Showing papers by "J. Leo van Hemmen published in 2012"
TL;DR: This letter demonstrates how spike-timing-dependent plasticity (STDP) can give rise to frequency-dependent learning, thus leading to an input selectivity that enables frequency identification and finds that synaptic delays play a crucial role in organizing the weight specialization induced by STDP.
Abstract: Periodic neuronal activity has been observed in various areas of the brain, from lower sensory to higher cortical levels. Specific frequency components contained in this periodic activity can be identified by a neuronal circuit that behaves as a bandpass filter with given preferred frequency, or best modulation frequency (BMF). For BMFs typically ranging from 10 to 200i¾ Hz, a plausible and minimal configuration consists of a single neuron with adjusted excitatory and inhibitory synaptic connections. The emergence, however, of such a neuronal circuitry is still unclear. In this letter, we demonstrate how spike-timing-dependent plasticity (STDP) can give rise to frequency-dependent learning, thus leading to an input selectivity that enables frequency identification. We use an in-depth mathematical analysis of the learning dynamics in a population of plastic inhibitory connections. These provide inhomogeneous postsynaptic responses that depend on their dendritic location. We find that synaptic delays play a crucial role in organizing the weight specialization induced by STDP. Under suitable conditions on the synaptic delays and postsynaptic potentials (PSPs), the BMF of a neuron after learning can match the training frequency. In particular, proximal (distal) synapses with shorter (longer) dendritic delay and somatically measured PSP time constants respond better to higher (lower) frequencies. As a result, the neuron will respond maximally to any stimulating frequency (in a given range) with which it has been trained in an unsupervised manner. The model predicts that synapses responding to a given BMF form clusters on dendritic branches.
18 citations
TL;DR: It is demonstrated that SN afferents respond in an extremely precise manner and with high reproducibility across a broad frequency band (10-150 Hz), revealing that an optimal decoder would need to rely extensively on a temporal code.
Abstract: Fish and aquatic frogs detect minute water motion by means of a specialized mechanosensory system, the lateral line. Ubiquitous in fish, the lateral-line system is characterized by hair-cell based sensory structures across the fish's surface called neuromasts. These neuromasts occur free-standing on the skin as superficial neuromasts (SN) or are recessed into canals as canal neuromasts. SNs respond to rapid changes of water velocity in a small layer of fluid around the fish, including the so-called boundary layer. Although omnipresent, the boundary layer's impact on the SN response is still a matter of debate. For the first time using an information-theoretic approach to this sensory system, we have investigated the SN afferents encoding capabilities. Combining covariance analysis, phase analysis, and modeling of recorded neuronal responses of primary lateral line afferents, we show that encoding by the SNs is adequately described as a linear, velocity-responsive mechanism. Afferent responses display a bimodal distribution of opposite Wiener kernels that likely reflected the two hair-cell populations within a given neuromast. Using frozen noise stimuli, we further demonstrate that SN afferents respond in an extremely precise manner and with high reproducibility across a broad frequency band (10-150 Hz), revealing that an optimal decoder would need to rely extensively on a temporal code. This was further substantiated by means of signal reconstruction of spike trains that were time shifted with respect to their original. On average, a time shift of 3.5 ms was enough to diminish the encoding capabilities of primary afferents by 70%. Our results further demonstrate that the SNs' encoding capability is linearly related to the stimulus outside the boundary layer, and that the boundary layer can, therefore, be neglected while interpreting lateral line response of SN afferents to hydrodynamic stimuli.
12 citations
TL;DR: Animals are quite efficient at discerning “objects” that appear in their surroundings, exceeding by far what technical systems have been able to replicate.
Abstract: Animals are quite efficient at discerning “objects” that appear in their surroundings, exceeding by far what technical systems have been able to replicate. Evolution has equipped animals with several modalities such as vision, hearing, and haptics to exploit different physical means of information representation: electromagnetic spectrum (vision), pressure waves (hearing), forces (haptics, tactile sensing), gravity (vestibular system), hydrodynamic velocity and pressure field (lateral-line system), etc. It is precisely the combination of these different sensing modalities that allows biological systems to attain their high level of accuracy and dependability in sensing their surroundings. A few words on the notions of multimodal and multisensory are in order (Stein et al 2010). A lateral line, for instance, consists of many sensors (neuromasts) all over the body. The conglomeration of all neuromasts allows a fish, say, a pike, to localize or even determine the shape of a roach in its neighborhood. Only through the total input of all neuromasts can the pike accomplish the localization task with the lateral line as a truly “multisensory” modality. In clear water the pike can then both “see” the roach and perceive it through its lateral-line system, a multimodal experience. How do all these modalities, with their specific, physically different transmission techniques, function? How does the brain generate “objects” as neuronal representations