Formation of temporal-feature maps by axonal propagation of synaptic learning

Computational maps are of central importance to a neuronal representation of the outside world. In a map, neighboring neurons respond to similar sensory features. A well studied example is the computational map of interaural time differences (ITDs), which is essential to sound localization in a variety of species and allows resolution of ITDs of the order of 10 μs. Nevertheless, it is unclear how such an orderly representation of temporal features arises. We address this problem by modeling the ontogenetic development of an ITD map in the laminar nucleus of the barn owl. We show how the owl's ITD map can emerge from a combined action of homosynaptic spike-based Hebbian learning and its propagation along the presynaptic axon. In spike-based Hebbian learning, synaptic strengths are modified according to the timing of pre- and postsynaptic action potentials. In unspecific axonal learning, a synapse's modification gives rise to a factor that propagates along the presynaptic axon and affects the properties of synapses at neighboring neurons. Our results indicate that both Hebbian learning and its presynaptic propagation are necessary for map formation in the laminar nucleus, but the latter can be orders of magnitude weaker than the former. We argue that the algorithm is important for the formation of computational maps, when, in particular, time plays a key role.

Song selectivity and sensorimotor signals in vocal learning and production

Bird song, like human speech, is a learned vocal behavior that requires auditory feedback. Both as juveniles, while they learn to sing, and as adults, songbirds use auditory feedback to compare their own vocalizations with an internal model of a target song. Here we describe experiments that explore a role for the songbird anterior forebrain pathway (AFP), a basal ganglia-forebrain circuit, in evaluating song feedback and modifying vocal output. First, neural recordings in anesthetized, juvenile birds show that single AFP neurons are specialized to process the song stimuli that are compared during sensorimotor learning. AFP neurons are tuned to both the bird's own song and the tutor song, even when these stimuli are manipulated to be very different from each other. Second, behavioral experiments in adult birds demonstrate that lesions to the AFP block the deterioration of song that normally follows deafening. This observation suggests that deafening results in an instructive signal, indicating a mismatch between feedback and the internal song model, and that the AFP is involved in generating or transmitting this instructive signal. Finally, neural recordings from behaving birds reveal robust singing-related activity in the AFP. This activity is likely to originate from premotor areas and could be modulated by auditory feedback of the bird's own voice. One possibility is that this activity represents an efference copy, predicting the sensory consequences of motor commands. Overall, these studies illustrate that sensory and motor processes are highly interrelated in this circuit devoted to vocal learning, as is true for brain areas involved in speech.

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.

Feedback circuitry within a song-learning pathway.

The song system of birds consists of several neural pathways. One of these, the anterior forebrain pathway, is necessary for the acquisition but not for the production of learned song in zebra finches. It has been shown that the anterior forebrain pathway sequentially connects the following nuclei: the high vocal center, area X of lobus parolfactorius, the medial portion of the dorsolateral thalamic nucleus, the lateral magnocellular nucleus of anterior neostriatum (IMAN), and the robust nucleus of the archistriatum (RA). We now show in zebra finches (Taeniopygia guttata) that IMAN cells that project to RA also project to area X, forming a feedback loop within the anterior forebrain pathway. The axonal endings of the IMAN projection into area X form cohesive and distinct domains. Small injections of tracer in subregions of area X backfill a spatially restricted subset of cells in IMAN, that, in turn, send projections to RA that are arranged in horizontal layers, which may correspond to the functional representation of vocal tract muscles demonstrated by others. We infer from our data that there is a myotopic representation throughout the anterior forebrain pathway. In addition, we suggest that the parcellation of area X into smaller domains by the projection from IMAN highlights a functional architecture within X, which might correspond to units of motor control, to the representation of acoustic features of song, or both.