The role of spike timing in the coding of stimulus location in rat somatosensory cortex

Although the timing of single spikes is known to code for time-varying features of a sensory stimulus, it remains unclear whether time is also exploited in the neuronal coding of the spatial structure of the environment, where nontemporal stimulus features are fundamental. This report demonstrates that, in the whisker representation of rat cortex, precise spike timing of single neurons increases the information transmitted about stimulus location by 44%, compared to that transmitted only by the total number of spikes. Crucial to this code is the timing of the first spike after whisker movement. Complex, single neuron spike patterns play a smaller, synergistic role. Timing permits very few spikes to transmit high quantities of information about a behaviorally significant, spatial stimulus.     

Beyond spike timing: the role of nonlinear plasticity and unreliable synapses

 Spike-timing-dependent plasticity (STDP) strengthens synapses that are activated immediately before a postsynaptic spike, and weakens those that are activated after a spike. To prevent an uncontrolled growth of the synaptic strengths, weakening must dominate strengthening for uncorrelated spike times. However, this weight-normalization property would preclude Hebbian potentiation when the pre- and postsynaptic neurons are strongly active without specific spike-time correlations. We show that nonlinear STDP as inherent in the data of Markram et al. [(1997) Science 275:213–215] can preserve the benefits of both weight normalization and Hebbian plasticity, and hence can account for learning based on spike-time correlations and on mean firing rates. As examples we consider the moving-threshold property of the Bienenstock–Cooper–Munro rule, the development of direction-selective simple cells by changing short-term synaptic depression, and the joint adaptation of axonal and dendritic delays. Without threshold nonlinearity at low frequencies, the development of direction selectivity does not stabilize in a natural stimulation environment. Without synaptic unreliability there is no causal development of axonal and dendritic delays. Received: 22 April 2002 / Accepted: 23 May 2002 Acknowledgements. This study was supported by the Swiss National Science Foundation (grant 3152-065234.01) and the Silva-Casa foundation. The author thanks Stefano Fusi, Henry Markram, and Misha Tsodyks for helpful discussions, Nissim Buchs and Martin Schneider for their simulations, and Jan Reutimann for proof reading. Correspondence to: e-mail: wsenn@cns.unibe.ch, Tel.: +41-31-6318721, Fax: 41-31-6314611     

Reliability of spike and burst firing in thalamocortical relay cells

The reliability and precision of the timing of spikes in a spike train is an important aspect of neuronal coding. We investigated reliability in thalamocortical relay (TCR) cells in the acute slice and also in a Morris-Lecar model with several extensions. A frozen Gaussian noise current, superimposed on a DC current, was injected into the TCR cell soma. The neuron responded with spike trains that showed trial-to-trial variability, due to amongst others slow changes in its internal state and the experimental setup. The DC current allowed to bring the neuron in different states, characterized by a well defined membrane voltage (between ?80 and ?50 mV) and by a specific firing regime that on depolarization gradually shifted from a predominantly bursting regime to a tonic spiking regime. The filtered frozen white noise generated a spike pattern output with a broad spike interval distribution. The coincidence factor and the Hunter and Milton measure were used as reliability measures of the output spike train. In the experimental TCR cell as well as the Morris-Lecar model cell the reliability depends on the shape (steepness) of the current input versus spike frequency output curve. The model also allowed to study the contribution of three relevant ionic membrane currents to reliability: a T-type calcium current, a cation selective h-current and a calcium dependent potassium current in order to allow bursting, investigate the consequences of a more complex current-frequency relation and produce realistic firing rates. The reliability of the output of the TCR cell increases with depolarization. In hyperpolarized states bursts are more reliable than single spikes. The analytically derived relations were capable to predict several of the experimentally recorded spike features.     

Reading spike timing without a clock: intrinsic decoding of spike trains

The precise timing of action potentials of sensory neurons relative to the time of stimulus presentation carries substantial sensory information that is lost or degraded when these responses are summed over longer time windows. However, it is unclear whether and how downstream networks can access information in precise time-varying neural responses. Here, we review approaches to test the hypothesis that the activity of neural populations provides the temporal reference frames needed to decode temporal spike patterns. These approaches are based on comparing the single-trial stimulus discriminability obtained from neural codes defined with respect to network-intrinsic reference frames to the discriminability obtained from codes defined relative to the experimenter''s computer clock. Application of this formalism to auditory, visual and somatosensory data shows that information carried by millisecond-scale spike times can be decoded robustly even with little or no independent external knowledge of stimulus time. In cortex, key components of such intrinsic temporal reference frames include dedicated neural populations that signal stimulus onset with reliable and precise latencies, and low-frequency oscillations that can serve as reference for partitioning extended neuronal responses into informative spike patterns.     

Regulation of spike timing in visual cortical circuits

A train of action potentials (a spike train) can carry information in both the average firing rate and the pattern of spikes in the train. But can such a spike-pattern code be supported by cortical circuits? Neurons in vitro produce a spike pattern in response to the injection of a fluctuating current. However, cortical neurons in vivo are modulated by local oscillatory neuronal activity and by top-down inputs. In a cortical circuit, precise spike patterns thus reflect the interaction between internally generated activity and sensory information encoded by input spike trains. We review the evidence for precise and reliable spike timing in the cortex and discuss its computational role.     

Interneurons, spike timing, and perception.

Rhythmic gamma oscillations at 30-70 Hz in cortical and hippocampal slices depend on a maintained excitation and on interactions between interneurons and pyramidal cells. These interactions include gap-junctional connections between inhibitory cells and fast excitatory and inhibitory chemical synapses. Spike timing with precision in the range of several ms may be assured by biphasic signaling mechanisms operating at these different connections. Such temporal precision may be important in cognitive processing.     

Decoding spike timing: the differential reverse-correlation method

It is widely acknowledged that detailed timing of action potentials is used to encode information, for example, in auditory pathways; however, the computational tools required to analyze encoding through timing are still in their infancy. We present a simple example of encoding, based on a recent model of time-frequency analysis, in which units fire action potentials when a certain condition is met, but the timing of the action potential depends also on other features of the stimulus. We show that, as a result, spike-triggered averages are smoothed so much that they do not represent the true features of the encoding. Inspired by this example, we present a simple method, differential reverse correlations, that can separate an analysis of what causes a neuron to spike, and what controls its timing. We analyze with this method the leaky integrate-and-fire neuron and show the method accurately reconstructs the model's kernel.     

Phenomenological models of synaptic plasticity based on spike timing

Synaptic plasticity is considered to be the biological substrate of learning and memory. In this document we review phenomenological models of short-term and long-term synaptic plasticity, in particular spike-timing dependent plasticity (STDP). The aim of the document is to provide a framework for classifying and evaluating different models of plasticity. We focus on phenomenological synaptic models that are compatible with integrate-and-fire type neuron models where each neuron is described by a small number of variables. This implies that synaptic update rules for short-term or long-term plasticity can only depend on spike timing and, potentially, on membrane potential, as well as on the value of the synaptic weight, or on low-pass filtered (temporally averaged) versions of the above variables. We examine the ability of the models to account for experimental data and to fulfill expectations derived from theoretical considerations. We further discuss their relations to teacher-based rules (supervised learning) and reward-based rules (reinforcement learning). All models discussed in this paper are suitable for large-scale network simulations.     

Dynamic shifting in thalamocortical processing during different behavioural states

Recent experiments in our laboratory have indicated that as rats shift the behavioural strategy employed to explore their surrounding environment, there is a parallel change in the physiological properties of the neuronal ensembles that define the main thalamocortical loop of the trigeminal somatosensory system. Based on experimental evidence from several laboratories, we propose that this concurrent shift in behavioural strategy and thalamocortical physiological properties provides rats with an efficient way to optimize either the detection or analysis of complex tactile stimuli.     

The role of synaptic facilitation in spike coincidence detection

Using a realistic model of activity dependent dynamical synapse, which includes both depressing and facilitating mechanisms, we study the conditions in which a postsynaptic neuron efficiently detects temporal coincidences of spikes which arrive from N different presynaptic neurons at certain frequency f. A numerical and analytical treatment of that system shows that: (1) facilitation enhances the detection of correlated signals arriving from a subset of presynaptic excitatory neurons, and (2) the presence of facilitation yields to a better detection of firing rate changes in the presynaptic activity. We also observed that facilitation determines the existence of an optimal input frequency which allows the best performance for a wide (maximum) range of the neuron firing threshold. This optimal frequency can be controlled by means of facilitation parameters. Finally, we show that these results are robust even for very noisy signals and in the presence of synaptic fluctuations produced by the stochastic release of neurotransmitters.     

Precision of intracellular calcium spike timing in primary rat hepatocytes

Extracellular stimuli are often encoded in the frequency, amplitude and duration of spikes in the intracellular concentration of calcium ([Ca2+]i). However, the timing of individual [Ca2+]i-spikes in relation to the dynamics of an extracellular stimulus is still an open question. To address this question, we use a systems biology approach combining experimental and theoretical methods. Using computer simulations, we predict that more naturalistic pulsed stimuli generate precisely-timed [Ca2+]i-spikes in contrast to the application of constant stimuli of the same dose. These computational results are confirmed experimentally in single primary rat hepatocytes upon alpha1-adrenergic stimulation. Hormonal signalling in analogy to neuronal signalling thus has the potential to make use of temporal coding on the level of single cells. The [Ca2+]i-signalling cascade provides a first example for increasing the information capacity of an intracellular regulatory signal beyond the known coding mechanisms of amplitude (AM) and frequency modulation (FM).     

Unsupervised learning of visual features through spike timing dependent plasticity

Spike timing dependent plasticity (STDP) is a learning rule that modifies synaptic strength as a function of the relative timing of pre- and postsynaptic spikes. When a neuron is repeatedly presented with similar inputs, STDP is known to have the effect of concentrating high synaptic weights on afferents that systematically fire early, while postsynaptic spike latencies decrease. Here we use this learning rule in an asynchronous feedforward spiking neural network that mimics the ventral visual pathway and shows that when the network is presented with natural images, selectivity to intermediate-complexity visual features emerges. Those features, which correspond to prototypical patterns that are both salient and consistently present in the images, are highly informative and enable robust object recognition, as demonstrated on various classification tasks. Taken together, these results show that temporal codes may be a key to understanding the phenomenal processing speed achieved by the visual system and that STDP can lead to fast and selective responses.     

The role of seasonal timing and phenological shifts for species coexistence

Shifts in the phenologies of coexistence species are altering the temporal structure of natural communities worldwide. However, predicting how these changes affect the structure and long‐term dynamics of natural communities is challenging because phenology and coexistence theory have largely proceeded independently. Here, I propose a conceptual framework that incorporates seasonal timing of species interactions into a well‐studied competition model to examine how changes in phenologies influence long‐term dynamics of natural communities. Using this framework I demonstrate that persistence and coexistence conditions strongly depend on the difference in species’ mean phenologies and how this difference varies across years. Consequently, shifts in mean and interannual variation in relative phenologies of species can fundamentally alter the outcome of interactions and the potential for persistence and coexistence of competing species. These effects can be predicted by how per‐capita effects scale with differences in species’ phenologies. I outline how this approach can be parameterized with empirical systems and discuss how it fits within the context of current coexistence theory. Overall, this synthesis reveals that phenology of species interactions can play a crucial yet currently understudied role in driving coexistence and biodiversity patterns in natural systems and determine how species will respond to future climate change.     

Bursts shape the NMDA-R mediated spike timing dependent plasticity curve: role of burst interspike interval and GABAergic inhibition

Spike timing dependent plasticity (STDP) is a synaptic learning rule where the relative timing between the presynaptic and postsynaptic action potentials determines the sign and strength of synaptic plasticity. In its basic form STDP has an asymmetric form which incorporates both persistent increases and persistent decreases in synaptic strength. The basic form of STDP, however, is not a fixed property and depends on the dendritic location. An asymmetric curve is observed in the distal dendrites, whereas a symmetrical one is observed in the proximal ones. A recent computational study has shown that the transition from the asymmetry to symmetry is due to inhibition under certain conditions. Synapses have also been observed to be unreliable at generating plasticity when excitatory postsynaptic potentials and single spikes are paired at low frequencies. Bursts of spikes, however, are reliably signaled because transmitter release is facilitated. This article presents a two-compartment model of the CA1 pyramidal cell. The model is neurophysiologically plausible with its dynamics resulting from the interplay of many ionic and synaptic currents. Plasticity is measured by a deterministic Ca²⁺ dynamics model which measures the instantaneous calcium level and its time course in the dendrite and change the strength of the synapse accordingly. The model is validated to match the asymmetrical form of STDP from the pairing of a presynaptic (dendritic) and postsynaptic (somatic) spikes as observed experimentally. With the parameter set unchanged the model investigates how pairing of bursts with single spikes and bursts in the presence or absence of inhibition shapes the STDP curve. The model predicts that inhibition strength and frequency are not the only factors of the asymmetry-to-symmetry switch of the STDP curve. Burst interspike interval is another factor. This study is an important first step towards understanding how STDP is affected under natural firing patterns in vivo.     

Cortical processing and perceived timing

As of yet, it is unclear how we determine relative perceived timing. One controversial suggestion is that timing perception might be related to when analyses are completed in the cortex of the brain. An alternate proposal suggests that perceived timing is instead related to the point in time at which cortical analyses commence. Accordingly, timing illusions should not occur owing to cortical analyses, but they could occur if there were differential delays between signals reaching cortex. Resolution of this controversy therefore requires that the contributions of cortical processing be isolated from the influence of subcortical activity. Here, we have done this by using binocular disparity changes, which are known to be detected via analyses that originate in cortex. We find that observers require longer stimulus exposures to detect small, relative to larger, disparity changes; observers are slower to react to smaller disparity changes and observers misperceive smaller disparity changes as being perceptually delayed. Interestingly, disparity magnitude influenced perceived timing more dramatically than it did stimulus change detection. Our data therefore suggest that perceived timing is both influenced by cortical processing and is shaped by sensory analyses subsequent to those that are minimally necessary for stimulus change perception.     

Predicting spike timing of neocortical pyramidal neurons by simple threshold models

Neurons generate spikes reliably with millisecond precision if driven by a fluctuating current—is it then possible to predict the spike timing knowing the input? We determined parameters of an adapting threshold model using data recorded in vitro from 24 layer 5 pyramidal neurons from rat somatosensory cortex, stimulated intracellularly by a fluctuating current simulating synaptic bombardment in vivo. The model generates output spikes whenever the membrane voltage (a filtered version of the input current) reaches a dynamic threshold. We find that for input currents with large fluctuation amplitude, up to 75% of the spike times can be predicted with a precision of ±2 ms. Some of the intrinsic neuronal unreliability can be accounted for by a noisy threshold mechanism. Our results suggest that, under random current injection into the soma, (i) neuronal behavior in the subthreshold regime can be well approximated by a simple linear filter; and (ii) most of the nonlinearities are captured by a simple threshold process.     

Democratic organization of the thalamocortical neural ensembles in nociceptive signal processing

Acute pain is a warning protective sensation for any impending harm. However, chronic pain syndromes are often resistant diseases that may consume large amount of health care costs. It has been suggested by recent studies that pain perception may be formed in central neural networks via large-scale coding processes, which involves sensory, affective, and cognitive dimensions. Many central areas are involved in these processes, including structures from the spinal cord, the brain stem, the limbic system, to the cortices. Thus, chronic painful diseases may be the result of some abnormal coding within this network. A thorough investigation of coding mechanism of pain within the central neuromatrix will bring us great insight into the mechanisms responsible for the development of chronic pain, hence leading to novel therapeutic interventions for pain management.     

EPSP amplification and the precision of spike timing in hippocampal neurons

The temporal precision with which EPSPs initiate action potentials in postsynaptic cells determines how activity spreads in neuronal networks. We found that small EPSPs evoked from just subthreshold potentials initiated firing with short latencies in most CA1 hippocampal inhibitory cells, while action potential timing in pyramidal cells was more variable due to plateau potentials that amplified and prolonged EPSPs. Action potential timing apparently depends on the balance of subthreshold intrinsic currents. In interneurons, outward currents dominate responses to somatically injected EPSP waveforms, while inward currents are larger than outward currents close to threshold in pyramidal cells. Suppressing outward potassium currents increases the variability in latency of synaptically induced firing in interneurons. These differences in precision of EPSP-spike coupling in inhibitory and pyramidal cells will enhance inhibitory control of the spread of excitation in the hippocampus.