learning & intelligence of plants: developments following ...from : acharya j. c. bose – a...
TRANSCRIPT
In the history of Indian science
Jagdish Chandra Bose occupies an
unique position. Seldom do we find a
scientist equally at home in wide
range of disciplines ranging from
physics, instrumentation and botany.
This article reviews the seminal
contributions of Bose in the realm of
plant biophysics and also the current
developments in line with his ideas.
Finally, the paper concludes with a
physical/computational model of
plant learning inspired by Jagdish
Chandra. The past decade has seen a
significant number of plant scientists
affirming the basic insights of Bose in
1. Introduction
Learning & Intelligence of Plants:
Developments following Jagadish Chandra Bose Rahul Banerjee and Bikas K. Chakrabarti
Rahul Banerjee is a Professor in
the Crystallography and Molecular
Biology Division of the Saha
Institute of Nuclear Physics,
Kolkata. His research interests
include protein crystallography and
plant intelligence.
Bikas K. Chakrabarti is a Senior
Professor (Physics) of the Saha
Institute of Nuclear Physics and
Visiting Professor (Economics) of
Indian Statistical Institute, Kolkata.
His research interest includes studies
on complex neural and other networks.
A Bronze Plaque of Acharya J.C. Bose – working
with Magnetic Chrescograph.
(Courtesy : Bose Institute, Kolkatta)
the realm of plant signaling and
information processing. Although in
his time many of his ideas received a
hostile reception from certain
quarters, it is now a fact that most of
them have been absorbed into the
mainstream, current scientific
literature.
In Bose's view (see box 1) there was
an essential unity in the physiological
mechanisms in both animal and
plants such that, “There is no
physiological response given by the
most organized animal tissues that is
also not met with in a simpler form in
plants” [1]. Further, plants were
endowed with a certain degree of
individuality and any experiment
involving plants would have to take
note of their past history and
consequent present response [2] . By
means of the Resonant Recorder and
the Electric Probe (both instruments
designed by him along with the
Crescograph [3] ), Bose was able to
demonstrate that the collapse in the
leaves of the Mimosa plant (Fig. 1)
upon stimulation was accompanied
by an electrical signal which traveled
Contributions of Bose
to the stem, and its passage through
the stem (in both up and down
directions) caused the other leaves to
collapse. Similar experiments
confirmed the coupling of electrical
oscillations and spontaneous leaf
movements in Desmodium. These
movements could be discontinued by
a cut in the Desmodium stalk, which
could again be restored by the
passage of an electric current through
the pulvinus. In addition, the
crescograph was used to demonstrate
the pulsatory nature of the growth in
De smod i um a nd i t s d i r e c t
correspondence with electrical
activity. Description of the most
elegant experiments related to the
ascent of sap lies outside the scope of
this article, yet even here Bose was
able to show the correlation between
the variability in turgor pressure
with electrical signals [4]. These and
a host of other experiments led Bose
to conclude that plants, like animals
are in possession of a nervous system
(though primitive), and similar to
animals, the response of the whole
plant to stimuli was a consequence of
long range electrical signaling
through out the entire plant body.
57
Instrumental Design and Views of J. C. Bose
J. C. Bose was unique in that he not only designed and fabricated his own instruments to study plant response to stimuli but
also gave a series of biological insights spanning the plant and animal kingdoms. Given in this box are two excerpts from the
writings of Acharya J. C. Bose; one concerned with the design of the crescograph and the other dealing with his philosophical views
on inanimate and living matter, to demonstrate his equal ability in instrumental design and original theory.
“ The magnification in my Crescograph is obtained by a compound system of two levers. The growing plant is attached to the
short arm of a lever, the long arm of which is attached to the short arm of the second lever. If the magnification by the first lever be
m, and that by the second, n, the resulting magnification is mn.
The practical difficulties met with in carrying out this idea are very numerous. It will be understood that just as the imperceptible
movement is highly magnified by the compound system of levers, the various errors and difficulties are likely to be magnified in
the same proportion. The principal difficulties met with were due to : 1) to the weight of the compound lever which exerted a great
tension on the growing plant, 2) to the yielding of flexible connections by which the plant was attached to the first lever, and the
first lever to the second, and 3) to the friction at the fulcrums.
Weight of the lever: - As the first lever is to exert a pull on the second , it has to be made rigid. The second lever serves as an index,
and can therefore be made of fine glass fibre. The securing of rigidity of the first lever entails large cross section and consequent
weight, which exerts considerable tension on the plant. Excessive tension greatly modifies growth; even the weight of the index
used in self – recording auxanometers is found to modify the rate of growth. The weight of the levers introduces an additional
difficulty in the increased friction at the fulcrums, on account of which there is an obstruction of the free movement of the recording
arm of the lever. The conditions essential for overcoming these difficulties are 1) construction of a very light lever possessing
sufficient rigidity, and 2) arranging the levers in such a way that the tension on the plant may be reduced to any extent, or even
eliminated.
I found in navaldum, an alloy of aluminium a light material possessing sufficient rigidity. The first lever is constructed out of a
thin narrow sheet 25 cm. in length; it has, as explained before, to be fairly rigid in order to exert a pull on the second without
undergoing any bending; this rigidity is secured by giving the thin narrow plate of the lever a T – shape. The first lever balances, to
a certain extent , the second. Finer adjustments are made by means of an adjustable counterpoise B, at the end of the levers. By
this means the tension on the plant can be greatly reduced; or a constant tension maybe exerted by means of a weight T. In my later
type of the apparatus the plant connection is made to the right, instead of the left side of the first fulcrum. This gives certain
practical advantages. The second lever is then made practically to balance the first, only a slight weight being necessary for the
exact counterpoise. The reduction of total weight thus secured reduces materially the friction at the fulcrum with great
enhancement of the efficiency of the apparatus.'
[ From The high magnification crescograph for researches on growth, Bose, J. C. From : Acharya J. C. Bose – A scientist and a
dreamer Vol. 1 (1996) Eds. P. Bhattacharyya & M. Engineer, Bose Institute , Calcutta, pp. 465 – 467, (1996)]
“ In the pursuit of my investigations I was unconsciously led into the border region of physics and physiology and was amazed to
find boundary lines vanishing and points of contact emerge between the realms of the living and the Non – living. Inorganic
matter was found anything but inert; it was also a – thrill under the action of multitudinous forces that played on it. A universal
reaction seemed to bring together metal, plant and animal under a common law. They all exhibited essentially the same
phenomenon of fatigue and depression, together with possibilities of recovery and exaltation, yet also that of permanent
irresponsiveness which is associated with death. I was filled with awe at this stupendous generalization; and it was with great
hope that I announced my results before the Royal Society, - results demonstrated by experiments. But the physiologists present
advised me, after my address, to confine myself to physical investigations in which my success had been assured , rather than
encroach on their preserve. I had thus unwittingly strayed into the domain of a new and unfamiliar caste system and so offended
its etiquette. An unconscious theological bias was also present which confounds ignorance with faith. It is forgotten that He,
who surrounds us with this ever – evolving mystery of creation, the ineffable wonder that lies hidden in the microcosm of the dust
particle, enclosing within the intricacies of its atomic form all the mystery of the cosmos, has also implanted in us the desire to
question and understand. To the theological bias was added the misgivings about the inherent bent of the Indian mind towards
mysticism and unchecked imagination. But in India this burning imagination which can extract new order out of a mass of
apparently contradictory facts, is also held in check by the habit of meditation. It is this restraint which confers the power to hold
the mind in pursuit of truth, in infinite patience, to wait, and reconsider, to experimentally test and repeatedly verify.”
[The voice of life, Bose, J. C. From : Acharya J. C. Bose – A scientist and a dreamer Vol. 4 (1996) Ed. P. Bhattacharyya, Bose
Institute , Calcutta, pp. 60 – 62, (1996)]
58
Figure 1(a): Mimosa Plant with leaves open.
1(b) The leaves fold and droop when touched
Figure 1 A Mimosa plant before and after touch
stimulation. Bose made a detailed study of the
electrical signaling involved in the collapse of
the leaves in the Mimosa plant ( Bose, J. C. The
nervous mechanism of plants. (1926)Longmans
Green & Co., London. pp. 40.). Other
experiments led him to conclude that electrical
signals were also coupled to the movements of
the leaf in Desmodium (Bose, J. C. Researches
on the irritability of plants. (1913) Longmans,
Green & Co., London. pp. 94 ).
P h o t o s b y B a r r y R i c e , P h D ;
http://www.sarracenia.com/galleria/galleria.ht
ml
Plant Tropisms
The directed response of a plant
due to environmental stimuli is
t e r m e d a s t r o p i sm , t o b e
d i s t i n g u i s h e d f r om n a s t i c
m o v e m e n t s w h i c h o c c u r
spontaneously without a definite
relation to any environmental
stimulus vector. Thus, response to the
direct ion of l ight i s termed
phototropism, due to gravity
g r a v i t o t r o p i s m , t o u c h –
thigmotropism and water - hydro
tropism . There is also the most
unusual tropism of the parasitic
creeper vine Monstera which grows
along the ground in search of darkness probably caused by the shadow of a
prospective host tree, called skototropism. Every tropism however, involves
three distinct phases, 1) the detection of the initial environmental signal by
plant receptors 2) subsequent processing/transduction of the primary signal
and 3) the consequent integrated physiological response. Plants being sessile
(that is literally rooted to the same spot), the response is more in terms of
growth and development in contrast to animals who respond primarily by
movement. Whatever the tropism, the resulting responsive growth invariably
involves the asymmetric redistribution of the plant hormone auxin. Even
though all tropisms will most probably involve auxin yet the receptor and the
suite of molecules involved in the transduction of the signal are quite different
in each case. Currently a plethora of plant hormones have been discovered
apart from auxin (gibberelic acid, cytokinin, sugars, brassinosteroids), with
interlinked signaling pathways.
A detailed description of the receptors and their associated signaling pathways
for the different tropisms lies beyond the scope of this article. Rather we will
only discuss thigmotropism (response due to touch) as it relates to the extensive
work of Bose on the Mimosa plant. The primary receptor for touch (if it at all
exists) is yet to be identified. However, there is no doubt that the second most 2+important messenger is intracellular calcium (Ca ). The electrical signal
which is an action potential, thus generated (measured by Bose in Mimosa) is 2+due to the rapid increase in cellular calcium (Ca ) in the mechanically
+ +perturbed cells [5]. Unlike animal cells which utilize Na and K ions, action 2+ + -potentials in plants require the dynamical redistribution of Ca , K and Cl
ions. The action potential generated in the Mimosa leaflet travels to the pulvinus with a speed of about [6] 20 – 30 mm/s. Bose had measured [2] the
speed of transmission to be about 30 mm/s . In comparison the action potential
transmission speeds in the case of Anodonta is 45 mm/s, slug (125 mm/s),
octopus (3000 mm/s) and in mammals up to 100,000 mm/s. On reaching a
pulvinus the action potential is transmitted laterally via plasmodesmata into
the cells of the motor cortex which respond by ion and water efflux leading to
the dramatic leaf movements in Mimosa.[6] The initiation of the action
potential and the subsequent signal transduction steps leading to ion and water
efflux in the motor cortex requires further elucidation in terms of molecular
Figure 2 : A Venus Fly Trap before and after entrapping an insect. Two successive electrical signals precede
the closing of the trap around the insect (Simons, P. J. The role electricity in plant movements. (1981) New
Phytologist, 87, pp. 11 – 37).Photos by Barry Rice, PhD; http://www.sarracenia.com/galleria/galleria.html
(a) (b) (c)
59
events. Yet a set of touch inducible
genes (TCH) have been identified
Arabidopsis Thaliana, a significant
fraction of which encodes calcium
binding calmodulin or calmodulin -
like proteins. This was probably only
to be expected as the cellular 2+distribution of Ca plays a key role
in the genesis and passage of action
potentials in the first place. The most
dramatic thigmotropic response is in
the case of the Venus fly trap (Dionaea
muscipula), a predatory plant which
closes its bilobed leaves around
unsuspecting insects (Fig. 2), who
mechanically disturb the trigger hairs
on its leaf surface. It is an interesting
fact that two successive action
potentials are required for leaf
closure.
There are actually two forms of
electrical signals prevalent in plants
the action potentials (AP) and the
slow wave variational potentials
(VP). In contrast to the action
potentials, the VP's vary with the
intensity of the stimulus (thereby
having a large range of variation
unlike AP's which are all or none) and
have delayed repolarizations. The
ionic mechanism behind the
transmission of AP's also differ
significantly from VP's. Both AP's
and VP's are involved in long –
distance signaling and can invoke a
response distant from the local area of
the applied stimulus [6]. Thus AP's
have been implicated in trap/tentacle
closure (for Dionaea, Drosera),
regulation of leaf movements
(Mimosa) , increase in respiration and
gas exchange (Zea), decrease in stem growth (Luffa) and induction of gene
expression (Lycopersicon). Similar
and other unique functions have been
identified for VPs [6].
A c t i o n p o t e n t i a l s &
variational potentials
Plant neurobiology
It thus appears we have turned a full
circle. Initially, electrical signaling
was set aside in favor of the concept
of 'chemical diffusion' of auxin which
was thought to predominantly
media te p lan t phys io log i ca l
response. It now transpires that
calcium signaling involving electrical
signals could play the primary role
[7], with auxin transport based on a
vesicle – based process, rather like
neurotransmitter release at synaptic
junctions in animal nervous systems.
So strong has been the returning wave
that there has been definite proposals
for a 'plant neurobiology [8] , a
discipline whose field of study
would seek to understand how plants
receive multiple environmental
signals, how these are processed by
signal transduction networks and
how these multiple yet interrelated
information processing streams are
integrated to yield the f inal
coordinated response . Brenner et al.
[8] sums up the objectives of the
n a s c e n t s c i e n c e a s , “ P l a n t
neurobiology is a newly initiated field
of research aimed at understanding
h ow p l a n t s p e r c e i v e t h e i r
circumstances and respond to
environmental input in an integrated
fashion, taking into account the
combined molecular, chemical and
electrical components of intercellular
plant signaling. Plant neurobiology
is distinct from the various disciplines
within plant biology in that the goal of
plant neurobiology is to illuminate
the information network that exists in
plants.”
This approach however, has not been
without detractors. In a strongly
worded article Alpi et al. [9] rejects
the scope and validity of the new
discipline on the grounds that plants
do not have neurons or brains, in
short a nervous system. Again
n e u r o t r a n sm i t t e r s a r e n o t
transported from cell to cell as in the
case of auxin, whose vesicular
transport remains to be conclusively
established. Another problem
appears to be the existence of
n um e r o u s p l a sm o d e sm a t a ,
apparently contributing to polar
auxin transport between cells. In response, [ 8,10] the protagonists for
'plant neurobiology' contend that the
term is used only as a metaphor,
w h i c h u n d e r t h e p r e s e n t
circumstances is highly apt. It is
factually incorrect to suppose that
existence of brains or nerves (literally
speaking) is being proposed in
plants. It is beyond any doubt that
long–range electrical signaling does
exist in plants and the mechanism by
which a sunflower plant conducts an
action potential over 0.3 m [8] remains
to be elucidated in sufficient detail.
As mentioned previously, much
work remains to be done in
understanding the role of electrical
signals and their integration with
plant chemical signaling systems. In
such a situation where else to seek for
analogies if not in animal neural
models ? In this context it is notable
that Bose [2] was the first to use the
term 'plant nerve' and currently the
plant phloem [8] has been
compared to an animal axon in that
both are capable of conducting
bioelectrochemical impulses over
long distances and being structurally
equivalent to “hollow tubes filled
w i th e l e c t ro ly t e so lu t i ons . ”
The question then arises as to
what constitutes the information
processing network in plants
analogous to the neural networks of
animals ?
The information processing
network(s) in plants
60
Any network will be composed of
elements which are in specific
communication with each other. In
animal neural nets, the elements are
of course neurons interacting with
each other via synaptic junctions.
What could be the analogous
pathways of information flow in
plants? Plants lack specialized
neuronal cells, yet information flows
by electrical and chemical means
through different levels of its
structural organization. As of today the view appears to be that [4] the
primary network in plants is a signal
transduction network involving 2+cytoplasmic calcium ( Ca ) , which
appears to have a ubiquitous role as a
cellular second messenger. This can
both give rise to electrical (APs &
VPs) and also interact with chemical
signals as there is evidence to suggest
that calcium could be an agonist or
antagonist for all plant growth 2+regulators including auxin. Since Ca
has limited cytoplasmic mobility , the
movement of calcium within the cell
by means of simple diffusion is highly
improbable. Rather a system of
calcium channels and APTases 2+(proteins) pump Ca along specific
directions (relative to subcellular
c ompar tmen t s ) r e su l t i ng i n 2+'propagated waves of Ca ' release [4],
the amplitude and kinetics of which
are a rich source for computation and
information transfer. Thus the nodes 2+in this net would be Ca channels and
the edges probably the direction of 2+the propagating Ca waves. How
2+these propagated waves of Ca
interact with the chemical signaling
pathways is of course one of the
c e n t r a l p r o b l em s o f p l a n t
neurobiology. In any case, what is
being proposed is a dual
information processing system in
plants; a primary electrical system
based on calcium signaling and a
secondary chemical system , the
other factors which are yet to be
determined. By now it should be
evident that the information network
in plants is quite complex and
modeling this network to account for
plant physiological response could
well be a daunting task.
2+ As calcium ion Ca is implicated in
all forms of electrical and chemical
communication prevalent in plants it
would be appropriate to take a closer
look at signaling pathways involving 2+Ca . Plants are capable of responding
specifically to a wide range of biotic
and abiotic signals. The natural
question then is , as to how the same 2+cation (Ca ) can be instrumental in
evoking a specific response for each
distinct stimulus ?[11] Internal to
plant cells are mobilizable calcium
stores located in the vacuole, cell
wall, mitochondria and endoplasmic
reticulum (ER). On the arrival of an
environmental signal either calcium
flux from external sources or release
from internal stores create transient
changes in the concentration of free 2 +c y t o s o l i c c a l c i u m [ C a ] . i
2+Characteristic patterns of such [Ca ]i transients, referred to as 'calcium
signatures'[11] direct downstream
signaling events, finally leading to
a r e s p on s e s p e c i f i c t o t h e
e n v i r o n m e n t a l s t i m u l u s .
' C a l c i u m s i g n a t u r e s ' a r e
characterized by their spatial
location, temporal pattern and 2+amplitude or strength of the [Ca ]i
transients, which confer on them the
ability to activate specific sets of
molecules as downstream signaling
events. There is evidence that
different pools of calcium stores are
preferentially mobilized by different
environmental cues. For example,
Calcium signaling and the
concept of 'calcium signature'
interplay of both leading to the
regulation of plant physiology. In 2+network terminology, Ca is a highly
connected hub as it is i) the corner
stone of intra–cellular signal
transduction pathways ii) implicated
in long distance electrical signals and
also iii) interacts with the chemical
signaling systems.
2+ +At the next level are directed Ca , K , -Cl ion fluxes which give rise to AP's or
VP's traveling over large cellular
distances (from their point of
initiation) from leaf to leaf, root to leaf
and up the stem to evoke growth
responses, protein expression or
changes in hormone metabolism.
There appears to be three pathways
of long distance electrical signaling in
the primitive plant 'neural system' [8] :
1) Fast moving action potentials
traveling long distances through
the phloem and companion cells.
2) Cellular complexes in the root
'transition' zone which have
'synapses' similar to animals.
Despite Alpi's criticism [9] there
is growing evidence in the
literature that auxin can be
transported from cell to cell with
the aid of a calcium dependent
vesicular trafficking system.
3) In the case of severe trauma
long distance electrical signaling
occurs in the xylem which is
coupled to hydraulic pressure
waves.
Apart from electrical signals, an
additional complication exists in
plants due to plasmodesmatal
connections between adjacent cells
which allow the passage of proteins,
nucleic acids and other smaller
mo l e cu l e s . P l a smode sma t a l
connections are sensitive to calcium
concentration, inositol phosphates,
osmotic stress and probably a host of
61
touch and wind appear to activate
the same intracellular calcium store
in contrast to cold shock which 2+largely involves extracellular Ca .
Again highly localized regions of the
cell could exhibit fluctuations in 2+[Ca ] depending on the stimulus , as i
in the case of root hairs stimulated
by the Nod factor where the increase 2+in [Ca ] is in the vicinity of the i
'nuclear region'.
Similar to animal cells, localized
elemental events such as quarks , 2+blips, sparks and puffs in [Ca ]I
encodes significant information
relevant to the subsequent response.
These localized elevations can 2+coalesce to generate Ca waves
w h i c h c o n t a i n s e n c r y p t e d
information both in its amplitude and
frequency. For example, in Fucus
strong correlation was found between
the strength of the hypo – osmotic
shock and the spatio – temporal 2+features of the [Ca ] waves, probably i
determining the response from a
wide range of possibilities. One of the 2+systems where [Ca ] oscillations i
have been extensively studied is in
the stomatal guard cells, where it was
found that only oscillations within a
defined window of frequency,
transient number, duration and
amplitude resulted in steady state
stomatal closure. Outside this
window the oscillations led to short 2+ term Ca reactive stomatal closure, a
wholly different physiological
response.
The calcium binding protein receptor
molecules (Box 2) which perceive and
can apparently differentiate calcium
signatures can be divided into two
classes : sensor relays and sensor
responders. The former consists of
the ca lc ium binding prote in
calmodulin (CaM) , which then
in te rac t s wi th down s t ream
Figure 1
Figure 2
62
calcineurin B like proteins (CBL). In
turn CBL's , interact with CBL -
interacting protein kinases (CIPK) ,
which directs the signal along
appropriate channels . Sensor
responders on the other hand include
calcium dependent protein kinases
(CDPK's) which integrate both the
calcium binding domain and the
kinase domain into the same
molecule. CDPK's are again
associated with their cognate CDPK
related protein kinases (CRK's) to
trigger specific signaling cascades.
There is evidence that CDPK's are
particularly sensitive to variation in
the calcium spikes as in the case of
three different CDPK isoforms in
soybean which exhibit different
calcium thresholds for activation.
How the plant molecular machinery
can discriminate between different
calcium signatures is a fruitful area of
ongoing research.
Animals display memory, learning
and intelligence by virtue of their
neural networks, which endows them
with plasticity in their movements
and overall behaviour. Can a similar
claim be made for plants ? In a series
of the most elegantly written articles
Trewavas [10] argues that plants are
actually intelligent organisms (not
automatons) possessed of memory
and capable of learning. There are
close parallels between neural nets
and signal transduction networks.
Learning in the context of a neural net
occurs when the information flux
rates between the signal and
adaptive response are improved by a)
making new connections or b)
modifying the strength of pre-
existing connections at synaptic
junctions. Memory lasts as long as the
specific pathway of heightened
Plant memory, learning and
intelligence
information flow can be maintained.
Memory and learning are thus
correlated. For example in the marine
snail Aplysia learning is due to the
formation of new dendrites, and
memory lasts as long as the specific
dendritic connections persist. Signal
transduction networks also have the
abi l i ty to direct or increase
information flow along specific
pathways either by increasing the
number of protein molecules or
c h a ng i n g t h e i r a f f i n i t y b y
phosphorylation/ mutation. Memory
results when increased signaling
through the specific pathway can be
retained and the information is made
available to other interacting
pathways through cross – talk. Thus
at the level of networks there appears
to be no reason why plants should be
incapable of learning. On the
contrary, plants would be expected to
display some modes of learning and
memory, however rudimentary.
A standard practice to establish
memory in plant systems is to
consider a physiological response
which requires two successive
environmental signals for its
initiation. By varying the time
interval between the signals, it is
possible to determine the duration for
which the memory of the first signal
persists. Thus in the predatory plant
Dionaea muscipula, two successive
action potentials have to be evoked
within 40s of each other [6] for leaf
closure and consequent entrapment
of the insect. Again tendrils require a
combination of mechanical stimulus
and blue light to coil. In this case, the
memory of the mechanical stimulus
can be retained for several hours.
Instances have also been observed of
prior signals modifying the pattern of
the subsequent response. For
example in de – etiolated flax
seedlings with the main stem above
cotyledons removed, prior exposure
to either white or red light changes
the pattern for bud growth. There is
also considerable accumulated 2+evidence that [Ca ] signatures i
suffer alteration by virtue of previous
experience. Repeated stimulation
leads to a attenuation in the 2+amplitude of [Ca ] spikes and i
calcium signatures from one
environmental challenge can be
modified if there has been previous
exposure to a contrasting one. It is
notable that Nicotiana plumbaginifolia
is unable to retain the memory of cold
shocks and is cold sensitive in
contrast to Arabidopsis which is
retentive of 'cold memory' and is also
resistant to cold stress. Thus plant
memory can last from a few seconds
to days , weeks and months
depending upon the physiological
response under consideration [10].
The fairly simple paradigm to study
learning involves subjecting the plant
to an entirely novel situation (not
previously encountered in evolution)
and observing its response to adapt
to the altered circumstances and
thereby continuing its development.
It has generally been observed that
exposure of plants to herbicides,
initially leads to depressed growth,
which the plant learns to overcome
after a certain time interval.
Thereafter there is accelerated growth
as a compensatory measure. Thus
application of Phosphon D at varying
concentrations to peppermint plants,
led to their diminished growth. After
a few weeks the plants not only
recovered their growth rate, but
actually grew faster than controls. A
variety of stressful situations
(drought, high salt, osmotic stress,
temperature extremes etc.) can be
lethal to plants. However, plants can
be trained under the appropriate
63
application of milder doses, to cope
with the onset of more extreme
conditions.
Learning in plants [10] can be viewed
as a trial and error process wherein
the fluctuating environmental signals
are continuously processed and the
adaptive response optimized in
several iterations. In such a situation
oscillatory behaviour can be expected
as the plant zeroes in to its optimal response. Bose was one of the first to
note that petioles, leaflets and roots of
Mimosa oscillated to its new state of
growth upon exposure to novel
stimuli. Similar oscillatory
behaviour was also observed in
seedling roots and shoots upon
vertical displacement. Sustained
oscillations were also found in
rhizomes upon gravitropic response.
Similar to animals the learning
stages appear to initially involve
reversible modification of ion fluxes
and signal transduction pathways,
followed by gene expression for
metabolic/physiological adaptation
and finally phenotypic modification.
The above changes are calibrated by
the sustained strength and continued
presence of the environmental signal.
Finally, we come to the question of
intelligence in plants. The concept of
intelligence (which has several levels
of significance) is evidently more
involved than that of either learning
or memory, given the difficulty
inherent in its very definition. Most
of the time there is a tendency to
restrict our definition to characteristic
human expressions of the faculty.
One definition attributed to David
Stenhouse [12] is, “…….adaptively
variable behaviour during the
lifetime of the individual.” Another
definition attributed to Cerra &
Bingham [12] is , “ The sine qua non of
behavioural intelligence systems is the capacity to predict the future : to
model likely behavioural outcomes in the service of inclusive fitness.” Since
plants respond primarily by growth and development rather than movement (with the exception of Mimosa and a few others) Trewavas has modified the
first definition in the context of plants as , “ adaptively variable growth and
development during the lifetime of the individual.” As has been mentioned
previously, since the display of intelligence is an individualistic phenomenon,
averaging some physiological parameter (e.g. growth) over a large number of
experimental specimens would not be conducive to its study. In its natural
environment plants have to grow so as to optimize its access to limited
environmental resources (water, light, nutrients) , which is probably akin to
navigating a maze, a standard test for intelligent activity. Thus, plants
continuously monitor at least 15 environmental variables with great
precision and accuracy to yield a coordinated/integrated response. Examples
would include the shoot sensing its nearest neighbours utilizing near – infra
red light and then taking necessary avoiding action. On the approach of
competitive neighbours the stilt palm simply moves away by differential
growth of prop roots supporting the stem [10]. Rhizomes (prostrate stems
carrying buds and roots) actively forage for new habitats which are resource
rich and free from competitors. Decisions are taken as to which buds will give
rise to leaves (instead of rhizomes) while other rhizomes continue their search.
Roots also monitor soil nutrients and take evasive action when challenged by
competitors. If intelligence involves forecasting future resource allocation for
physiological action, such predictive computations are also performed by
plants. One such example, is the dodder (Cuscuta sp.), a parasitic plant (Fig. 3)
which assesses its prospective host by an initial touch contact. If found
unsuitable the dodder continues its search, whereas on selection of a
suitable host the dodder coils around its host in a specific manner with
resource transfer from the host commencing after several days.
Figure 3 A Dodder (Cuscuta sp.) coiling around its host.
The plant does not coil around every host with which it
comes into contact. Subsequent to the first contact
Cuscuta asseses its prospective host before coiling
around the host plant (Kelly, C. K. Plant foraging: a
marginal value model and coiling response in Cuscuta
subinclusa. (1990) Ecology, 71, pp. 1916 – 1925 ; Kelly. C.
K. Resource choice in Cuscuta europea. (1992) Proc. Natl.
Acad. Sci. U.S.A., 89, pp. 12194 – 12197). If the
prospective host is found to be unsuitable the parasitic
plant continues its search for other hosts. Photos by
Barry Rice, PhD;
http://www.sarracenia.com/galleria/galleria.html
By now it should be evident that modeling of information networks in plants
to gain insight into its physiological activity is a task of primary importance.
One such attempt [13] can be found in Box 3 and Box 4.
64
Summing up
To sum up [14], Bose's primary intuition of plants as living organisms capable of intelligent behaviour ( a property of the whole organism) stands in the process of being completely justified. Progressively, there seems to be little doubt that
aspects of intelligent behaviour, such as memory, learning, decision – making, sensing, predictive foresight to optimize
'resource acquisition with economy of effort' are found in varying degrees in plants. Some could even possess all these
intelligent capabilities. It is somewhat ironic that current developments in botany had been foreseen by Bose about a
hundred years ago, though they were consistently rejected during his lifetime. The entire matter is put in a nutshell by
Brenner et al [8], “ Bose's overall conclusion that plants have an electro – mechanical pulse, a nervous system, a form of
intelligence, and are capable of remembering and learning, was not well received in his time A hundred years later,
concepts of plant intelligence, learning and long – distance electrical signaling in plants have entered mainstream
literature.”
Hopfield Model of Associative Memory
In the Hopfield model, a neuron i is represented by a two - state Ising spin at that site (i). The synaptic connections are
represented by spin – spin interaction and they are taken to be symmetric. This symmetry of synaptic connections allows one to
define an energy function. Synaptic connections are constructed following Hebb's rule of learning, which says that for p patterns
the synaptic strength for the pair (i,j) is
where i = 1,2, …….., N, denotes the ì - th pattern learned (ì = 1,2,……p). Each can take values ± 1. The parameter N is the
total number of neurons, each connected in a one to all manner and p is the number of patterns to be learned. For a system of N
neurons, each (i) with two states, (ó = 1) the energy function is i
The expectation is that, with w 's constructed as in (1), the Hamiltonian or the energy function (2) will ensure that any arbitrary ij
pattern will have higher energy than those for patterns learned; they will correspond to the (local) minima in the (free) energy
landscape. Any pattern then evolves following the dynamics
where h (t) is the internal field on the neuron i, given by i
*Here, a fixed point of dynamics or attractor is guaranteed; i.e., after a certain (finite) number of iterations t , the network stabilizes * *and ó (t + 1) = ó (t ) . Detailed analytical as well as numerical studies shows that the local minima for H in (2) indeed i i
correspond to the patterns (with 100 % overlap) fed to be learned in the limit when memory loading factor á = (p/N) tends to zero;
and they are less than ~3% off from the patterns (97 % overlap) fed to be learned when á < á ~ 0.14. Above this loading, the c
network goes to a confused state where the local minima in the energy landscape do not have any significant overlap with the
patterns fed to, or learned by, the network.
[ For details see Hertz,J., Krough, A. and R.G. Palmer, R. G. Introduction of Theory of Neural Computation. Addison-Wesley
Publishing, Cambridge, MA (1991) ]
(1)
(3)
(4)
(2)
��
65
A Hopfield – like Plant Intelligence Model
Let us first briefly mention several results concerning properties of the plant units (cells), namely, the current (I) – voltage (V)
characteristics of their cell membrane. In Fig.1 [(a) The non-linear current (I)-voltage (V ) characteristics of cell membranes.
(b) Its Zener diode like representation with threshold voltages indicated by VT ], we show the typical non – linear I – V
characteristics of cell membranes acting as logical gates [See Chakrabarti, B. K. and Dutta, O. An electrical network model of
plant intelligence. Ind. J. Phys. A 77, pp. 551-556 (2003) ]
From this figure , we find that the I – V characteristics are equivalent to those of
the Zenner Diode. Namely, some threshold v exists crossing which, the T
direction of the current changes. By assuming that the current has two
directions, that is ± 1, and the voltage is determined by the weighted
contributions, Chakrabarti and Dutta (2003) constructed a mathematical
plant cell as a non – linear unit. From the viewpoint of input – output logical
units like perceptrons for neural networks, the output of the i – th unit
O is given by i
where strength of each connection w is all positive or negative. Note that in the Hopfield model it is given as ± randomly ij
distributed weight matrix in terms of the Hebbian rule
From these experimental results and simple observations, we now ask a natural question: could the plants act as memory devices
similar to a real brain ? Obviously, in the above model of a plant network, there is no frustration in the network as in animal brain
models [Inoue, J. and Chakrabarti, B. K. Competition between ferro-retrieval and antiferro orders in Hopfield-like network model
plant intelligence. Physica A , 346, pp. 58-67 (2004) ]. The above mentioned paper attempts to elucidate, as to what extent
constraints on the sign of the weight matrix influences the ability in plants to retrieve patterns as associative memories.
For this purpose, they introduce a simple plant intelligence model based on a Hopfield - like model in which ferromagnetic
retrieval and anti – ferromagnetic ordered phases co exist. Inoue & Chakrabarti (2004) start from the following Hamiltoni an
where we defined the following two parts of the total Hamiltoni an
ì ì ìAs in Box 3, the vector æ = { æ ………. æ } denotes the ì – th embedded pattern and ó = {ó …….., ó } stands for neuronal 1 N 1 N
states. A single parameter � determines the strength of the antiferromagnetic order. That is to say, in the limit of ����, the system
is completely determined by the energy function H . On the other hand, in the limit of ���0, the system becomes identical to the AF
conventional Hopfield model.
Comparing with the Hopfield model results as in Box 3, it was shown by Inoue & Chakrabarti (2004) that the memory capacity of
this plant network model decreases considerably with � , leaving the network with a weak memory capacity [ For review see
Inoue, J. A simple Hopfield-like cellular network model of plant intelligence in Ref. 13, pp. 169-174 ]
66
Acknowledgment
References
This article has been developed from
a previous article published in
Science & Culture, Vol 74 (11-12)
Nov-Dec (2008) pp.423-432. Mr.
Kausik Das and Mr. Venugopal are
a cknowledged fo r t e chn i ca l
assistance.
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67