103 research outputs found
Precisely Timed Signal Transmission in Neocortical Networks with Reliable Intermediate-Range Projections
The mammalian neocortex has a remarkable ability to precisely reproduce behavioral sequences or to reliably retrieve stored information. In contrast, spiking activity in behaving animals shows a considerable trial-to-trial variability and temporal irregularity. The signal propagation and processing underlying these conflicting observations is based on fundamental neurophysiological processes like synaptic transmission, signal integration within single cells, and spike formation. Each of these steps in the neuronal signaling chain has been studied separately to a great extend, but it has been difficult to judge how they interact and sum up in active sub-networks of neocortical cells. In the present study, we experimentally assessed the precision and reliability of small neocortical networks consisting of trans-columnar, intermediate-range projections (200ā1000āĪ¼m) on a millisecond time-scale. Employing photo-uncaging of glutamate in acute slices, we activated a number of distant presynaptic cells in a spatio-temporally precisely controlled manner, while monitoring the resulting membrane potential fluctuations of a postsynaptic cell. We found that signal integration in this part of the network is highly reliable and temporally precise. As numerical simulations showed, the residual membrane potential variability can be attributed to amplitude variability in synaptic transmission and may significantly contribute to trial-to-trial output variability of a rate signal. However, it does not impair the temporal accuracy of signal integration. We conclude that signals from intermediate-range projections onto neocortical neurons are propagated and integrated in a highly reliable and precise manner, and may serve as a substrate for temporally precise signal transmission in neocortical networks
Sequential Sparsening by Successive Adaptation in Neural Populations
In the principal cells of the insect mushroom body, the Kenyon cells (KC),
olfactory information is represented by a spatially and temporally sparse code.
Each odor stimulus will activate only a small portion of neurons and each
stimulus leads to only a short phasic response following stimulus onset
irrespective of the actual duration of a constant stimulus. The mechanisms
responsible for the sparse code in the KCs are yet unresolved.
Here, we explore the role of the neuron-intrinsic mechanism of
spike-frequency adaptation (SFA) in producing temporally sparse responses to
sensory stimulation in higher processing stages. Our single neuron model is
defined through a conductance-based integrate-and-fire neuron with
spike-frequency adaptation [1]. We study a fully connected feed-forward network
architecture in coarse analogy to the insect olfactory pathway. A first layer
of ten neurons represents the projection neurons (PNs) of the antenna lobe. All
PNs receive a step-like input from the olfactory receptor neurons, which was
realized by independent Poisson processes. The second layer represents 100 KCs
which converge onto ten neurons in the output layer which represents the
population of mushroom body extrinsic neurons (ENs).
Our simulation result matches with the experimental observations. In
particular, intracellular recordings of PNs show a clear phasic-tonic response
that outlasts the stimulus [2] while extracellular recordings from KCs in the
locust express sharp transient responses [3]. We conclude that the
neuron-intrinsic mechanism is can explain a progressive temporal response
sparsening in the insect olfactory system. Further experimental work is needed
to test this hypothesis empirically.
[1] Muller et. al., Neural Comput, 19(11):2958-3010, 2007. [2] Assisi et.
al., Nat Neurosci, 10(9):1176-1184, 2007. [3] Krofczik et. al. Front. Comput.
Neurosci., 2(9), 2009.Comment: 5 pages, 2 figures, This manuscript was submitted for review to the
Eighteenth Annual Computational Neuroscience Meeting CNS*2009 in Berlin and
accepted for oral presentation at the meetin
Rapid Odor Processing in the Honeybee Antennal Lobe Network
In their natural environment, many insects need to identify and evaluate behaviorally relevant odorants on a rich and dynamic olfactory background. Behavioral studies have demonstrated that bees recognize learned odors within <200āms, indicating a rapid processing of olfactory input in the sensory pathway. We studied the role of the honeybee antennal lobe network in constructing a fast and reliable code of odor identity using in vivo intracellular recordings of individual projection neurons (PNs) and local interneurons (LNs). We found a complementary ensemble code where odor identity is encoded in the spatio-temporal pattern of response latencies as well as in the pattern of activated and inactivated PN firing. This coding scheme rapidly reaches a stable representation within 50ā150āms after stimulus onset. Testing an odor mixture versus its individual compounds revealed different representations in the two morphologically distinct types of lateral- and median PNs (l- and m-PNs). Individual m-PNs mixture responses were dominated by the most effective compound (elemental representation) whereas l-PNs showed suppressed responses to the mixture but not to its individual compounds (synthetic representation). The onset of inhibition in the membrane potential of l-PNs coincided with the responses of putative inhibitory interneurons that responded significantly faster than PNs. Taken together, our results suggest that processing within the LN network of the AL is an essential component of constructing the antennal lobe population code
Neural representation of a spatial odor memory in the honeybee mushroom body
Nawrot MP, D'Albis T, Menzel R, Strube-Bloss M. Neural representation of a spatial odor memory in the honeybee mushroom body. BMC Neuroscience. 2015;16(S1): P240
Critical Song Features for Auditory Pattern Recognition in Crickets
Many different invertebrate and vertebrate species use acoustic communication
for pair formation. In the cricket Gryllus bimaculatus, females recognize
their species-specific calling song and localize singing males by positive
phonotaxis. The song pattern of males has a clear structure consisting of
brief and regular pulses that are grouped into repetitive chirps. Information
is thus present on a short and a long time scale. Here, we ask which
structural features of the song critically determine the phonotactic
performance. To this end we employed artificial neural networks to analyze a
large body of behavioral data that measured femalesā phonotactic behavior
under systematic variation of artificially generated song patterns. In a first
step we used four non-redundant descriptive temporal features to predict the
female response. The model prediction showed a high correlation with the
experimental results. We used this behavioral model to explore the integration
of the two different time scales. Our result suggested that only an attractive
pulse structure in combination with an attractive chirp structure reliably
induced phonotactic behavior to signals. In a further step we investigated all
feature sets, each one consisting of a different combination of eight proposed
temporal features. We identified feature sets of size two, three, and four
that achieve highest prediction power by using the pulse period from the short
time scale plus additional information from the long time scale
Beyond the Cortical Column: Abundance and Physiology of Horizontal Connections Imply a Strong Role for Inputs from the Surround
Current concepts of cortical information processing and most cortical network models largely rest on the assumption that well-studied properties of local synaptic connectivity are sufficient to understand the generic properties of cortical networks. This view seems to be justified by the observation that the vertical connectivity within local volumes is strong, whereas horizontally, the connection probability between pairs of neurons drops sharply with distance. Recent neuroanatomical studies, however, have emphasized that a substantial fraction of synapses onto neocortical pyramidal neurons stems from cells outside the local volume. Here, we discuss recent findings on the signal integration from horizontal inputs, showing that they could serve as a substrate for reliable and temporally precise signal propagation. Quantification of connection probabilities and parameters of synaptic physiology as a function of lateral distance indicates that horizontal projections constitute a considerable fraction, if not the majority, of inputs from within the cortical network. Taking these non-local horizontal inputs into account may dramatically change our current view on cortical information processing
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