Cholinergic suppression of transmission may 
allow combined associative memory function and 
self-organization in the neocortex. 
Michael E. Hasselmo and Milos Cekic 
Department of Psychology and Program in Neurosciences, 
Harvard University, 33 Kirkland St., Cambridge, MA 02138 
hasselmo@katla. harvard.edu 
Abstract 
Selective suppression of transmission at feedback synapses during 
learning is proposed as a mechanism for combining associative feed- 
back with self-organization of feedforward synapses. Experimental 
data demonstrates cholinergic suppression of synaptic transmission in 
layer I (feedback synapses), and a lack of suppression in layer IV (feed- 
forward synapses). A network with this feature uses local rules to learn 
mappings which are not linearly separable. During learning, sensory 
stimuli and desired response are simultaneously presented as input. 
Feedforward connections form self-organized representations of input, 
while suppressed feedback connections leam the transpose of feedfor- 
ward connectivity. During recall, suppression is removed, sensory input 
activates the self-organized representation, and activity generates the 
learned response. 
1 INTRODUCTION 
The synaptic connections in most models of the cortex can be defined as either associative 
or self-organizing on the basis of a single feature: the relative influence of modifiable syn- 
apses on post-synaptic activity during learning (figure 1). In associative memofids, post- 
synaptic activity during learning is determined by nonmodifiable afferent input connec- 
tions, with no change in the storage due to synaptic transmission at modifiable synapses 
(Anderson, 1983; McNaughton and Morris, 1987). In self-organization, post-synaptic 
activity is predominantly influenced by the modifiable synapses, such that modification of 
synapses influences subsequent learning (Von der Malsburg, 1973; Miller et al., 1990). 
Models of cortical function must combine the capacity to form new representations and 
store associations between these representations. Networks combining self-organization 
and associative memory function can learn complex mapping functions with more biolog- 
ically plausible learning rules (Hecht-Nielsen, 1987; Carpenter et al., 1991; Dayan et al., 
132 M.E. HASSELMO, M. CEKIC 
1995), but must control the influence of feedback associative connections on self-organi- 
zation. Some networks use special activation dynamics which prevent feedback from 
influencing activity unless it coincides with feedforward activity (Carpenter et al., 1991). 
A new network alternately shuts off feedforward and feedback synaptic transmission 
(Dayan et al., 1995). 
Ai Self-g rganizing 
C. HAfferent 
. Self-organizing 
( : edfrward  2 
 Associative  
feedback 
Figure 1 - Defining characteristics of self-organization and associative memory. A. At 
self-organizing synapses, post-synaptic activity during learning depends predominantly 
upon transmission at the modifiable synapses. B. At synapses mediating associative mem- 
ory function, post-synaptic activity during learning does not depend primarily on the mod- 
ifiable synapses, but is predominantly influenced by separate afferent input. C. Self- 
organization and associative memory function can be combined if associative feedback 
synapses are selectively suppressed during learning but not recall. 
Here we present a model using selective suppression of feedback synaptic transmission 
during learning to allow simultaneous self-organization and association between two 
regions. Previous experiments show that the neuromodulator acetylcholine selectively 
suppresses synaptic transmission within the olfactory cortex (Hasselmo and Bower, 1992; 
1993) and hippocampus (Hasselmo and Schnell, 1994). If the model is valid for neocorti- 
cal structures, cholinergic suppression should be stronger for feedback but not feedfor- 
ward synapses. Here we review experimental data (Hasselmo and Cekic, 1996) comparing 
cholinergic suppression of synaptic transmission in layers with predominantly feedfor- 
ward or feedback synapses. 
2. BRAIN SLICE PHYSIOLOGY 
As shown in Figure 2, we utilized brain slice preparations of the rat somatosensory neo- 
cortex to investigate whether cholinergic suppression of synaptic transmission is selective 
for feedback but not feedforward synaptic connections. This was possible because feed- 
forward and feedback connections show different patterns of termination in neocortex. As 
shown in Figure 2, Layer I contains primarily feedback synapses from other cortical 
regions (Cauller and Connors, 1994), whereas layer IV contains primarily afferent syn- 
apses from the thalamus and feedforward synapses from more primary neocortical struc- 
tures (Van Essen and Maunsell, 1983). Using previously developed techniques (Cauller 
and Connors, 1994; Li and Cauller, 1995) for testing of the predominantly feedback con- 
nections in layer I, we stimulated layer I and recorded in layer I (a cut prevented spread of 
Cholinergic Suppression of Transmission in the Neocortex 133 
activity from layers II and III). For testing the predominantly feedforward connections 
terminating in layer IV, we elicited synaptic potentials by stimulating the white matter 
deep to layer VI and recorded in layer IV. We tested suppression by measuring the change 
in height of synaptic potentials during perfusion of the cholinergic agonist carbachol at 
100M. Figure 3 shows that perfusion of carbachol caused much stronger suppression of 
synaptic transmission in layer I as compared to layer IV (Hasselmo and Cekic, 1996), sug- 
gesting that cholinergic suppression of transmission is selective for feedback synapses and 
not for feedforward synapses. 
Layer I //Layer I 
timulatio //recording 
_ I I 
H-llI ll-m 
ard V-VI 
V-VI - 
! .t yeriv 
-.. recording Region 1 Region 2 
Whitenatt'/ I 
stimulation 
Figure 2. A. Brain slice preparation of somatosensory cortex showing location of stimula- 
tion and recording electrodes for testing suppression of synaptic transmission in layer I 
and in layer IV. Experiment based on procedures developed by Cauller (Cauller and Con- 
nors, 1994; Li and Cauller, 1995). B. Anatomical pattern of feedforward and feedback 
connectivity within cortical structures (based on Van Essen and Maunsell, 1983). 
Feedforward - layer IV 
Control 
Feedback - layer I 
Carbachol (100gM) 
Control Carbachol (100gM) Wash 
Figure 3 - Suppression of transmission in somatosensory neocortex. Top: Synaptic poten- 
rials recorded in layer IV (where feedforward and afferent synapses predominate) show 
little effect of 100gM carbachol. Bottom: Synaptic potentials recorded in layer I (where 
feedback synapses predominate) show suppression in the presence of 100gM carbachol. 
134 M.E. HASSELMO, M. CEKIC 
3. COMPUTATIONAL MODELING 
These experimental results supported the use of selective suppression in a computational 
model (Hasselmo and Cekic, 1996) with self-organization in its feedforward synaptic con- 
nections and associative memory function in its feedback synaptic connections (Figs 1 and 
4). The proposed network uses local, Hebb-type learning rules supported by evidence on 
the physiology of long-term potentiation in the hippocampus (Gustafsson and Wigstrom, 
1986). The learning rule for each set of connections in the network takes the form: 
Where W (x' y) designates the connections from region x to region y, 0 is the threshold of 
synaptic modification in region y, ! is the rate of modification, and the output function is 
g(ai(X) ) = [tanh(aj (x) - kt(x))]+ where []+ represents the constraint to positive values only. 
Feedforward connections (Wij(x<Y)) have self-organizing properties, while feedback con- 
nections (Wij (x>=y)) have associative memory properties. This difference depends entirely 
upon the selective suppression of feedback synapses during learning, which is imple- 
mented in the activation rule in the form (l-c). For the entire network, the activation rule 
M n (x) N n (x) 
ai (y) = Ai (y) + ', ', Wi( < Y)g (a (x)) + ', ', 
x=lk=l x=lk=l 
(1-c) Wi(:->Y) g (a (x)) - ', Hi([ ) (g (a7))) 
k=l 
where ai (y) represents the activity of each of the n (y) neurons in region y, ak (x) is the activ- 
ity of each of the n (x) neurons in other regions x, M is the total number of regions provid- 
ing feedforward input, N is the total number of regions providing feedback input, Ai(Y) is 
the input pattern to region y, H (y) represents the inhibition between neurons in region y, 
and (1 - c) represents the suppression of synaptic transmission. During learning, c takes a 
value between 0 and 1. During recall, suppression is removed, c = 0. In this network, syn- 
apses (W) between regions only take positive values, reflecting the fact that long-range 
connections between cortical regions consist of excitatory synapses arising from pyrami- 
dal cells. Thus, inhibition mediated by the local inhibitory interneurons within a region is 
represented by a separate inhibitory connectivity matrix H. 
After each step of learning, the total weight of synaptic connections is normalized pre-syn- 
aptically for each neuron j in each region: 
Wij(t+ l ) = [Wij(t) +aWij(t)]/ [Wij(t) +aWij(t)]2 
=1 
Synaptic weights are then normalized post-synaptically for each neuron i in each region 
(replacing i with j in the sum in the denominator in equation 3). This normalization of 
synaptic strength represents slower cellular mechanisms which redislribute pre and post- 
synaptic resources for maintaining synapses depending upon local influences. 
In these simulations, both the sensory input stimuli and the desired output response to be 
learned are presented as afferent input to the neurons in region 1. Most networks using 
error-based learning rules consist of feedforward architectures with separate layers of 
input and output units. One can imagine this network as an auto-encoder network folded 
back on itself, with both input and output units in region 1, and hidden units in region 2. 
Cholinergic Suppression of Transmission in the Neocortex 135 
As an example of its functional properties, the network presented here was trained on the 
XOR problem. The XOR problem has previously been used as an example of the capabil- 
ity of error based training schemes for solving problems which are not linearly separable. 
The specific characteristics of the network and patterns used for this simulation are shown 
in figure 4. The two logical states of each component of the XOR problem are represented 
by two separate units (designated on or off in figures 4 and 5), ensuring that activation of 
the network is equal for each input condition. The problem has the appearance of two 
XOR problems with inverse logical states being solved simultaneously. 
As shown in figure 4, the input and desired output of the network are presented simulta- 
neously during learning to region 1. The six neurons in region 1 project along feedfor- 
ward connections to four neurons in region 2, the hidden units of the network. These four 
neurons project along feedback connections to the six neurons in region 1. All connec- 
tions take random initial weights. During learning, the feedforward connections undergo 
self-organization which ultimately causes the hidden units to become feature detectors 
responding to each of the four patterns of input to region 1. Thus, the rows of the feedfor- 
ward synaptic connectivity matrix gradually take the form of the individual input patterns. 
,.oooo 
Afferent 
input 
'"' Region 1 
egion 2 
Figure 4 - Network for learning the XOR problem, with 6 units in region 1 and 4 units in 
region 2. Four different patterns of afferent input are presented successively to region 1. 
The input stimuli of the XOR problem are represented by the four units on the left, and the 
desired output designation of XOR or not-XOR is represented by the two units on the 
right. The XOR problem has four basic states: on-off and off-on on the input is catego- 
rized by yes on the output, while on-on and off-off on the input is categorized by no on the 
output. 
Modulation is applied during learning in the form of selective suppression of synaptic 
transmission along feedback connections (this suppression need not be complete), giving 
these connections associative memory function. Hebbian synaptic modification causes 
these connections to link each of the feature detecting hidden units in region 2 with the 
cells in region 1 activated by the pattern to which the hidden unit responds. Gradually, the 
feedback synaptic connectivity matrix becomes the transpose of the feedforward connec- 
tivity matrix. (Parameters used in simulation: Aj(1) = 0 or 1, h = 2.0, q(1) = 0.5, q(2) = 
0.6, (1) = 0.2, (2) = 0.5, c = 1.0 and Hik(2) = 0.6). Function was similar and convergence 
was obtained more rapidly with c = 0.5. Feedback synaptic transmission prevented con- 
136 M.E. HASSELMO, M. CEKIC 
vergence during learning when c = 0.367). 
During recall, modulation of synaptic transmission is removed, and the various input stim- 
uli of the XOR problem are presented to region 1 without the corresponding output pat- 
tern. Activity spreads along the self-organized feedforward connections to activate the 
specific hidden layer unit responding to that pattern. Activity then spreads back along 
feedback connections from that particular unit to activate the desired output units. The 
activity in the two regions settles into a final pattern of recall. Figure 5 shows the settled 
recall of the network at different stages of learning. It can be seen that the network ini- 
tially may show little recall activity, or erroneous recall activity, but after several cycles of 
learning, the network settles into the proper response to each of the XOR problem states. 
Convergence during learning and recall have been obtained with other problems, includ- 
ing recognition of whether on units were on the left or right, symmetry of on units, and 
number of on units. In addition, larger scale problems involving multiple feedforward and 
feedback layers have been shown to converge. 
1. 2. 3. 4. 
on on no off off no off on yes on off yes 
Illllll - m "ml - Illsl m . . . "]l Im .... m  {l-mm m - -- . 
lllll - I  - II  I a I m  m  ' [I ' I s  m [I  III- I - 
'11 ' m  I   ' I' I I I  I  ' ]1 ' Im - m 11  I ...... 
' II  m  I    i ' I I I  I - ' il ' Im  - m II - I  
l a a ' I ' I I a - m . ' ]1 ' Im . m !l ' I 
 - a a ' I ' I I a - m - ' il  I m   m !1  I - 
ll- I. I   '!'1 I m - m  'll' Im   m 11'1 . 
II  I  I   ' i ' I I I  I - ' ]1 ' Im - I !1 ' I 
ll a a ' l'l I m - I - 'il ' I1 -  m II ' I  1 
II. m  m  '  I' I I m  I  ' !l ' II . - m II  I  
ll ' I  a   '1'1 I m - m  'il' Im   a !l'l . 
II  I  I . - ' I ' I I I  I - - ]1 - I I I !l - I l 
  m '   '  i' I I m - m -  II  Im  m II - I  
l l  m  m ' '  I' I I m ' m '  ]l  Im m II - I  
II   - m  '  i- I I m  m - - II  Im .  II  I - 
I I  I - m  - ' !' I I m  I  - !l  lm . m l  I . 1 
'  '  I' m m  '  -m- m  !  
l - m  m - -  i- I I m - m  - ]l  Im m l - I 
' m  m ..... I I m ' m  'il - Im  m II  I m 
I 
Region 1 Region 2 
Figure 5 - Output neuronal activity in the network shown at different learning steps. The 
four input patterns are shown at top. Below these are degraded patterns presented during 
recall, missing the response components of the input pattern. The output of the 6 region 1 
units and the 4 region 2 units are shown at each stage of learning. As learning progresses, 
gradually one region 2 unit starts to respond selectively to each input pattern, and the cor- 
rect output unit becomes active in response to the degraded input. Note that as learning 
progresses the response to pattern 4 changes gradually from incorrect (yes) to correct (no). 
Cholinergic Suppression of Transmission in the Neocortex 137 
References 
Anderson, J.A. (1983) Cognitive and psychological computation with neural models. 
IEEE Trans. Systems, Man, Cybern. SMC-13,799-815. 
Carpenter, G.A., Grossberg, S. and Reynolds, J.H. (1991) ARTMAP: Supervised real- 
time learning and classification of nonstationary data by a self-organizing neural network. 
Neural Networks 4: 565-588. 
Cauller, LJ. and Connors, B.W. (1994) Synaptic physiology of horizontal afferents to 
layer I in slices of rat SI neocortex. J. Neurosci. 14: 751-762. 
Dayan, P., Hinton, G.E., Neal, R.M. and Zemel, R.S. (1995) The Helmholtz machine. 
Neural Computation. 
Gustafsson, B. and Wigstrom, H. (1988) Physiological mechanisms underlying long-term 
potentiation. Trends Neurosci. 11: 156-162. 
Hasselmo, M.E. (1993) Acetylcholine and learning in a cortical associative memory. 
Neural Computation. 5(1): 32-44. 
Hasselmo M.E. and Bower J.M. (1992) Cholinergic suppression specific to intrinsic not 
afferent fiber synapses in rat piriform (olfactory) cortex. J. Neurophysiol. 67: 1222-1229. 
Hasselmo, M.E. and Bower, J.M. (1993) Acetylcholine and Memory. Trends Neurosci. 
26: 218-222. 
Hasselmo, M.E. and Cekic, M. (1996) Suppression of synaptic transmission may allow 
combination of associative feedback and self-organizing feedforward connections in the 
neocortex. Behav. Brain Res. in press. 
Hasselmo M.E., Anderson B.P. and Bower J.M. (1992) Cholinergic modulation of cortical 
associative memory function. J. Neurophysiol. 67: 1230-1246. 
Hasselmo M.E. and Schnell, E. (1994) Laminar selectivity of the cholinergic suppression 
of synaptic transmission in rat hippocampal region CAI: Computational modeling and 
brain slice physiology. J. Neurosci. 15: 3898-3914. 
Hecht-Nielsen, R. (1987)Counterpropagation networks. Applied Optics 26: 4979-4984. 
Li, H. and Cauller, L.J. (1995) Acetylcholine modulation of excitatory synaptic inputs 
from layer I to the superficial layers of rat somatosensory neocortex in vitro. Soc. Neuro- 
sci. Abstr. 21: 68. 
Linsker, R. (1988) Self-organization in a perceptual network. Computer 21: 105-117. 
McNaughton B.L. and Morals R.G.M. (1987) Hippocampal synaptic enhancement and 
information storage within a distributed memory system. Trends in Neurosci. 10:408-415. 
Miller, K.D., Keller, J.B. and Stryker, M.P. (1989) Ocular dominance column develop- 
ment: Analysis and simulation. Science 245: 605-615. 
van Essen, D.C. and Maunsell, J.H.R. (1983) Heirarchical organization and functional 
streams in the visual cortex. Trends Neurosci. 6: 370-375. 
von der Malsburg, C. (1973) Self-organization of orientation sensitive cells in the stfiate 
cortex. Kybemetik 14: 85-100. 
