this paper, we examine in detail the potential role of time as a variable in the combinatorial representation of sensory stimuli by this part of the brain. We focus on processing of odors to which the animal has been exposed, i.e., we do not consider here the responses evoked by the first one to three presentations of an unfamiliar odor. We find that the temporal firing patterns of individual neurons, as well as the synchronization of firing across groups of neurons, are stimulus specific. In other words, each odor appears to be represented not simply by an ensemble of synchronized neurons but by a progressive and odor-specific transformation of that ensemble, so that each neuron synchronizes with several others only during one or more precise epochs of the ensemble response. We, thus, propose that oscillations in the olfactory nervous system occur, at least here, in parallel with a slower code that is distributed both in time and across many neurons

Single neurons generate action potentials that express their output in pulse frequencies, so that sensory stimuli can be microscopically expressed as spatial patterns of phase-locked firing of "feature detector" neurons. The visual, auditory, somatic, and olfactory cortices generate dendritic potentials that oscillate at frequencies from 1-100 Hz. These waves reveal macroscopic activity arising from synaptic interactions of millions of neurons. They share a spatially coherent oscillation as a "carrier", by which spatial patterns of amplitude modulation (AM) are transmitted in distinctive configurations, when subjects receive sensory stimuli they have learned to discriminate. These spatial AM patterns are unique to each subject, are not invariant with respect to stimuli, and cannot be derived from the stimuli by logical operations. The "carrier" is aperiodic, usually dispersed over a wide spectral range. Our simulations of the "carrier" indicate that its dynamics is chaotic, and that sequential patterns are freshly constructed during perception, because chaotic systems can create as well as destroy information. The entire experience of a subject, which is embedded in synaptic connections in cortex that were modified during learning, can be brought instantly to bear at each state transition by which a new construction is initiated. It is suggested that "feature binding" revealed by microscopic recording is related to the formation of a "chaotic construct" early in the process of perception.

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Objectives: The time series of single trial cortical evoked potentials typically have a random appearance, and their trial-to-trial variability is commonly explained by a model in which random ongoing background noise activity is linearly combined with a stereotyped evoked response. In this paper, we demonstrate that more realistic models, incorporating amplitude and latency variability of the evoked response itself, can explain statistical properties of cortical potentials that have often been attributed to stimulus-related changes in functional connectivity or other intrinsic neural parameters.

distribution; power spectral density PSD; The ECoG background activity of cerebral cortex in states of rest and slow wave sleep resembles broadband noise. The power spectral density (PSD) then may often conform to a power-law distribution: a straight line in coordinates of log power vs. log frequency. The exponent, x, of the distribution, 1/f x, ranges between 2 and 4. These findings are explained with a model of the neural source of the background activity in mutual excitation among pyramidal cells. The dendritic response of a population of interactive excitatory neurons to an impulse input is a rapid exponential rise and a slow exponential decay, which can be fitted with the sum of two exponential terms. When that function is convolved as the kernel with pulses from a Poisson process and summed, the resulting “brown ” or “black noise conforms to the ECoG time series and the PSD in rest and sleep. The PSD slope is dependent on the rate of rise. The variation in the observed slope is attributed to variation in the level of the background activity that is homeostatically regulated by the refractory periods of the excitatory neurons. Departures in behavior from rest and sleep to action are accompanied by local peaks in the PSD, which manifest emergent nonrandom structure in the ECoG, and which prevent reliable estimation of the 1/f x exponents in active states. We conclude that the resting ECoG truly is lowdimensional noise, and that the resting state is an optimal starting point for defining and measuring both artifactual and physiological structures emergent in the activated ECoG. 1.

Identifying and defining the characteristic features of the inhibitory neurons in the nervous system has become essential for achieving a cellular understanding of complex brain activities. For this, the olfactory bulb is ideally suited because it is readily accessible, it is a laminated structure where local interneurons can be easily distinguished from projecting neurons, and, more important, GABAergic interneurons are continuously replaced. How the newly generated neurons integrate into a preexisting neural network and how basic network functions are maintained when a large percentage of neurons are subjected to continuous renewal are important questions that have recently received new insights. Here, it is seen that the production of bulbar interneurons is specifically adapted to experience-dependent regulation of adult neural networks. In particular, the authors report the degree of sensitivity of the bulbar neurogenesis to the activity level of sensory inputs and, in turn, how the adult neurogenesis adjusts the neural network functioning to optimize information processing. By maintaining a constitutive neurogenesis sensitive to environmental

State-dependent EEG in the hippocampus (HPC) has traditionally been divided into two activity patterns: theta, a large-amplitude, regular oscillation with a bandwidth of 3–12 Hz, and large-amplitude irregular activity (LIA), a less regular signal with broadband characteristics. Both of these activity patterns have been linked to the memory functions subserved by the HPC. Here we describe, using extracellular field recording techniques in naturally sleeping and urethane-anesthetized rats, a novel state present during deactivated stages of sleep and anesthesia that is characterized by a prominent large-amplitude and slow frequency (�1 Hz) rhythm. We have called this activity the hippocampal slow oscillation (SO) because of its similarity and correspondence with the previously described neocortical SO. Almost all hippocampal units recorded exhibited differential spiking behavior during the SO as compared with other states. Although the hippocampal SO occurred in situations similar to the neocortical SO, it demonstrated some independence in its initiation, coordination, and coherence. The SO was abolished by sensory stimulation or cholinergic agonism and was enhanced by increasing anesthetic depth or muscarinic receptor antagonism. Laminar profile analyses of the SO showed a phase shift and prominent current sink-source alternations in stratum lacunosum-moleculare of CA1. This, along with correlated slow oscillatory field and multiunit activity in superficial entorhinal cortex suggests that the hippocampal SO may be coordinated with slow neocortical activity through input arriving via the temporo-ammonic pathway. This novel state may present a favorable milieu for synchronization-dependent synaptic plasticity within and between hippocampal and neocortical ensembles. Key words: synchrony; state; sleep; memory; urethane; non-REM

Abstract. We briefly present some aspects of information processing in the mammalian visual system. The chapter focuses on the problem of scaleindependent object recognition. We provide a simple model, based on spiking neurons that make use of shunting inhibition in order to optimally select their driving afferent inputs. The model is able to resist to some degree to scale changes of the stimulus. We discuss possible mechanisms that the brain could use to achieve invariant object recognition and correlate our model with biophysical evidence.

this paper, we address the question whether, in the hippocampus, an interneuronal network can generate a coherent oscillatory output to the pyramidal neurons, thereby providing a substrate for the synaptic organization of coherent gamma population oscillations. In the behaving rat, physiologically identified interneurons were shown to fire spikes in the gamma frequency range and phase-locked to the local field waves (Bragin et al., 1995). Intracellular studies and immunochemical staining demonstrated that these interneurons are interconnected via GABAergic synapses (Lacaille et al., 1987; Sik et al., 1995; Gulyas et al., 1996). Theoretical studies suggest that these GABAergic interconnections may synchronize an interneuronal network when appropriate conditions on the time course of synaptic transmission are satisfied (Wang and Rinzel, 1992, 1993; van Vreeswijk et al., 1995). Moreover, in a recent in vitro experiment (Whittington et al., 1995; Traub et al., 1996), the excitatory glutamate AMPA and NMDA synaptic transmissions were blocked in the hippocampal slice. When metabotropic glutamate receptors were activated, transient oscillatory IPSPs in the 40 Hz frequency range were observed in pyramidal cells. These IPSPs were assumed to originate from the firing activities of fast-spiking interneurons synchronized by their interconnections. Computer simulations (Whittington et al., 1995; Traub et al., 1996) lend further support to this hypothesis

Fields of neural activity are seen in synchronized oscillations that are detected at mesoscopic scales in syntheses of multicellular recordings of action potentials and electroencephalograms (EEGs) over broad areas of cerebral cortex. The waves often have large-scale, highly textured spatial patterns of cortical activity that form in the context of associative learning under classical and operant conditioning in rabbits. The patterns show spatial amplitude modulation of shared oscillations of carrier waves in the beta and gamma ranges of the EEG, with recurrence at frame rates in the alpha and theta ranges. The frames also show spatial phase modulation that is inconsistent with driving of the oscillations by focal pacemakers. The hypothesis is developed that the synchronization manifests continuous distributions of activity in cortical neuropil that modulate firings of selected neural networks embedded in the neuropil. Five interactive agencies have been postulated to explain the mechanism for the field synchrony: electric fields; magnetic fields; electromagnetic fields (radio waves); diffusion chemical gradients; and order parameters that control self-organization of large populations of neurons by widespread synaptic interaction constituting negative and positive feedback. Only the last fits the data. The points are emphasized that these field patterns in frames require interactive neural dynamics that is modulated in respect to