Biomedical subjects

Adam Kepecs

Publications and source records attributed to Adam Kepecs.

7 recordsLinked to original sources

PubMed2006

Seeing at a glance, smelling in a whiff: rapid forms of perceptual decision making.

Intuitively, decisions should always improve with more time for the accumulation of evidence, yet psychophysical data show a limit of 200-300 ms for many perceptual tasks. Here, we consider mechanisms that favour such rapid information processing in vision and olfaction. We suggest that the brain limits some types of perceptual processing to short, discrete chunks (for example, eye fixations and sniffs) in order to facilitate the construction of global sensory images.

Animals↗

PubMed2005

The sniff as a unit of olfactory processing.

Sniffing is a rhythmic motor process essential for the acquisition of olfactory information. Recent behavioral experiments show that using a single sniff rats can accurately discriminate between very similar odors and fail to improve their accuracy by taking multiple sniffs. This implies that each sniff has the potential to provide a complete snapshot of the local olfactory environment. The discrete and intermittent nature of sniffing has implications beyond the physical process of odor capture as it strongly shapes the flow of information into the olfactory system. We review electrophysiological studies-primarily from anesthetized rodents-demonstrating that olfactory neural responses are coupled to respiration. Hence, the "sniff cycle" might play a role in odor coding, by allowing the timing of spikes with respect to the phase of the respiration cycle to encode information about odor identity or concentration. We also discuss behavioral and physiological results indicating that sniffing can be dynamically coordinated with other rhythmic behaviors, such as whisking, as well as with rhythmic neural activity, such as hippocampal theta oscillations. Thus, the sniff cycle might also facilitate the coordination of the olfactory system with other brain areas. These converging lines of empirical data support the notion that each sniff is a unit of olfactory processing relevant for both neural coding and inter-areal coordination. Further electrophysiological recordings in behaving animals will be necessary to assess these proposals.

Animals↗

PubMed2003

Basic mechanisms for graded persistent activity: discrete attractors, continuous attractors, and dynamic representations.

Persistent neural activity is observed in many systems, and is thought to be a neural substrate for holding memories over time delays of a few seconds. Recent work has addressed two issues. First, how can networks of neurons robustly hold such an active memory? Computer systems obtain significant robustness to noise by approximating analogue quantities with discrete digital representations. In a similar manner, theoretical models of persistent activity in spiking neurons have shown that the most robust and stable way to store the short-term memory of a continuous parameter is to approximate it with a discrete representation. This general idea applies very broadly to mechanisms that range from biochemical networks to single cells and to large circuits of neurons. Second, why is it commonly observed that persistent activity in the cortex can be strongly time-varying? This observation is almost ubiquitous, and therefore must be taken into account in our models and our understanding of how short-term memories are held in the cortex.

Animals↗

PubMed2003

Information encoding and computation with spikes and bursts.

Neurons compute and communicate by transforming synaptic input patterns into output spike trains. The nature of this transformation depends crucially on the properties of voltage-gated conductances in neuronal membranes. These intrinsic membrane conductances can enable neurons to generate different spike patterns including brief, high-frequency bursts that are commonly observed in a variety of brain regions. Here we examine how the membrane conductances that generate bursts affect neural computation and encoding. We simulated a bursting neuron model driven by random current input signal and superposed noise. We consider two issues: the timing reliability of different spike patterns and the computation performed by the neuron. Statistical analysis of the simulated spike trains shows that the timing of bursts is much more precise than the timing of single spikes. Furthermore, the number of spikes per burst is highly robust to noise. Next we considered the computation performed by the neuron: how different features of the input current are mapped into specific output spike patterns. Dimensional reduction and statistical classification techniques were used to determine the stimulus features triggering different firing patterns. Our main result is that spikes, and bursts of different durations, code for different stimulus features, which can be quantified without a priori assumptions about those features. These findings lead us to propose that the biophysical mechanisms of spike generation enables individual neurons to encode different stimulus features into distinct spike patterns.

Computer Simulation↗

PubMed2002

Bursting neurons signal input slope.

Brief bursts of high-frequency action potentials represent a common firing mode of pyramidal neurons, and there are indications that they represent a special neural code. It is therefore of interest to determine whether there are particular spatial and temporal features of neuronal inputs that trigger bursts. Recent work on pyramidal cells indicates that bursts can be initiated by a specific spatial arrangement of inputs in which there is coincident proximal and distal dendritic excitation (Larkum et al., 1999). Here we have used a computational model of an important class of bursting neurons to investigate whether there are special temporal features of inputs that trigger bursts. We find that when a model pyramidal neuron receives sinusoidally or randomly varying inputs, bursts occur preferentially on the positive slope of the input signal. We further find that the number of spikes per burst can signal the magnitude of the slope in a graded manner. We show how these computations can be understood in terms of the biophysical mechanism of burst generation. There are several examples in the literature suggesting that bursts indeed occur preferentially on positive slopes (Guido et al., 1992; Gabbiani et al., 1996). Our results suggest that this selectivity could be a simple consequence of the biophysics of burst generation. Our observations also raise the possibility that neurons use a burst duration code useful for rapid information transmission. This possibility could be further examined experimentally by looking for correlations between burst duration and stimulus variables.

Action Potentials↗

PubMed2002

Spike-timing-dependent plasticity: common themes and divergent vistas.

Recent experimental observations of spike-timing-dependent synaptic plasticity (STDP) have revitalized the study of synaptic learning rules. The most surprising aspect of these experiments lies in the observation that synapses activated shortly after the occurrence of a postsynaptic spike are weakened. Thus, synaptic plasticity is sensitive to the temporal ordering of pre- and postsynaptic activation. This temporal asymmetry has been suggested to underlie a range of learning tasks. In the first part of this review we highlight some of the common themes from a range of findings in the framework of predictive coding. As an example of how this principle can be used in a learning task, we discuss a recent model of cortical map formation. In the second part of the review, we point out some of the differences in STDP models and their functional consequences. We discuss how differences in the weight-dependence, the time-constants and the non-linear properties of learning rules give rise to distinct computational functions. In light of these computational issues raised, we review current experimental findings and suggest further experiments to resolve some controversies.

Action Potentials↗

PubMed2002

Model for a robust neural integrator.

Integrator circuits in the brain show persistent firing that reflects the sum of previous excitatory and inhibitory inputs from external sources. Integrator circuits have been implicated in parametric working memory, decision making and motor control. Previous work has shown that stable integrator function can be achieved by an excitatory recurrent neural circuit, provided synaptic strengths are tuned with extreme precision (better than 1% accuracy). Here we show that integrator circuits can function without fine tuning if the neuronal units have bistable properties. Two specific mechanisms of bistability are analyzed, one based on local recurrent excitation, and the other on the voltage-dependence of the NMDA (N-methyl-D-aspartate) channel. Neither circuit requires fine tuning to perform robust integration, and the latter actually exploits the variability of neuronal conductances.

Computer Simulation↗