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Plasticity in auditory cortical circuitry
Plasticity in auditory cortical circuitry Ehud Ahissar and Merav Ahissar The Weizmann
Institute,
Recent the
studies
short-
and
the primary behavioral between
have focused long-term
auditory
factors operating
Rehovot and The Hebrew
on the mechanisms
plasticity
cortex of adults.
at the cellular and local
levels.
that
following
observed
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Opinion
system, which could mediate
manipulation.
The direct behavioral consequences of the extended representation have not been extensively studied. The nearly ubiquitous expansion (although see [S*]) is puzzling and raises several questions as to its cause, on the one hand, and its consequences, on the other. For example, is it induced by a common local rule of plasticity operating at the intercellular level? Is it necessary and/or sufficient for behavioral improvement?
1994,
580
changes
additional
4:580-587
rules of plasticity
Intercellular temporal contingency
Increased cortical representations of experimentally enhanced input frequencies can be explained by learning mechanisms based on the Hebbian principle operating at the intercellular level. The original Hebb rule [9], combined with Stent’s extension [lo], suggests that a positive contingency between the increased activities of the pre- and postsynaptic neurons will strengthen their connection, whereas a negative contingency will weaken it. According to this rule, input synapses conveying the enhanced range of inputs will strengthen, given that the postsynaptic cell is activated by these stimuli. Quiescent synapses, exhibiting in this case negative correlation with the output cell, will weaken. Consequently, tuning curves of postsynaptic cells will shifi in favor of the enhanced stimulus range expanding its representation (for further discussion see, e.g. [11,12*,13*]). However, none of the experiments demonstrating tuning shifts measured intercellular temporal contingency directly. Several studies (in visual and motor cortices) manipulated local contingency between a postsynaptic unit and an external or thalamic stimulation, applying invasive techniques [14-201. These studies were highly informative in indicating that temporal contingency was necessary to induce modulations. However, as the effect of the intracranial stimulation was not well defined, and other synapses, e.g. cholinergic and noradrenergic, may have also been stimulated, these studies could not determine whether temporal contingency was sufficient. Furthermore, not having recorded a specific pre- and
Ahbreviations ACh-acetylcholine;
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However,
required to generate behavioral
local
Data collected during the past years indicate that adult auditory cortex (including the primary cortex) is capable of undergoing substantial plasticity. The most prominent change, found under different behavioral conditions and using a variety of recording techniques, is a specific increase in the extent of cortical representation of the experimentally enhanced range of tone f?equenties: a fast establishment of a conditioned fear response to pure tone is accompanied by a tist [l’], long-lasting [2*] shifi of neuronal characteristic fi-equencies towards the frequency of the conditioned stimulus. Consequently, the representation of this frequency (measured with fluorodeoxyglucose) is increased [3]; improvement in (pure tone) frequency discrimination is correlated with a longterm increase in the representation of the whole range of stimulating frequencies [4*-l - although training to discriminate between spectral envelopes mainly sharpens neuronal tuning curves (D Keeling, K Krueger, B Calhoun, CE Schreiner, abstract 17:22, Assoc Res Otolaryngol, St. Petersburg Beach, Florida, February 1994). Frequency-specific deafferentation also induces shortand long-term expansion of the cortical representation of spared neighboring frequencies [5,6,7*].
gated unsupervised
has not yet been tested directly.
in Neurobiology
Introduction
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0 Current Biology Ltd ISSN 0959-4388
Plasticity in auditory cortical circuitry Ahissar and Ahissar
postsynaptic neuronal pair, a quantitative study of the required temporal contingency was not possible. Simultaneous recording of two neurons in the behaving mammal is possible only when recorded extracellularly. A quantitative analysis of the nature of correlated activity yielding neuronal plasticity can be obtained applying cross-correlation analysis to the extracellularly recorded activities. This correlation reflects the statistical efficacy of the coupling, induced by direct, but also by indirect, synaptic connectivity. It is therefore termed functional coupling [21]. It can be shown that functional coupling describes activity covariance, mutual information and surprise functions. The competence of these functions in describing neural coding was supported by both theoretical accounts [22-261 and physiological findings [23,27-301. A novel approach utilizes measures of lasting changes in the functional coupling to determine functional plasticity [27]. Plastic changes of functional couplings serve as an estimate of neuronal plasticity. Using this approach it was shown that functional plasticity is indeed guided by changes in the temporal contingency between the activities of the coupled cells: when conditioning increased the temporal covariance, the coupling was potentiated, whereas when it decreased the covariance, the coupling was depressed [27]. When the covariance was not affected by the conditioning (e.g. during pseudo-conditioning), the strength of the coupling was, in most cases, not changed (Fig. la).
Negative generally
feedback operating on the synaptic weight was formulated as follows [34] :
Aw = [g(x)c- WIh(y), where w is the synaptic function.
weight
and h is a monotonic
This formulation predicts, firstly, that there should be a negative correlation between pre-conditioning weights and the magnitude of changes induced by conditioning, and, secondly, that at steady-state, there should be a positive correlation between weights and presynaptic activity levels. None of these three predictions was confirmed by analysis of functional plasticity [27,38]. The finding that weights were not correlated with activities of either the pre- or postsynaptic neuron, and weights changes were not correlated with changes of either activity, suggests that the negative feedback operates on a different factor. This factor may be the correlated activity itself, as in the following form: Aw = g(%y- c %y >), where t denotes a ‘trace’ of the pre-synaptic activity [35] on a scale of a few milliseconds, and c > denotes averaging over a longer time window, on the scale of a few minutes.
These findings partially support the covariance formulation of temporal contingency [22]. However, functional coupling was modified by the change in covariance and not by its absolute value (see also [31]). Thus, the reference level around which coupling was strengthened or depressed was the preconditioning, steady-state level and not zero covariance (expected only when there is no coupling). Such a reference level is beneficial as it overcomes the ‘runaway’ problem [32-351 embedded in the covariance formulation: excitatory synapses, inducing positive covariance, would be indefinitely facilitated towards some saturation value.
Nonetheless, a multiplicative dependency should also be considered, as it is suggested by experimental findings that the fractional change, rather than the differential change, was negatively correlated with the pre-conditioning strength [38]:
Learning rules that address the runaway problem incorporate a negative feedback component: changes of synaptic weights are inversely related to either the present synaptic weights or to the expected postsynaptic activities (reviewed in [34], and for classical conditioning in [35-371). Negative feedback operating on the postsynaptic activity was in general formulated as follows:
Note that we tested rules derived for synaptic weights using results of functional couplings. While it is clear that these two measures should be strongly related, this testing could be refined either by deriving predictions of models in the functional domain or by translating these findings to the synaptic domain.
Aw = g(x)c (Y- < Y >I, where Aw is the change in synaptic weight, x and y denote the activities of the pre- and postsynaptic neurons, respectively, c is a constant, g is a monotonic function and denotes the expected value of y. The straightforward prediction of this formulation is that synaptic modifications will be correlated with changes in postsynaptic activities.
Awlw
= g(%y<%y>)-
1.
The function g can be approximated by experimental data, e.g. see Fig. 1. The actual time windows used by cortical neurons for averaging activities or correlation of activities are yet to be determined.
Intracellular normalization
Neuronal plasticity may also be the outcome of intrinsic cellular mechanisms [39]. Based on theoretical grounds, it was suggested that there should be intracellular mechanisms that are directed at maintaining stability of mean firing rates under changing input conditions, by modifying synaptic weights [40] or by changing excitability [39]. Physiological findings support the suggestion that neuronal excitability may change as a consequence
581
582
Sensorv svstems
(a) Strengthening
Behaving
(b)
lo
Strengthening
factor
Non-behaving
10
factor
0.1
0.1 0.1
1
10
100
0.1
Contigency factor
Cc) Strengthening
1
10
100
Contigency factor
Behaving
10
factor
0.111 0.1
1
10
100
Contigency factor
Fig. 1. A quantification of learning rules for functional couplings. Functional plasticity was evaluated from the modifications of the strengths of correlations following cellular conditioning in behaving monkeys. Contingency factor (CF) = strength during-conditioning/strength-before-conditioning, ordinate; strengthening factor (SF) = strength after-conditioning/strength before-conditioning. (Note the log-log scale). Each symbol represents average values for several conditioning blocks of one neuronal pair. (a),(b) A first order approximation yields the following rule of plasticity: SF = CF 0.1s+o.aewhere B stands for the behavioral gating factor [B=l when the monkey is behaving (a) and B=O otherwise (b)]. Note that SF = Aw/w + 1. Adapted from [27]. (c) Dividing the CF range into three parts suggests: first, that the best-fit curve for potentiations (CF > 2) is shallow and does not cross the origin, suggesting that there may be a threshold for potentiation - once the threshold is reached, modifications depend less on the contingency factor; second, that in the range of 12; SF = 9.4-5OCF+107CF2-1 05CFQ4EKF4-8.2CFs, 0.5
of cellular or behavioral conditioning [41]. However, whether these mechanisms are aimed at maintaining output stability is questionable. Mechanisms that stabilize the firing rate may explain results from behavioral studies of adaptation and input deprivation. For example, adaptation to frequency motion biases judgment towards the opposite direction [42*], whereas artificial scotoma biases spatial judgments towards the scotoma [43*]. Behavioral biases, induced by both conditions, may derive from misinterpretation of a ‘readout’ process resulting from modifications of activities of some of its inputs, which outlast stimulus manipu-
lation. The modified inputs, in this case, are neurons that actively avoid extreme conditions of hyper- or hypoactivity and, thus, seek to maintain some output stability.
locus
of changes
Changes exhibited at the auditory cortex may be the consequence of changes within subcortical circuits, thalamocortical projections [44], or cortical, local [45*] or remote [46] connections. Naturally, these are not mutually exclusive options. In fact, recent evidence indicates
Plasticitv in auditorv cortical circuitrv Ahissar and Ahissar
that conditioning induces modifications at various areas along the lemniscal and extra-lemniscal path, including the ventral and dorsal cochlear nuclei [3,47*,48*]. Yet, the finding that subcortical stages modify does not indicate that modifications found at cortical level only reflect these peripheral changes. Indeed, in some cases, nonmonotonous degrees of changes are found along the auditory pathway [3,48*]. Furthermore, at least under some conditions, cortical changes can be induced faster than subcortical changes [49]. It is not clear whether modifications at the various stages are subject to the same behavioral control (or local rule of plasticity). For example, different levels may be subject to similar local factors but to different types of behavioral control: for bottom-up induced plasticity, the primary focus of plastic changes may be subcortical, whereas for top-down induced modulations, the primary focus is expected to be cortical. Such a distinction was in fact implied in suggestions that deafferentation results mostly in subcortical changes (as in [50*,51], but perhaps not in [52]; see [53] for a recent review), whereas behavioral training leads to cortical and/or thalamo-cortical modifications ([4”], reviewed in [54*]). (The allocation of classical conditioning to any of these two categories probably depends on the specific paradigm employed.) In particular, it was suggested that cortical plasticity might be important in inhibition of learned reflexive responses [55**], difficult frequency discrimination [4**,55**], attentive pattern discrimination [4**] or storage of records of events for ‘off-line’ use [55**]. Additional support for the suggested anatomical segregation comes from the analysis of response latencies. Strengthening of connections along the input pathways is expected, on the average, to decrease the minimal latency in cortical responses to preferred stimuli, whereas weakening of these connections is expected to increase it. Such an increase was indeed found following training on frequency discrimination [4**], but not following cochlear lesions [50*]. This behaviorally induced cortical re-tuning can be explained by relative strengthening of cortical connections on account of thalamo-cortical connections (by means of intrinsic regulating, or normalizing, mechanisms), so as to preserve output stability. In that case, cortico-cortical and thalamo-cortical connections will co-change, but generally in opposite directions.
Computational
assembly (e.g. [57]). Both types of changes will lead to behavioral improvement by means of increased reliability and shortening of required processing time. However, the most abundant change, presumably also related to unsupervised Hebbian mechanisms, namely expansion of representation, will not automatically lead to behavioral improvement. Obtaining behavioral benefits requires accompanying, probably supervised (i.e. directed by internal knowledge), update of the readout mechanism. Otherwise the induced shift of tuning curves will be misleading and not profitable, as is the case following adaptation or input deprivation. In fact, improvement may be achieved by task-specific optimization of the readout mechanism even without any changes in basic architecture [58*]. The implication that improvement requires task-specific behavioral control, even though architectural plasticity does not, is supported by behavioral findings [59*]. The suggestion that shifting tuning curves towards experimentally enhanced frequencies reflects unsupervised Hebbian mechanisms (probably influenced by general apriori behavioral relevance), whereas additional supervised mechanisms are involved in task-related processes, may account for apparent inconsistencies between studies of differential aversive conditioning. In one line of studies (reviewed in [60]), neurons increased their responses to the conditioned stimulus frequency, whereas in another study, neurons increased responses to fiequenties neighboring the conditioned stimulus but not to the conditioned stimulus frequency [8-l. This difference may reflect two different processes activated with each stimulus presentation: an initial unsupervised, stimulus-driven process, and a subsequent, behaviorally guided, supervised process. Indeed the first type of modification was found studying initial ‘on’ responses and the latter when longer delays were analyzed (the first 250ms). Interestingly, ‘on’ and ‘sustained’ response components were often found to be modified independently in the auditory cortex by either external stimuli [61] or cholinergic stimulation [62,63]. The suggestion that improvement of behavioral discrimination requires mechanisms operating with lOO200 ms latency from stimulus application, is supported by an EEG (electroencephalogram) auditory study [64**]. Practice-induced improvement of discrimination between a standard and a deviant complex auditory stimuli was correlated with an increase in the EEG component of 100-200ms, originating probably from the auditory cortex [65].
considerations
As long as cortical algorithms and representations are not known, their optimization by learning cannot be deduced (see discussion in [56]). However, some general considerations are still valid. Unsupervised Hebbian local mechanisms can enhance mean responses of single cells and also increase the coherence between activities of different neurons participating in the same processing
Behavioral
aspects
Behavioral control of physiological modificat%ns
Behavioral training induces modality-specific modulations in receptive field properties [4**]. However, the extent of this specificity (e.g. the degree of channel-
SRR
584
Sensow svstems
specificity) has not yet been studied. Furthermore, the relative significance of each of a variety of behavioral factors (e.g. apriori attention, stimulus-response reinforcement, and internal feed-back supervision) forming a behavioral context, in affecting neuronal plasticity, has not yet been studied. Modulations found following deafferentation while the animal is still anaesthetized, imply that under extreme conditions behavioral control may not be required (reviewed in [54*]), suggesting that its role, for at least some plastic changes, is modulatory and not permissive. The importance of behavioral context for neuronal plasticity was directly demonstrated at the intercellular level, studying functional plasticity [27]. Potentiations obtained by cellular conditioning when a monkey was behaving were on average six times stronger than those obtained by a similar conditioning when the monkey was not behaving (change of 180% versus 30%; Fig. la,b). Intriguingly, this ratio fits Thorndike’s estimate that “a single occurrence followed by reward strengthens a connection about six times as much as it would be strengthened by merely occurring” [66]. Although few neuronal pairs showed marked plasticity even under the non-behaving condition, all neuronal pairs recorded under both conditions exhibited stronger changes when the monkey was behaving. The behavioral control of plasticity may be mediated by some (or all) of the ascending diffuse systems, and, in particular, the cholinergic and noradrenergic ones. These systems could be activated under different behavioral conditions and consequently act on cortical synapses. Recent studies of the cholinergic system have shown that increasing acetylcholine (ACh) concentration, locally (by iontophoretic applications) or globally (by stimulating the cholinergic basal forebrain), induces immediate changes in auditory cortical responsiveness [62,63,67**,68,69*]. Pairing of ACh stimulation with a specific auditory stimulus often induces lasting specific changes in responsiveness [70], although probably only when strong enough cholinergic stimulation is applied [69*]. The polarity, magnitude and duration of these changes depend on many experimental parameters. These include the animal’s state of arousal ([71*] and see discussion in [62]), the intensity of ACh application [69*,71*,72], the fi-equency of the basal forebrain stimulation [67**], and, perhaps, even the cortical area (compare [73] with [70]). Furthermore, the polarity of the modifications outlasting the stimulation is not necessarily the same as the polarity during stimulation [69’,70,71’]. The diffuse nature of the cholinergic (and noradrenergic) system makes it a likely candidate for mediating general behavioral control but at the same time questions its sufficiency to generate the observed behavioral specificity. It is possible that specificity is the result of additional mechanisms. On the other hand, the puzzling diversity of cholinergic influences implies that the nature of its effects is far from being understood.
Behavioral expression of physiological modifications
Recanzone et al. [4”] found that expansion of cortical representation of the range of hequencies presented during training on frequency discrimination was correlated with behavioral improvement whereas other modifications were not. Edeline et al. [55**] found that physiological shifts of tuning curves are not sufficient to improve behavioral discrimination. It would be interesting to determine under what conditions frequencies that gain extra representation, for example, by deafferentation or by invasive manipulations, gain behavioral benefits. It would be of further interest to test the effect of preventing local cortical changes, possibly using invasive techniques, on behavioral improvement.
Conclusions Under a variety of experimental conditions there is an expansion and enhancement of representation of experimentally reinforced input parameters in both cortical and subcortical levels. These changes can be explained by unsupervised Hebbian local rules of plasticity. Indeed, recent studies suggest that a subgroup of the Hebbian rules, subject to behavioral control, operates in the auditory cortex. However, the relation between the cellular expression and intercellular rules of plasticity has not yet been tested directly. Furthermore, the behavioral consequences of these cellular modifications have not been characterized. Improving behavioral performance probably requires additional, supervised mechanisms, possibly focused at cortical levels. Some aspects of general behavioral control may be mediated by the cholinergic and/or noradrenergic systems. The embodiment of the specific behavioral control, including supervision and its relation to the cholinergic and noradrenergic systems, are at present far from being understood.
Acknowledgements We would
like to thank A Aertsen, M Segal, S Seung for remarks and D Shulz for extensive and invaluable discussions on the manuscript. E Ahissar is supported by the Alon Fellowship. helpful
and illuminating
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Woody CD, Wang X, Gruen E: Response to Acoustic Stimuli Increases in the Ventral Cochlear Nucleus After Stimulus Pairing. Neuroreport 1994, 5:513-515. The responsiveness of neurons of the ventral cochlear nucleus was modified after either backward (an unconditioned stimulus, glabella tap and hypothalamic electrical stimulation, precedes the conditioned stimulus, a click) or forward conditioning (an unconditioned stimulus follows the conditioned stimulus). Modulations were attributed to increased attentiveness. Time course of modifications was not described. 48. .
McIntosh AR, Gonzalez-Lima F: Network Analysis of Functional Auditory Pathways Mapped with Fluorodeoxyglucose: Associative Effects of a Tone Conditioned as a Pavlovian Ex. citor or Inhibitor. Brain Res 1993, 627:129-140. Two groups of rats were trained with a conditioned stimulus associated as either an excitor (included in reinforced trials) or as an inhibitor, respectively. Differential effects, measured with flurodeoxyglucose, indicate that multiple levels within the auditory system are subject to changes that depend on the associative significance of the stimulus. 49.
Oleson TD, Ashe JH, Weinberger NM: Modifications of Auditory and Somatosensory System Activity During Pupillary
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Seung HS, Sompolinski H: Simple Models for Reading Neuronal Population Codes. Proc Nat/ Acad Sci USA 1993, 90:10749-l 0753. An analysis of a linear network that improves discrimination by optimizing the ‘readout’ of a population vector. Discusses receptive field characteristics that yield maximal information for a given task. 59. .
Ahissar M, Hochstein 5: Attentional Control of Early Perceptual Learning. Proc Nat/ Acad Sci USA 1993, 90:571%5722. A psychophysical study of humans learning simple visual tasks. Demonstrates that intensive, spatially attentive training with a stimulus does not yield substantial improvement without task-specific selective attention. 60.
Weinberger NM: Learning-Induced Changes of Auditory Receptive Fields. Curr Opin Neurobiol 1993, 3:570-577.
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Ahissar M, Ahissar E, Bergman H, Vaadia E: Encoding of Sound Source and Movement: the Activity of Single Neurons and Interactions Between Adjpcent Neurons in the Primary Auditory Cortex of Monkeys. / Neurophysiol 1992, 67:203-215.
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Hars B, Maho C, Edeline JM, Hennevin E: Basal Forebrain Stimulation Facilitates Tone-Evoked Responses in the Auditory Cortex of Awake Rat. Neuroscience 1993, 56:61-74.
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McKenna TM, Ashe JH, Hui GK, Weinberger NM: Muscarinic Agonists Modulate Spontaneous and Evoked Unit Discharge in Auditory Cortex of Cat. Synapse 1988, 2z54-68.
64. ..
Naatanen R, Schroger E, Karakas 5, Tervanieme M, Paavilainen P: Development of a Memory Trace for a Complex Sound in the Human Brain. Neuroreporf 1993, 4:503-506. An EEG component, probably generated in the auditory cortex, appeared and increased during training on auditory discrimination task. This com-
Plasticity in auditory cortical circuitry Ahissar and Ahissar portent did not emerged when the same stimuli were merely presented to non-aware subjects. The same component was recorded from naive subjects that exhibited good performance before training.
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65.
Alho K, Huotilainen M, Tiitinen H, llmoniemi RJ, Knuutila J, Naatanen R: Memory-Related Processing of Complex Sound Patterns in Human Auditory Cortex: a MEG Study. Neuroreport 1993, 4391-394.
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Metherate R, Weinberger NM: Cholinergic Modulation of Re_ sponses to Single Tones Produces Tone-Specific Receptive Field Alterations in Cat Auditory Cortex. Synapse 1990, 6:133-l45.
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Metherate R, Ashe JH: Basal Forebrain Stimulation Modifies Auditory Cortex Responsiveness by an Action at Muscarinic Receptors. Brain Res 1991, 559:163-l 67.
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E Ahissar, Center
for Brain
Research,
ogy, The Weizmann Institute, Rehovot
Department
76100,
of Neurobiol-
Israel.
M Ahissar, Center for Neural Computation, Department of Neurobiology, The Hebrew University, Jerusalem 91904, Israel.
.587
Institute,
Recent the
studies
short-
and
the primary behavioral between
have focused long-term
auditory
factors operating
Rehovot and The Hebrew
on the mechanisms
plasticity
cortex of adults.
at the cellular and local
levels.
that
following
observed
Current
This
Opinion
system, which could mediate
manipulation.
The direct behavioral consequences of the extended representation have not been extensively studied. The nearly ubiquitous expansion (although see [S*]) is puzzling and raises several questions as to its cause, on the one hand, and its consequences, on the other. For example, is it induced by a common local rule of plasticity operating at the intercellular level? Is it necessary and/or sufficient for behavioral improvement?
1994,
580
changes
additional
4:580-587
rules of plasticity
Intercellular temporal contingency
Increased cortical representations of experimentally enhanced input frequencies can be explained by learning mechanisms based on the Hebbian principle operating at the intercellular level. The original Hebb rule [9], combined with Stent’s extension [lo], suggests that a positive contingency between the increased activities of the pre- and postsynaptic neurons will strengthen their connection, whereas a negative contingency will weaken it. According to this rule, input synapses conveying the enhanced range of inputs will strengthen, given that the postsynaptic cell is activated by these stimuli. Quiescent synapses, exhibiting in this case negative correlation with the output cell, will weaken. Consequently, tuning curves of postsynaptic cells will shifi in favor of the enhanced stimulus range expanding its representation (for further discussion see, e.g. [11,12*,13*]). However, none of the experiments demonstrating tuning shifts measured intercellular temporal contingency directly. Several studies (in visual and motor cortices) manipulated local contingency between a postsynaptic unit and an external or thalamic stimulation, applying invasive techniques [14-201. These studies were highly informative in indicating that temporal contingency was necessary to induce modulations. However, as the effect of the intracranial stimulation was not well defined, and other synapses, e.g. cholinergic and noradrenergic, may have also been stimulated, these studies could not determine whether temporal contingency was sufficient. Furthermore, not having recorded a specific pre- and
Ahbreviations ACh-acetylcholine;
driven
However,
required to generate behavioral
local
Data collected during the past years indicate that adult auditory cortex (including the primary cortex) is capable of undergoing substantial plasticity. The most prominent change, found under different behavioral conditions and using a variety of recording techniques, is a specific increase in the extent of cortical representation of the experimentally enhanced range of tone f?equenties: a fast establishment of a conditioned fear response to pure tone is accompanied by a tist [l’], long-lasting [2*] shifi of neuronal characteristic fi-equencies towards the frequency of the conditioned stimulus. Consequently, the representation of this frequency (measured with fluorodeoxyglucose) is increased [3]; improvement in (pure tone) frequency discrimination is correlated with a longterm increase in the representation of the whole range of stimulating frequencies [4*-l - although training to discriminate between spectral envelopes mainly sharpens neuronal tuning curves (D Keeling, K Krueger, B Calhoun, CE Schreiner, abstract 17:22, Assoc Res Otolaryngol, St. Petersburg Beach, Florida, February 1994). Frequency-specific deafferentation also induces shortand long-term expansion of the cortical representation of spared neighboring frequencies [5,6,7*].
gated unsupervised
has not yet been tested directly.
in Neurobiology
Introduction
have examined
levels, the effects of global
A behaviorally
are probably
suggestion
yielding
Israel
responses in
neuronal
most of the bottom-up
sensory
learning mechanisms
improvement.
and conditions
investigations
states and the role of the cholinergic the global
rule can explain
supervised
These
Jerusalem,
by
and intercellular
Hebbian-covariance were
exhibited
University,
EEG-electroencephalogram.
0 Current Biology Ltd ISSN 0959-4388
Plasticity in auditory cortical circuitry Ahissar and Ahissar
postsynaptic neuronal pair, a quantitative study of the required temporal contingency was not possible. Simultaneous recording of two neurons in the behaving mammal is possible only when recorded extracellularly. A quantitative analysis of the nature of correlated activity yielding neuronal plasticity can be obtained applying cross-correlation analysis to the extracellularly recorded activities. This correlation reflects the statistical efficacy of the coupling, induced by direct, but also by indirect, synaptic connectivity. It is therefore termed functional coupling [21]. It can be shown that functional coupling describes activity covariance, mutual information and surprise functions. The competence of these functions in describing neural coding was supported by both theoretical accounts [22-261 and physiological findings [23,27-301. A novel approach utilizes measures of lasting changes in the functional coupling to determine functional plasticity [27]. Plastic changes of functional couplings serve as an estimate of neuronal plasticity. Using this approach it was shown that functional plasticity is indeed guided by changes in the temporal contingency between the activities of the coupled cells: when conditioning increased the temporal covariance, the coupling was potentiated, whereas when it decreased the covariance, the coupling was depressed [27]. When the covariance was not affected by the conditioning (e.g. during pseudo-conditioning), the strength of the coupling was, in most cases, not changed (Fig. la).
Negative generally
feedback operating on the synaptic weight was formulated as follows [34] :
Aw = [g(x)c- WIh(y), where w is the synaptic function.
weight
and h is a monotonic
This formulation predicts, firstly, that there should be a negative correlation between pre-conditioning weights and the magnitude of changes induced by conditioning, and, secondly, that at steady-state, there should be a positive correlation between weights and presynaptic activity levels. None of these three predictions was confirmed by analysis of functional plasticity [27,38]. The finding that weights were not correlated with activities of either the pre- or postsynaptic neuron, and weights changes were not correlated with changes of either activity, suggests that the negative feedback operates on a different factor. This factor may be the correlated activity itself, as in the following form: Aw = g(%y- c %y >), where t denotes a ‘trace’ of the pre-synaptic activity [35] on a scale of a few milliseconds, and c > denotes averaging over a longer time window, on the scale of a few minutes.
These findings partially support the covariance formulation of temporal contingency [22]. However, functional coupling was modified by the change in covariance and not by its absolute value (see also [31]). Thus, the reference level around which coupling was strengthened or depressed was the preconditioning, steady-state level and not zero covariance (expected only when there is no coupling). Such a reference level is beneficial as it overcomes the ‘runaway’ problem [32-351 embedded in the covariance formulation: excitatory synapses, inducing positive covariance, would be indefinitely facilitated towards some saturation value.
Nonetheless, a multiplicative dependency should also be considered, as it is suggested by experimental findings that the fractional change, rather than the differential change, was negatively correlated with the pre-conditioning strength [38]:
Learning rules that address the runaway problem incorporate a negative feedback component: changes of synaptic weights are inversely related to either the present synaptic weights or to the expected postsynaptic activities (reviewed in [34], and for classical conditioning in [35-371). Negative feedback operating on the postsynaptic activity was in general formulated as follows:
Note that we tested rules derived for synaptic weights using results of functional couplings. While it is clear that these two measures should be strongly related, this testing could be refined either by deriving predictions of models in the functional domain or by translating these findings to the synaptic domain.
Aw = g(x)c (Y- < Y >I, where Aw is the change in synaptic weight, x and y denote the activities of the pre- and postsynaptic neurons, respectively, c is a constant, g is a monotonic function and
Awlw
= g(%y<%y>)-
1.
The function g can be approximated by experimental data, e.g. see Fig. 1. The actual time windows used by cortical neurons for averaging activities or correlation of activities are yet to be determined.
Intracellular normalization
Neuronal plasticity may also be the outcome of intrinsic cellular mechanisms [39]. Based on theoretical grounds, it was suggested that there should be intracellular mechanisms that are directed at maintaining stability of mean firing rates under changing input conditions, by modifying synaptic weights [40] or by changing excitability [39]. Physiological findings support the suggestion that neuronal excitability may change as a consequence
581
582
Sensorv svstems
(a) Strengthening
Behaving
(b)
lo
Strengthening
factor
Non-behaving
10
factor
0.1
0.1 0.1
1
10
100
0.1
Contigency factor
Cc) Strengthening
1
10
100
Contigency factor
Behaving
10
factor
0.111 0.1
1
10
100
Contigency factor
Fig. 1. A quantification of learning rules for functional couplings. Functional plasticity was evaluated from the modifications of the strengths of correlations following cellular conditioning in behaving monkeys. Contingency factor (CF) = strength during-conditioning/strength-before-conditioning, ordinate; strengthening factor (SF) = strength after-conditioning/strength before-conditioning. (Note the log-log scale). Each symbol represents average values for several conditioning blocks of one neuronal pair. (a),(b) A first order approximation yields the following rule of plasticity: SF = CF 0.1s+o.aewhere B stands for the behavioral gating factor [B=l when the monkey is behaving (a) and B=O otherwise (b)]. Note that SF = Aw/w + 1. Adapted from [27]. (c) Dividing the CF range into three parts suggests: first, that the best-fit curve for potentiations (CF > 2) is shallow and does not cross the origin, suggesting that there may be a threshold for potentiation - once the threshold is reached, modifications depend less on the contingency factor; second, that in the range of 1
of cellular or behavioral conditioning [41]. However, whether these mechanisms are aimed at maintaining output stability is questionable. Mechanisms that stabilize the firing rate may explain results from behavioral studies of adaptation and input deprivation. For example, adaptation to frequency motion biases judgment towards the opposite direction [42*], whereas artificial scotoma biases spatial judgments towards the scotoma [43*]. Behavioral biases, induced by both conditions, may derive from misinterpretation of a ‘readout’ process resulting from modifications of activities of some of its inputs, which outlast stimulus manipu-
lation. The modified inputs, in this case, are neurons that actively avoid extreme conditions of hyper- or hypoactivity and, thus, seek to maintain some output stability.
locus
of changes
Changes exhibited at the auditory cortex may be the consequence of changes within subcortical circuits, thalamocortical projections [44], or cortical, local [45*] or remote [46] connections. Naturally, these are not mutually exclusive options. In fact, recent evidence indicates
Plasticitv in auditorv cortical circuitrv Ahissar and Ahissar
that conditioning induces modifications at various areas along the lemniscal and extra-lemniscal path, including the ventral and dorsal cochlear nuclei [3,47*,48*]. Yet, the finding that subcortical stages modify does not indicate that modifications found at cortical level only reflect these peripheral changes. Indeed, in some cases, nonmonotonous degrees of changes are found along the auditory pathway [3,48*]. Furthermore, at least under some conditions, cortical changes can be induced faster than subcortical changes [49]. It is not clear whether modifications at the various stages are subject to the same behavioral control (or local rule of plasticity). For example, different levels may be subject to similar local factors but to different types of behavioral control: for bottom-up induced plasticity, the primary focus of plastic changes may be subcortical, whereas for top-down induced modulations, the primary focus is expected to be cortical. Such a distinction was in fact implied in suggestions that deafferentation results mostly in subcortical changes (as in [50*,51], but perhaps not in [52]; see [53] for a recent review), whereas behavioral training leads to cortical and/or thalamo-cortical modifications ([4”], reviewed in [54*]). (The allocation of classical conditioning to any of these two categories probably depends on the specific paradigm employed.) In particular, it was suggested that cortical plasticity might be important in inhibition of learned reflexive responses [55**], difficult frequency discrimination [4**,55**], attentive pattern discrimination [4**] or storage of records of events for ‘off-line’ use [55**]. Additional support for the suggested anatomical segregation comes from the analysis of response latencies. Strengthening of connections along the input pathways is expected, on the average, to decrease the minimal latency in cortical responses to preferred stimuli, whereas weakening of these connections is expected to increase it. Such an increase was indeed found following training on frequency discrimination [4**], but not following cochlear lesions [50*]. This behaviorally induced cortical re-tuning can be explained by relative strengthening of cortical connections on account of thalamo-cortical connections (by means of intrinsic regulating, or normalizing, mechanisms), so as to preserve output stability. In that case, cortico-cortical and thalamo-cortical connections will co-change, but generally in opposite directions.
Computational
assembly (e.g. [57]). Both types of changes will lead to behavioral improvement by means of increased reliability and shortening of required processing time. However, the most abundant change, presumably also related to unsupervised Hebbian mechanisms, namely expansion of representation, will not automatically lead to behavioral improvement. Obtaining behavioral benefits requires accompanying, probably supervised (i.e. directed by internal knowledge), update of the readout mechanism. Otherwise the induced shift of tuning curves will be misleading and not profitable, as is the case following adaptation or input deprivation. In fact, improvement may be achieved by task-specific optimization of the readout mechanism even without any changes in basic architecture [58*]. The implication that improvement requires task-specific behavioral control, even though architectural plasticity does not, is supported by behavioral findings [59*]. The suggestion that shifting tuning curves towards experimentally enhanced frequencies reflects unsupervised Hebbian mechanisms (probably influenced by general apriori behavioral relevance), whereas additional supervised mechanisms are involved in task-related processes, may account for apparent inconsistencies between studies of differential aversive conditioning. In one line of studies (reviewed in [60]), neurons increased their responses to the conditioned stimulus frequency, whereas in another study, neurons increased responses to fiequenties neighboring the conditioned stimulus but not to the conditioned stimulus frequency [8-l. This difference may reflect two different processes activated with each stimulus presentation: an initial unsupervised, stimulus-driven process, and a subsequent, behaviorally guided, supervised process. Indeed the first type of modification was found studying initial ‘on’ responses and the latter when longer delays were analyzed (the first 250ms). Interestingly, ‘on’ and ‘sustained’ response components were often found to be modified independently in the auditory cortex by either external stimuli [61] or cholinergic stimulation [62,63]. The suggestion that improvement of behavioral discrimination requires mechanisms operating with lOO200 ms latency from stimulus application, is supported by an EEG (electroencephalogram) auditory study [64**]. Practice-induced improvement of discrimination between a standard and a deviant complex auditory stimuli was correlated with an increase in the EEG component of 100-200ms, originating probably from the auditory cortex [65].
considerations
As long as cortical algorithms and representations are not known, their optimization by learning cannot be deduced (see discussion in [56]). However, some general considerations are still valid. Unsupervised Hebbian local mechanisms can enhance mean responses of single cells and also increase the coherence between activities of different neurons participating in the same processing
Behavioral
aspects
Behavioral control of physiological modificat%ns
Behavioral training induces modality-specific modulations in receptive field properties [4**]. However, the extent of this specificity (e.g. the degree of channel-
SRR
584
Sensow svstems
specificity) has not yet been studied. Furthermore, the relative significance of each of a variety of behavioral factors (e.g. apriori attention, stimulus-response reinforcement, and internal feed-back supervision) forming a behavioral context, in affecting neuronal plasticity, has not yet been studied. Modulations found following deafferentation while the animal is still anaesthetized, imply that under extreme conditions behavioral control may not be required (reviewed in [54*]), suggesting that its role, for at least some plastic changes, is modulatory and not permissive. The importance of behavioral context for neuronal plasticity was directly demonstrated at the intercellular level, studying functional plasticity [27]. Potentiations obtained by cellular conditioning when a monkey was behaving were on average six times stronger than those obtained by a similar conditioning when the monkey was not behaving (change of 180% versus 30%; Fig. la,b). Intriguingly, this ratio fits Thorndike’s estimate that “a single occurrence followed by reward strengthens a connection about six times as much as it would be strengthened by merely occurring” [66]. Although few neuronal pairs showed marked plasticity even under the non-behaving condition, all neuronal pairs recorded under both conditions exhibited stronger changes when the monkey was behaving. The behavioral control of plasticity may be mediated by some (or all) of the ascending diffuse systems, and, in particular, the cholinergic and noradrenergic ones. These systems could be activated under different behavioral conditions and consequently act on cortical synapses. Recent studies of the cholinergic system have shown that increasing acetylcholine (ACh) concentration, locally (by iontophoretic applications) or globally (by stimulating the cholinergic basal forebrain), induces immediate changes in auditory cortical responsiveness [62,63,67**,68,69*]. Pairing of ACh stimulation with a specific auditory stimulus often induces lasting specific changes in responsiveness [70], although probably only when strong enough cholinergic stimulation is applied [69*]. The polarity, magnitude and duration of these changes depend on many experimental parameters. These include the animal’s state of arousal ([71*] and see discussion in [62]), the intensity of ACh application [69*,71*,72], the fi-equency of the basal forebrain stimulation [67**], and, perhaps, even the cortical area (compare [73] with [70]). Furthermore, the polarity of the modifications outlasting the stimulation is not necessarily the same as the polarity during stimulation [69’,70,71’]. The diffuse nature of the cholinergic (and noradrenergic) system makes it a likely candidate for mediating general behavioral control but at the same time questions its sufficiency to generate the observed behavioral specificity. It is possible that specificity is the result of additional mechanisms. On the other hand, the puzzling diversity of cholinergic influences implies that the nature of its effects is far from being understood.
Behavioral expression of physiological modifications
Recanzone et al. [4”] found that expansion of cortical representation of the range of hequencies presented during training on frequency discrimination was correlated with behavioral improvement whereas other modifications were not. Edeline et al. [55**] found that physiological shifts of tuning curves are not sufficient to improve behavioral discrimination. It would be interesting to determine under what conditions frequencies that gain extra representation, for example, by deafferentation or by invasive manipulations, gain behavioral benefits. It would be of further interest to test the effect of preventing local cortical changes, possibly using invasive techniques, on behavioral improvement.
Conclusions Under a variety of experimental conditions there is an expansion and enhancement of representation of experimentally reinforced input parameters in both cortical and subcortical levels. These changes can be explained by unsupervised Hebbian local rules of plasticity. Indeed, recent studies suggest that a subgroup of the Hebbian rules, subject to behavioral control, operates in the auditory cortex. However, the relation between the cellular expression and intercellular rules of plasticity has not yet been tested directly. Furthermore, the behavioral consequences of these cellular modifications have not been characterized. Improving behavioral performance probably requires additional, supervised mechanisms, possibly focused at cortical levels. Some aspects of general behavioral control may be mediated by the cholinergic and/or noradrenergic systems. The embodiment of the specific behavioral control, including supervision and its relation to the cholinergic and noradrenergic systems, are at present far from being understood.
Acknowledgements We would
like to thank A Aertsen, M Segal, S Seung for remarks and D Shulz for extensive and invaluable discussions on the manuscript. E Ahissar is supported by the Alon Fellowship. helpful
and illuminating
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E Ahissar, Center
for Brain
Research,
ogy, The Weizmann Institute, Rehovot
Department
76100,
of Neurobiol-
Israel.
M Ahissar, Center for Neural Computation, Department of Neurobiology, The Hebrew University, Jerusalem 91904, Israel.
.587