Reading the spike coding of hypothalamic neurones presents a considerable challenge because they exhibit highly irregular firing patterns. Electrophysiologists working in the motor and sensory systems, in which neuron...
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Reading the spike coding of hypothalamic neurones presents a considerable challenge because they exhibit highly irregular firing patterns. Electrophysiologists working in the motor and sensory systems, in which neurones fire more regularly, have devised satisfactory methods to describe the firing of cells, although the statistical assumptions that underlie the methods do not apply to hypothalamic neurones. Measurement of neural activity is nevertheless vital to characterise the activity of neuroendocrine cells. It has thus become necessary to develop methods suitable for the analysis of the highly irregular spike discharge patterns of both spontaneous and stimulus-evoked firing of hypothalamic neurones. We review techniques used to meet this challenge and demonstrate their considerable capacity to address important physiological questions. We also introduce a novel approach for valid statistical estimation of the information conveyed by the response of a single neurone to a periodic stimulus. The approach demonstrated significant diurnal rhythms of synaptic connectivity between hypothalamic nuclei.
In the study of the neural code for taste, two theories have dominated the literature: the across neuron pattern (ANP), and the labeled line theories. Both of these theories are based on the observations that taste ce...
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In the study of the neural code for taste, two theories have dominated the literature: the across neuron pattern (ANP), and the labeled line theories. Both of these theories are based on the observations that taste cells are multisensitive across a variety of different taste stimuli. Given a fixed array of taste stimuli, a cell's particular set of sensitivities defines its response profile. The characteristics of response profiles are the basis of both major theories of coding. In reviewing the literature, it is apparent that response profiles are an expression of a complex interplay of excitatory and inhibitory inputs that derive from cells with a wide variety of sensitivity patterns. These observations suggest that, in the absence of inhibition, taste cells might be potentially responsive to all taste stimuli. Several studies also suggest that response profiles can be influenced by the taste context, defined as the taste stimulus presented just before or simultaneously with another, under which they are recorded. A theory, called dynamic coding, was proposed to account for context dependency of taste response profiles. In this theory, those cells that are unaffected by taste context would provide the signal, i.e., the information-containing portion of the ANP, and those cells whose responses are context dependent would provide noise, i.e., less stimulus specific information. When singular taste stimuli are presented, noise cells would provide amplification of the signal, and when complex mixtures are presented, the responses of the noise cells would be suppressed (depending on the particular combination of tastants), and the ratio of signal to noise would be enhanced. (C) 2000 Elsevier Science Inc. All rights reserved.
The advent of powerful perturbation tools, such as optogenetics, has created new frontiers for probing causal dependencies in neural and behavioral states. These approaches have significantly enhanced the ability to c...
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The advent of powerful perturbation tools, such as optogenetics, has created new frontiers for probing causal dependencies in neural and behavioral states. These approaches have significantly enhanced the ability to characterize the contribution of different cells and circuits to neural function in health and disease. They have shifted the emphasis of research toward causal interrogations and increased the demand for more precise and powerful tools to control and manipulate neural activity. Here, we clarify the conditions under which measurements and perturbations support causal inferences. We note that the brain functions at multiple scales and that causal dependencies may be best inferred with perturbation tools that interface with the system at the appropriate scale. Finally, we develop a geometric framework to facilitate the interpretation of causal experiments when brain perturbations do or do not respect the intrinsic patterns of brain activity. We describe the challenges and opportunities of applying perturbations in the presence of dynamics, and we close with a general perspective on navigating the activity space of neurons in the search for neural codes.
Many evolutionary years separate humans and macaques, and although the amygdala and cingulate cortex evolved to enable emotion and cognition in both, an evident functional gap exists. Although they were traditionally ...
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Many evolutionary years separate humans and macaques, and although the amygdala and cingulate cortex evolved to enable emotion and cognition in both, an evident functional gap exists. Although they were traditionally attributed to differential neuroanatomy, functional differences might also arise from coding mechanisms. Here we find that human neurons better utilize information capacity (efficient coding) than macaque neurons in both regions, and that cingulate neurons are more efficient than amygdala neurons in both species. In contrast, we find more overlap in the neural vocabulary and more synchronized activity (robustness coding) in monkeys in both regions and in the amygdala of both species. Our findings demonstrate a tradeoff between robustness and efficiency across species and regions. We suggest that this tradeoff can contribute to differential cognitive functions between species and underlie the complementary roles of the amygdala and the cingulate cortex. In turn, it can contribute to fragility underlying human psychopathologies.
The highly irregular firing of mammalian cortical pyramidal neurons is one of the most striking observation of the brain activity. This result affects greatly the discussion on the neural code, i.e. how the brain code...
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The highly irregular firing of mammalian cortical pyramidal neurons is one of the most striking observation of the brain activity. This result affects greatly the discussion on the neural code, i.e. how the brain codes information transmitted along the different cortical stages. In fact it seems to be in favor of one of the two main hypotheses about this issue, named the rate code. But the supporters of the contrasting hypothesis, the temporal code, consider this evidence inconclusive. We discuss here a leaky integrate-and-fire model of a hippocampal pyramidal neuron intended to be biologically sound to investigate the genesis of the irregular pyramidal firing and to give useful information about the coding problem. To this aim, the complete set of excitatory and inhibitory synapses impinging on such a neuron has been taken into account. The firing activity of the neuron model has been studied by computer simulation both in basic conditions and allowing brief periods of over-stimulation in specific regions of its synaptic constellation. Our results show neuronal firing conditions similar to those observed in experimental investigations on pyramidal cortical neurons. In particular, the variation coefficient (CV) computed from the inter-spike intervals (ISIs) in our simulations for basic conditions is close to the unity as that computed from experimental data. Our simulation shows also different behaviors in firing sequences for different frequencies of stimulation. (c) 2006 Elsevier Ireland Ltd. All rights reserved.
The 80 chorda tympani single fibers were selected following the rate of appearance of each response type of units for four taste stimuli in arbitrarily selected 400 fibers in the rat. These 80 fibers were distributed ...
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The 80 chorda tympani single fibers were selected following the rate of appearance of each response type of units for four taste stimuli in arbitrarily selected 400 fibers in the rat. These 80 fibers were distributed in the matrix with 80 squares in order of the units with sucrose, NaCl, acid and quinine sensitivities from left to right, and also with the intention to make the monotonic function for each quality accomplished by putting the units in appropriate columns. Thus, we could satisfactorily demonstrate the possibility to represent the pictures of across fiber response patterns quantitatively as bar graphs in the matrix. As the matrix patterns for 0.01 M to 0.1 M NaCl were almost the same, the matrix pattern is considered to represent only the taste quality. The matrix patterns obtained for DL-alanine, Na-saccharin, MSG and AgNO 3 were well consistent with results of electrophysiological, behavioral and psychophysical studies.
Image compression still remains one of the most challenging scientific fields as it is widely used in almost every kind of application. To design groundbreaking architectures the image processing community concentrate...
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Various classes of neurons alternate between high-frequency discharges and silent intervals. This phenomenon is called burst firing. To analyze burst activity in an insect system, grasshopper auditory receptor neurons...
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Various classes of neurons alternate between high-frequency discharges and silent intervals. This phenomenon is called burst firing. To analyze burst activity in an insect system, grasshopper auditory receptor neurons were recorded in vivo for several distinct stimulus types. The experimental data show that both burst probability and burst characteristics are strongly influenced by temporal modulations of the acoustic stimulus. The tendency to burst, hence, is not only determined by cell-intrinsic processes, but also by their interaction with the stimulus time course. We study this interaction quantitatively and observe that bursts containing a certain number of spikes occur shortly after stimulus deflections of specific intensity and duration. Our findings suggest a sparse neural code where information about the stimulus is represented by the number of spikes per burst, irrespective of the detailed interspike-interval structure within a burst. This compact representation cannot be interpreted as a firing-rate code. An information-theoretical analysis reveals that the number of spikes per burst reliably conveys information about the amplitude and duration of sound transients, whereas their time of occurrence is reflected by the burst onset time. The investigated neurons encode almost half of the total transmitted information in burst activity.
This work introduces neural Chronos Ordinary Differential Equations (neural code), a deep neural network architecture that fits a continuous-time ODE dynamics for predicting the chronology of a system both forward and...
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This work introduces neural Chronos Ordinary Differential Equations (neural code), a deep neural network architecture that fits a continuous-time ODE dynamics for predicting the chronology of a system both forward and backward in time. To train the model, we solve the ODE as an initial value problem and a final value problem, similar to neural ODEs. We also explore two approaches to combining neural code with Recurrent neural Networks by replacing neural ODE with neural code (code-RNN), and incorporating a bidirectional RNN for full information flow in both time directions (code-BiRNN). Variants with other update cells namely GRU and LSTM are also considered and referred to as: code-GRU, code-BiGRU, code-LSTM, code-BiLSTM. Experimental results demonstrate that neural code outperforms neural ODE in learning the dynamics of a spiral forward and backward in time, even with sparser data. We also compare the performance of code-RNN/GRU/-LSTM and code-BiRNN/-BiGRU/-BiLSTM against ODE-RNN/-GRU/-LSTM on three real-life time-series data tasks: imputation of missing data for lower and higher dimensional data, and forward and backward extrapolation with shorter and longer time horizons. Our findings show that the proposed architectures converge faster, with code-BiRNN/-BiGRU/-BiLSTM consistently outperforming the other architectures on all tasks, achieving a notably smaller mean squared error-often reduced by up to an order of magnitude.
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