin

inomion sn uon wi o omu im diviv, bus w know

mil onnion dd by snso is nging ov im s

wom movs oug nvionmn. Howv, w nd o know mo n

inomion is there. W nd o know ow inomion is encodednd

subsqunlyusedby ognism. As Akins (2001: 381) us simil oin:

Inomion is id by, bu no nodd in, signl is inomion is

vilbl onlyin theory. o sy inomion is presentis o sy only

xiss omubl union wi, i usd, would yild o sul

I is sn, s i w, om oin o viw o univs. Bu no u s

v d uon inomion is vilbl only in inil.

Lockery and his colleagues are not blind to this sort o shortcoming. In a subse-

quent publication Dunn et al. (2004: 138) write Te chemosensory neurons re-


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sponsible or the input representation are known as are the premotor interneu-

rons or turning behavior Much less is known about the interneurons that link

chemosensory input to behavioral output. o get a urther handle on what the in-terneurons might be doing, Dunn et al. run simulations o networks optimized or

chemotaxis. Te networks in these simulations have a single input neuron, one

output neuron, and eight interneurons. All o the neurons in each network are con-

nected to each other and have sel-connections as well. Afer optimization and test-

ing, the networks that perormed successul chemotaxis were subjected to a prun-

ing procedure whereby unused neurons and connections were eliminated. Dunn et

al. report that the pruned yet still-successul networks have only one or two in-

terneurons and they all have inhibitory eedback among all o the neurons. Dunn

et al. proposed that the main unction o this eedback is to regulate the latencybetween sensory input and behavior but we note that while this latency regulation

may indeed be occurring, it certainly does not explain how successul chemotaxis

is accomplished. Te mere introduction o a delay between input and response

surely cannot suce or successul chemotaxis. We hypothesize that the crucial yet

underappreciated mechanism in the successul networks is the existence o recur-

rent connections. Recurrence has been noted by many authors (e.g. Mandik 2002;

Churchland 2002; Lloyd 2003) as a mechanism whereby a network may instantiate

a orm o short-term or working memory, since activity in the network will notsimply reect the inormation currently coming into the sensory inputs, but also

reect inormation eeding back and thus representing past inormation that came

into the sensory inputs. We hypothesize, then, that the recurrence is implementing

a orm o memory that allows the network to compute the time derivative o the

concentration in virtue o both encoding inormation about the current concentra-

tion (in the state o the sensor) and encoding inormation about past concentration

(in the signal propagated along the recurrent connections).

o s is yoosis, w ondud ou own simulions oC. Eleganssin-

gl-snso moxis. Fo ou simulions w uilizd Fmsiks 3-D Ai-

il Li sow (Komosinski 2000) llowd o onsuion nd sing

o woms in simuld ysis nd oimizion o woms using simu-

ld Dwinin slion. T moologis o ou syni woms did

in Figu 1 nd i nul nwok oologis did in Figu 2. Nwoks

modul. On modul onsius nl n gno guls

owd moion by snding sinusoidl signl o in o musls onol

gllion. Ano modul guls sing wi singl snsoy nuon,

innuons, nd on ouu nuons. Tis v nuon sing nwok is u-n wi vy nuon in i onnd o vy o.


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Figure 1. Syni C. Elgns. On l, on viw. On ig, o viw

Figure 2. Nul nwok o syni C. Elgns. Nuons inlud on snso (s) nd

svl moo nuons (m) nd innuons (i). Singl-dd ows indi ow o in-omion om on nuon o nx. A doubl-dd ow bwn wo nuons in-

dis bo d-owd nd dbk onnion bwn m

In ou simulions iniil moologis nd nwok oologis w s by

nd. T onnion wigs, owv, w oimizd oug Dwinin

oss wby muions llowd only o onnion wigs nd no o

moologis o nwok oologis. Finss is dnd in ms o ovll liim

disn. Tis od woms bo o minin ig vloiy nd lso o x-nd i livs by lnising i ngy so wi ound ood. W omd

omn o kinds o oinion nwoks: ully un nwoks

wi snsoy inu, un nwoks wi no snsoy inu (blind nwoks),


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nd sily d-owd nwoks wi snsoy inu. Fou oulions o

o kinds o oinion nwoks w subjd o voluiony sim-

ulion o 240 million ss o unning ogm.Results are shown in Figure 3 o the lietime distances averaged over the our

populations or each o the three kinds o orientation networks. Te perormance

o the blind networks involved the maximal distance accomplished by worms with

maximally optimized velocities but no extension o liespan through nding ood

beyond whatever ood they collided with accidentally. Worms with sensory inputs

and recurrent connections were able to maximize their liespan through ood-nd-

ing by chemotaxis. Further, their swimming behaviors were similar to those exhib-

ited by real C. Elegans: directed movement up the gradient and dwelling at the

peak. Worms without recurrent connections were conerred no advantage by sen-sory input. Our explanation o this is that without the recurrent connections to

constitute a memory, the worms are missing a crucial representation or the com-

putation o the change o the local concentration over time. We turn now to exam-

ine the nature o these underlying representations.

Figure 3. Rsuls o ximn oming un, d-owd, nd blind n-

woks in n voluiony simulion o moxis

4 What the representations are in the models

W dmidly do no y v oml xlnion oC. Elegans moxis,

bu w do v y good sk o w is going on: Hding in gdin


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is dmind by omuion ks s inus bo snsoy snion

nods inomion bou un lol onnion nd mmoy

snion nods inomion bou s lol onnion. Txisn o mmoy mnism ws did by olk syologil xl-

nion nd suod by simulion ximns. Fu, w in osiion

wi s o s modls o mk som mks bou w sn-

ions nd w lions obin dmin snionl onns.

In oinion nwoks w my disn ys o snions: snsoy

snions, mmoy snions, nd moo snions. T snsoy

snions ss o ivions in mo-snsoy inu nuon,

mmoy snions signls onvyd long un onnions, nd

moo snions ss o ivion in nuons ouu o mus-ls. In s, onns o snions ings -

snd. In snsoy s, w is snd is un lol onnion. In

mmoy s, w is snd is s lol onnion. In moo

s, snion is ommnd signl nd w is snd is lvl o

musul onion.

T qusion iss o w lion is bwn snion nd -

snd is su om is snion o l. wo mjo sos o

suggsion ommon in ilosoil liu on snionl onnsm iniilly libl o s o moxis nwoks: inomionl -

os nd isomoism-bsd os. T s so o suggsion is

lions undwi snion usl-inomionl. On su

suggsion, i is in viu o bing uslly old wi iul xnl

s iul innl s oms o sn i. In moxis xm-

ls, indd lions o usl olion bwn snions

nd w y sn. In s o snsoy snion, is li-

bl usl olion bwn snso s nd un lol onn-

ion nd in mmoy s is libl usl olion bwn -

un signl nd s lol mil onnion. T inomionl viw

mus giv sligly din mn o moo snions sin ommnds

sul ndns o i snionl gs (Mndik 1999, 200).

Te isomorphism suggestion seems applicable as well, though beore discuss-

ing its application we need to spell out the relevant notion o isomorphism. An

isomorphism is a structure preserving one-to-one mapping. A structure is a set o

elements plus a set o relations dened over those elements. So, or example, a set o

temperatures plus the hotter-than relation constitutes a structure as does a set oheights o a mercury column in a thermometer and the taller-than relation. A one-

to-one mapping exists between a set o temperatures and a set o heights just in case


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Mnl Ss

or any height and the next higher one they are mapped respectively to a tempera-

ture and the next hottest one.

Inormation-based theories o representational content make it a necessarycondition on a representation ro a thing c that rcarry inormation about (causally

correlate with) c. Isomorphism-based theories o representational content make it

a necessary condition on a representation ro a thing c that rand c be elements in

structures wherein an isomorphism obtains that maps rto c. Can we adjudicate

between the inormational and isomorphism suggestions? More specically, can

the way in which attributions o representation in the explanations o the network

control o chemotaxis be used to avor inormation-based theories over isomor-

phism-based theories or vice versa? We see the respective roles o the notions o

representation, inormation, and isomorphism in this context as ollows. Te sen-sory and memory states are able to drive successul chemotaxis in virtue o the in-

ormational relationships that they enter into with current and past levels o local

chemical concentration, but they are able to enter into those inormational rela-

tions because o their participation in isomorphsims between structures dened by

ensembles o neural states and structures dened by ensembles o environmental

states. In brie, in order to have the representational contents that they have, they

must carry the inormation that they do and in order to carry the inormation that

they do they must enter into the isomorphisms that they do. o spell this out a biturther will require spelling out two things: First, why it is that representation re-

quires inormation and second, why inormation requires isomorphism.

W bgin wi son wy snion quis inomion. A lg

o son snion quis inomion in xml o m-

oxis nwoks is bus o sos o snion w lking bou,

nmly snsoy nd mmoy snions. I is o nu o snsoy

ss y y inomion bou un lol siuion o n ognism

nd o nu o mmoy ss y y inomion bou s.

Ano wy o i ying o inomion is o liz i n-

woks didn nod inomion bou un nd s mil onn-

ions n y would no b bl o giv is o sussul moxis bv-

io. Consid blind woms: y w divd o mns o noding

inomion bou sn mil onnion. Consid lso woms

wi sily d-owd nwoks. Wiou un onnions, y w

divd o mns o noding lvn inomion bou s. I

sms uil s o ibuing snsoy nd mmoy snions

in xlining sussul on-snso moxis is su ibuions k inomion-bing ois o ss.

o s wy isomoism is imon, i ls o bgin by onsiding ow

d i would b o no v isomoism. Fis o, no , s Gllisl (1990)


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Mnl Ss

my b gdd s onsiuing knowldg o o oy o g domin. As

Cummins (1997: 3637) us oin:

Disl ois gnlly nno b dily nsdud. Insd, dion odisl ois mus b mdid by w w mig s wll ll oy bou

oy. o d s (n insniion o nss) quis oy

sys, in , w sos o oximl simuli libl indios o nss. o

d s visully, you v o know ow s look. T sm gos o olos,

ss, nd sizs: o s o b libly dd visully und ngs in -

siv, liging, nd disn quis knowldg o su s s inl

img siz vis s invs squ o disn o obj. Mu o

knowldg mdis dion o disl ois mus b quid: w

, s, bon wi i knowldg o Emms Lw, bu w no bonknowing ow s look, o wi biliy o disinguis dgs om sdows.

(Fo n gumn simil o Cummins s lso Culnd 2001: 131132; Fo n

gumn u n x inomion om ul snion

only i in isomoisms obin bwn ss o ion nd w is -

ivd, s Kulviki 2004.)

Bsd on bov sos o gumns, w dw ollowing onlusions

bou nu o snion, ls s i lis o simls ss o

us bving inllignly in viu o ossssing mnl snions. A-ibuions o snions o ognisms no simly uisis o b bn-

dond l wn b wys o xlining bvio disovd. Ty -

ibuions o l ois o ognisms nd l lions bwn ognisms

nd i nvionmns. T snions ibud ss o nvous

sysms o us sn nvionmnl (nd bodily) ss in viu

o ying inomion bou os nd quimn on quisiion by

ognism o su ss is ss n ino isomoism lions bwn

nul nd o suus.

On so o objion wv nound in vious sonl ommuni-

ions is noion o isomoism mloyd bov sould insd b -

ld wi noion o omomoism w, in bi, min din

bwn wo is w isomoisms involv on-o-on mings, omo-

moisms involv ming on suu ino (no ono) no. Homomo-

ism oms u in liu on non-mnl snions su s sini

snion (Suz 2003) nd snions inn o msumn

oy (Mws 1994; Sus & Zinns 196) bu w ink isomoism is

mo oi o mnl snion. ying o uiliz noion o omo-moism o mnl snion would involv id suu A -

sns B i nd only i B is omomoi o A wi involvs B ming ino (no

ono) A. Tis llgdly llows o, mong o ings, A o b illy u


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