motion biases judgment towards the opposite direction. [42*], whereas artificial scotoma biases spatial judgments towards the scotoma [43*]. Behavioral biases ...
Plasticity in auditory cortical circuitry Ehud Ahissar and Merav Ahissar The Weizmann
Institute,
Recent the
studies
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and
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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?
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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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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*].
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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)-
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
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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 2; SF = 9.4-5OCF+107CF2-1 05CFQ4EKF4-8.2CFs, 0.5
