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W J Freeman

Publications and source records attributed to W J Freeman.

9 recordsLinked to original sources
PubMed1991

Asymmetric sigmoid non-linearity in the rat olfactory system.

The statistical relationship between multi-unit spike activity and simultaneously recorded local dendritic field potentials in the olfactory system of the waking rat was studied with chronically placed electrodes. The relationship had the form of a sigmoid increase in axonal firing probability conditional on the amplitude of dendritic potentials. These data were fitted with an asymmetric sigmoid curve previously derived from the Hodgkin-Huxley equations. The curve was fitted using non-linear regression to optimize its parameter: the maximal firing rate. The maximal rate also gave the steepness of the slope of the sigmoid. Pulse trains were recorded from excitatory and inhibitory neurons in the olfactory cortex (including the anterior olfactory nucleus, the prepyriform cortex and the lateral entorhinal area) as identified by the phase relations of the pulse probability and the dendritic potentials, and from the excitatory neurons in the bulb (the inhibitory granule cells do not give extracellularly detectable action potentials). All these neurons are known to interact in disynaptic negative feedback loops giving rise to oscillations. The same sigmoid function fit the data from both types of neurons in all locations. The curves for neurons in all parts of the olfactory cortex had a 3-fold higher slope and maximal value than the curves from bulbar neurons. The significances of this difference and of the asymmetric sigmoid are discussed in terms of models for olfactory oscillatory dynamics and pattern recognition.

Anesthesia↗
PubMed1990

Correlations between unit firing and EEG in the rat olfactory system.

The olfactory EEG of awake animals displays oscillatory bursts of activity in the gamma- (30-100 Hz) range. The bursts are correlated with inflow of air over the receptor layer in the nose. None of the inputs to the cortices that display these oscillations carries periodic signals in the gamma-range. Thus these bursts are generated locally, either by neuronal feedback interactions or by coupling of oscillatory neurons. In the first case if the oscillations are generated by negative feedback, then two classes of cells must exist: excitatory neurons and inhibitory neurons with the same frequency of oscillation but with a quarter cycle phase lag by the inhibitory cells from the excitatory cells. On the other hand, if the EEG's result from coupling of cells that are intrinsically oscillatory, there should be a broad but monomodal distribution of phase values. In order to determine the origin of these bursts, we performed simultaneous recordings of EEG and multi-unit spikes in the 4 parts of the olfactory system (olfactory bulb, anterior olfactory nucleus, prepyriform cortex and lateral entorhinal area) of awake and motivated rats. For each sample, the EEG and the multi-unit spikes were recorded from the same local neighborhood. The multi-unit electrode recorded pulses from the principal output neurons of the respective cortical areas. In all locations tested, the oscillations in pulse probabilities of firing were found to have the same frequency as the dominant EEG frequency. In all 4 structures two sets of cells were found. One set displayed pulses in phase with the EEG and the other set displayed pulses that led or lagged the EEG by approximately 1/4 cycle. These data confirm the negative feedback interaction model rather than the coupled oscillator model for the generation of the bursts in the olfactory system. The relevance of these findings to other cortical systems, in casu the visual cortex is discussed.

Animals↗
PubMed1979

EEG analysis gives model of neuronal template-matching mechanism for sensory search with olfactory bulb.

The spatial pattern of EEG activity at the surface of the olfactory bulb tends to be invariant with respect to input and to change to a new pattern whenever an animal is trained to expect or search for a particular odor. It is postulated here that the spatial EEG pattern is dependent on a neural template for that odor that is formed during training. This hypothesis is expressed in the form of a model consisting of an array of interconnected elements (1 X 10 or 6 X 6). Each element represents 2 excitatory and 2 inhibitory subsets of neurons with 3 types of internal feedback: negative, mutually excitatory, and mutually inhibitory. The elements are interconnected only by mutual excitation and mutual inhibition. Each neural subset is represented by a nonlinear differential equation; the connections are represented by modifiable coupling coefficients. With appropriate values of the time, coupling, and gain coefficients, and with input that is modelled on olfactory input, the set of 40 or 144 equations gives output that simulates the time and space patterns of the EEG. In the naive state the coefficients are uniform. A template is formed by giving input to selected elements, cross-correlating the outputs, and weighting the mutually excitatory coupling coefficient between each pair of elements by the corresponding correlation coefficient. When a template has been formed, input to nontemplate elements is treated as noise. Optionally a matched filter is made to simulate habituation by reducing the synaptic gain coefficients of those excitatory subsets that receive the noise. The model is tested by giving input to nontemplate elements and to none, part or all of the template elements. There are two outputs of the model. One is the spatial pattern Vj of the root mean square (rms) amplitudes of the individual outputs v(j, t) of the elements. The other output is the rms amplitude Erms of the ensemble average E(t) over v(j, t). The results show that Vj depends on the template and is relatively insensitive to input, whether or not input is given to template elements. However, Erms increases in proportion to the number of "hits" on the template. If the number of elements receiving noise does not exceed the number of elements in a template, or if the noise is matched with a habituation filter, then Erms rises above the noise level for a "hit" on any one or more template elements irrespective of location or combination. Vj conforms to the performance of the surface EEG. Erms is not yet accessible to physiological measurement.

Animals↗
PubMed1979

Nonlinear dynamics of paleocortex manifested in the olfactory EEG.

The olfactory bulb is the first central component in a highly sensitive yet markedly stable sensory system. It receives a surge of receptor activity with each inspiration and transmits output as a brief burst of oscillatory activity that is most clearly seen in the EEG. These properties together with the known anatomy and physiology of the bulb are used as design criteria to synthesize, evaluate and solve a set of nonlinear differential equations that represent lumped bulbar dynamics. According to the model bulbar processing is in two stages. In the outer layers the interneurons perform the operations of input range compression, integration, clipping, holding, and bias control. In the inner layers the input surge is converted to a burst, which is transmitted by the mitral cells as a pulse density wave. The phase, frequency duration and amplitude of the wave convey information centrally about both the input and the state of the system. The model suffices to replicate the forms of the EEG burst; the pulse probability distributions conditional on the EEG; the waveforms of averaged evoked potentials (AEPs) and post stimulus time (PST) histograms from the bulb and cortex; and the changes in waveform induced by behavioral control of attentiveness and habituation. It is inferred that with selective attention there is a permanent change in the strength of mutually excitatory connections among excitatory neurons, and that with habituation there is a reversible change in the effectiveness of excitatory synapses. The limitations and deficiencies of the model and the need for centrifugal controls of bulbocortical function are discussed.

Action Potentials↗
PubMed1978

Models of the dynamics of neural populations.

Three requirements are posed for constructing models to simulate EEG dynamics. The element of the model should be an interactive ensemble of neurons and not single neurons. The observations must be statistical, such as EEG waves and averages of unit activity over time and over local neighborhoods containing neural subsets. The state variables and operations of the model must be clearly related to behavioral functions such as sensory reception and perception. A model is presented that exemplifies these requirements. Its key feature is the dependence of its levels of interaction on the level of its input, so that with each burst of input the model switches from an equilibrium state to a limit cycle state. A mechanism is described for coding sensory input into the spatial modulation of the limit cycle activity viewed as a carrier. It is suggested that sensory recepts and percepts exist at different hierarchical levels in the brain, recepts at the level of single neurones, and percepts at the level of neural ensembles, the latter being possibly manifested in the EEG.

Animals↗