Hebbian-type Rules Research Articles

Synaptic Plasticity in Cortical Inhibitory Neurons: What Mechanisms May Help to Balance Synaptic Weight Changes?

Inhibitory neurons play a fundamental role in the normal operation of neuronal networks. Diverse types of inhibitory neurons serve vital functions in cortical networks, such as balancing excitation and taming excessive activity, organizing neuronal activity in spatial and temporal patterns, and shaping response selectivity. Serving these, and a multitude of other functions effectively requires fine-tuning of inhibition, mediated by synaptic plasticity. Plasticity of inhibitory systems can be mediated by changes at inhibitory synapses and/or by changes at excitatory synapses at inhibitory neurons. In this review, we consider that latter locus: plasticity at excitatory synapses to inhibitory neurons. Despite the fact that plasticity of excitatory synaptic transmission to interneurons has been studied in much less detail than in pyramids and other excitatory cells, an abundance of forms and mechanisms of plasticity have been observed in interneurons. Specific requirements and rules for induction, while exhibiting a broad diversity, could correlate with distinct sources of excitatory inputs and distinct types of inhibitory neurons. One common requirement for the induction of plasticity is the rise of intracellular calcium, which could be mediated by a variety of ligand-gated, voltage-dependent, and intrinsic mechanisms. The majority of the investigated forms of plasticity can be classified as Hebbian-type associative plasticity. Hebbian-type learning rules mediate adaptive changes of synaptic transmission. However, these rules also introduce intrinsic positive feedback on synaptic weight changes, making plastic synapses and learning networks prone to runaway dynamics. Because real inhibitory neurons do not express runaway dynamics, additional plasticity mechanisms that counteract imbalances introduced by Hebbian-type rules must exist. We argue that weight-dependent heterosynaptic plasticity has a number of characteristics that make it an ideal candidate mechanism to achieve homeostatic regulation of synaptic weight changes at excitatory synapses to inhibitory neurons.

Partial Breakdown of Input Specificity of STDP at Individual Synapses Promotes New Learning.

Hebbian-type learning rules, which underlie learning and refinement of neuronal connectivity, postulate input specificity of synaptic changes. However, theoretical analyses have long appreciated that additional mechanisms, not restricted to activated synapses, are needed to counteract positive feedback imposed by Hebbian-type rules on synaptic weight changes and to achieve stable operation of learning systems. The biological basis of such mechanisms has remained elusive. Here we show that, in layer 2/3 pyramidal neurons from slices of visual cortex of rats, synaptic changes induced at individual synapses by spike timing-dependent plasticity do not strictly follow the input specificity rule. Spike timing-dependent plasticity is accompanied by changes in unpaired synapses: heterosynaptic plasticity. The direction of heterosynaptic changes is weight-dependent, with balanced potentiation and depression, so that the total synaptic input to a cell remains preserved despite potentiation or depression of individual synapses. Importantly, this form of heterosynaptic plasticity is induced at unpaired synapses by the same pattern of postsynaptic activity that induces homosynaptic changes at paired synapses. In computer simulations, we show that experimentally observed heterosynaptic plasticity can indeed serve the theoretically predicted role of robustly preventing runaway dynamics of synaptic weights and activity. Moreover, it endows model neurons and networks with essential computational features: enhancement of synaptic competition, facilitation of the development of specific intrinsic connectivity, and the ability for relearning. We conclude that heterosynaptic plasticity is an inherent property of plastic synapses, crucial for normal operation of learning systems. We show that spike timing-dependent plasticity in L2/L3 pyramids from rat visual cortex is accompanied by plastic changes in unpaired synapses. These heterosynaptic changes are weight-dependent and balanced: individual synapses expressed significant LTP or LTD, but the average over all synapses did not change. Thus, the rule of input specificity breaks down at individual synapses but holds for responses averaged over many inputs. In model neurons and networks, this experimentally characterized form of heterosynaptic plasticity prevents runaway dynamics of synaptic weights and activity, enhances synaptic competition, facilitates development of specific intrinsic connectivity, and enables relearning. This new form of heterosynaptic plasticity represents the cellular basis of a theoretically postulated mechanism, which is additional to Hebbian-type rules, and is necessary for stable operation of learning systems.

Heterosynaptic plasticity induced by intracellular tetanization in layer 2/3 pyramidal neurons in rat auditory cortex

Associative Hebbian-type synaptic plasticity underlies the mechanisms of learning and memory; however, Hebbian learning rules lead to runaway dynamics of synaptic weights and lack mechanisms for synaptic competition.Heterosynaptic plasticity may solve these problems by complementing plasticity at synapses that were active during the induction, with opposite-sign changes at non-activated synapses. In visual cortex, a potential candidate mechanism for normalization is plasticity induced by a purely postsynaptic protocol, intracellular tetanization. Here we asked if intracellular tetanization can induce long-term plasticity in auditory cortex. We recorded excitatory postsynaptic potentials (EPSPs) of regular (n =76) and all-or-none (n =24) type in layer 2/3 pyramidal cells in slices from rat auditory cortex. After intracellular tetanization, 32 of 76 regular inputs (42%) showed long-term depression, 21 inputs (28%) showed potentiation and 23 inputs (30%) did not change. The direction of plasticity correlated with the initial release probability: inputs with initially low release probability tended to be potentiated, while inputs with high release probability tended to be depressed. Thus, intracellular tetanization had a normalizing effect on synaptic efficacy. Induction of plasticity by intracellular tetanization required a rise of intracellular [Ca(2+)], because it was impaired by chelating intracellular calcium with EGTA. The long-term changes induced by intracellular tetanization involved both pre and postsynaptic mechanisms. EPSP amplitude changes were correlated with changes of release indices: paired-pulse ratio and the inverse of the coefficient of variation (CV(-2)). Furthermore at some all-or-none synapses, changes of averaged response amplitude were correlated with a change of the failure rate, without a change of the synaptic potency, measured as averaged amplitude of successful responses. Presynaptic components of plastic changes were abolished in experiments with blockade of NO-synthesis and spread, indicating involvement of NO signalling. These results demonstrate that the ability of purely postsynaptic challenges to induce plasticity is a general property of pyramidal neurons of both auditory and visual cortices.

Autonomous development of decorrelation filters in neural networks with recurrent inhibition

We perform a quantitative analysis of information processing in a simple neural network model with recurrent inhibition. We postulate that both excitatory and inhibitory synapses continually adapt according to the following Hebbian-type rules: for excitatory synapses correlated pre- and post-synaptic activity induces enhanced excitation; for inhibitory synapses it induces enhanced inhibition. Following synaptic equilibration in unsupervised learning processes, the model is found to perform a novel type of principal-component analysis which involves filtering and decorrelation. In the light of these results we discuss the possible role of the granule-/Golgi-cell subnetwork in the cerebellum.