Synaptic strength depresses for low and potentiates for high activation of the postsynaptic neuron. This feature is a key property of the Bienenstock-Cooper-Munro (BCM) synaptic learning rule, which has been shown to maximize the selectivity of the postsynaptic neuron, and thereby offers a possible explanation for experience-dependent cortical plasticity such as orientation selectivity. However, the BCM framework is rate-based and a significant amount of recent work has shown that synaptic plasticity also depends on the precise timing of presynaptic and postsynaptic spikes. Here we consider a triplet model of spike-timing-dependent plasticity (STDP) that depends on the interactions of three precisely timed spikes. Triplet STDP has been shown to describe plasticity experiments that the classical STDP rule, based on pairs of spikes, has failed to capture. In the case of ratebased patterns, we show a tight correspondence between the triplet STDP rule and the BCM rule. We analytically demonstrate the selectivity property of the triplet STDP rule for orthogonal inputs and perform numerical simulations for nonorthogonal inputs. Moreover, in contrast to BCM, we show that triplet STDP can also induce selectivity for input patterns consisting of higher-order spatiotemporal correlations, which exist in natural stimuli and have been measured in the brain. We show that this sensitivity to higher-order correlations can be used to develop direction and speed selectivity. S ynaptic plasticity depends on the activity of presynaptic and postsynaptic neurons and is believed to provide the basis for learning and memory (1, 2). It has been shown that low-frequency stimulation (1-3 Hz) (3) or stimulation paired with low postsynaptic depolarization (4) induces synaptic long-term depression (LTD), whereas synapses undergo long-term potentiation (LTP) after high-frequency stimulation (100 Hz) (5). Such findings are consistent with the well-known Bienenstock-Cooper-Munro (BCM) learning rule (6). This BCM model has been shown to elicit orientation selectivity and other aspects of experience-dependent cortical plasticity (6, 7). Furthermore, in this model the modification threshold between LTP and LTD varies as a function of the history of postsynaptic activity, a prediction that has been confirmed experimentally (8).Despite its consistency with experimental data and its functional relevance, the BCM framework is still limited experimentally and functionally. Experimentally, because the learning rule is expressed in terms of firing rates, it cannot predict synaptic modification on the basis of the timing of pre-and postsynaptic spikes (9, 10). This form of plasticity, called spike-timing-dependent plasticity (STDP), uses the timing of spike pairs to induce synaptic modification (11,12). The presynaptic spike is required to shortly precede the postsynaptic spike to elicit LTP, whereas the reverse timing of pre-and postsynaptic spikes leads to LTD (9, 10). Functionally, the BCM model cannot segregate input patterns that are characterized by...

In many sensory systems, the neural signal splits into multiple parallel pathways. For example, in the mammalian retina, ϳ20 types of retinal ganglion cells transmit information about the visual scene to the brain. The purpose of this profuse and early pathway splitting remains unknown. We examine a common instance of splitting into ON and OFF neurons excited by increments and decrements of light intensity in the visual scene, respectively. We test the hypothesis that pathway splitting enables more efficient encoding of sensory stimuli. Specifically, we compare a model system with an ON and an OFF neuron to one with two ON neurons. Surprisingly, the optimal ON-OFF system transmits the same information as the optimal ON-ON system, if one constrains the maximal firing rate of the neurons. However, the ON-OFF system uses fewer spikes on average to transmit this information. This superiority of the ON-OFF system is also observed when the two systems are optimized while constraining their mean firing rate. The efficiency gain for the ON-OFF split is comparable with that derived from decorrelation, a well known processing strategy of early sensory systems. The gain can be orders of magnitude larger when the ecologically important stimuli are rare but large events of either polarity. The ON-OFF system also provides a better code for extracting information by a linear downstream decoder. The results suggest that the evolution of ON-OFF diversification in sensory systems may be driven by the benefits of lowering average metabolic cost, especially in a world in which the relevant stimuli are sparse.

Highlights d Auditory cortex is selectively required for complex stimulus threat memory d Adjacent temporal association cortex controls all forms of auditory threat memory d Cortico-amygdala information transmission governs complex stimulus memory d Amygdala-projecting neurons show population plasticity for stimulus discrimination

Despite advances in experimental and theoretical neuroscience, we are still trying to identify key biophysical details that are important for characterizing the operation of brain circuits. Biological mechanisms at the level of single neurons and synapses can be combined as ‘building blocks’ to generate circuit function. We focus on the importance of capturing multiple timescales when describing these intrinsic and synaptic components. Whether inherent in the ionic currents, the neuron’s complex morphology, or the neurotransmitter composition of synapses, these multiple timescales prove crucial for capturing the variability and richness of circuit output and enhancing the information-carrying capacity observed across nervous systems.