Studies on the neuronal correlates of decision making have demonstrated that the continuous flow of sensorial information is integrated by sensorimotor brain areas in order to select one among simultaneously represent...
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Studies on the neuronal correlates of decision making have demonstrated that the continuous flow of sensorial information is integrated by sensorimotor brain areas in order to select one among simultaneously represented targets and potential actions. In contrast, little is known about how these areas integrate memory information to lead to similar decisions. Using serial order learning, we explore how fragments of information, learned and stored independently (e.g., A > B and B > C), are linked in an abstract representation according to their reciprocal relations (such as A > B > C) and how this representation can be accessed and manipulated to make decisions. We show that manipulating information after learning occurs with increased difficulty as logical relationships get closer in the mental map and that the activity of neurons in the dorsal premotor cortex (PMd) encodes the difficulty level during target selection for motor decision making at the single-neuron and population levels.
The prelimbic (PL) area and basolateral amygdala (lateral [LA] and basolateral [BL] nuclei) have closely related functions and similar extrinsic connectivity. Reasoning that the computational advantage of such redunda...
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The prelimbic (PL) area and basolateral amygdala (lateral [LA] and basolateral [BL] nuclei) have closely related functions and similar extrinsic connectivity. Reasoning that the computational advantage of such redundancy should be reflected in differences in how these structures represent information, we compared the coding properties of PL and amygdala neurons during a task that requires rats to produce different conditioned defensive or appetitive behaviors. Rather than unambiguous regional differences in the identities of the variables encoded, we found gradients in how the same variables are represented. Whereas PL and BL neurons represented many different parameters through minor variations in firing rates, LA cells coded fewer task features with stronger changes in activity. At the population level, whereas valence could be easily distinguished from amygdala activity, PL neurons could distinguish both valence and trial identity as well as or better than amygdala neurons. Thus, PL has greater representational capacity.
populations of neurons represent sensory, motor, and cognitive variables via patterns of activity distributed across the population. The size of the population used to encode a variable is typically much greater than ...
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populations of neurons represent sensory, motor, and cognitive variables via patterns of activity distributed across the population. The size of the population used to encode a variable is typically much greater than the dimension of the variable itself, and thus, the corresponding neural population activity occupies lower-dimensional subsets of the full set of possible activity states. Given population activity data with such lower-dimensional structure, a fundamental question asks how close the low-dimensional data lie to a linear subspace. The linearity or nonlinearity of the low-dimensional structure reflects important computational features of the encoding, such as robustness and generalizability. Moreover, identifying such linear structure underlies common data analysis methods such as Principal Component Analysis (PCA). Here, we show that for data drawn from many common population codes the resulting point clouds and manifolds are exceedingly nonlinear, with the dimension of the best-fitting linear subspace growing at least exponentially with the true dimension of the data. Consequently, linear methods like PCA fail dramatically at identifying the true underlying structure, even in the limit of arbitrarily many data points and no noise.
We investigated the role of feedback gain in optimal feedback control (OFC) theory using a neuromotor system. Neural studies have shown that directional tuning, known as the "preferred direction" (PD), is a ...
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ISBN:
(纸本)9781479957637
We investigated the role of feedback gain in optimal feedback control (OFC) theory using a neuromotor system. Neural studies have shown that directional tuning, known as the "preferred direction" (PD), is a basic functional property of cell activity in the primary motor cortex (Ml). However, it is not clear which directions the Ml codes for, because neural activities can correlate with several directional parameters, such as joint torque and end-point motion. Thus, to examine the computational mechanism in the Ml, we modeled the isometric motor task of a musculoskeletal system required to generate the desired joint torque. Then, we computed the optimal feedback gain according to OFC. The feedback gain indicated directional tunings of the joint torque and end-point motion in Cartesian space that were similar to the Ml neuron PDs observed in previous studies. Thus, we suggest that the Ml acts as a feedback gain in OFC.
This paper introduces a novel neuro-dynamic system for adaptive online clustering using populations of spiking neurons and spike-timing dependent plasticity (STDP). Real-valued data samples are temporally encoded into...
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ISBN:
(纸本)9781467314886
This paper introduces a novel neuro-dynamic system for adaptive online clustering using populations of spiking neurons and spike-timing dependent plasticity (STDP). Real-valued data samples are temporally encoded into spike events, used by biological neurons to encode information and communicate with one another, and clusters are represented by spiking neuron populations of varying size. The number of clusters is unknown a priori and clusters are learned in an online fashion where each data sample is provided only once. The coincidence detection capability of spiking neurons is utilized for data clustering and clusters are dynamically formed. The structure of the spiking neural network is constantly adjusted through adding and pruning of neuron populations. Besides, the number of neurons within each population constantly adapts as new data arrives. STDP is employed to adjust the strength of synaptic connections and enhance the selectivity of each population to its corresponding group of data. Preliminary experiments were carried out on synthetic and selected benchmark datasets to evaluate the performance of the proposed system. Promising results were obtained, which indicate the viability of spike-based population coding for online data clustering.
Many concepts in neuromuscular physiology can be difficult for instructors to teach, and for students to understand. The behaviors of various components in neuromuscular systems do not always interact in obvious ways,...
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Many concepts in neuromuscular physiology can be difficult for instructors to teach, and for students to understand. The behaviors of various components in neuromuscular systems do not always interact in obvious ways, and the function of hundreds of components can be very different from the function of just one or two ''representatives.'' In this paper, a simulator is presented that can model both small and large spinal circuitry systems thus allowing students to explore the dynamic functional implications of the static circuitry diagrams that are common in many neuroscience textbooks. The simulator brings to life many concepts in neuromuscular physiology and permits students to explore such concepts without extensive supervision. The benefits and drawbacks of using this kind of simulator in the classroom are discussed, based on initial field tests with undergraduate and graduate students as well as input from the literature. It was found that such a simulation can be very useful as a teaching tool if it is used properly with the right audience.
In the brain, information is encoded, transmitted and used to inform behaviour at the level of timing of action potentials distributed over population of neurons. To implement neural-like systems in silico, to emulate...
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In the brain, information is encoded, transmitted and used to inform behaviour at the level of timing of action potentials distributed over population of neurons. To implement neural-like systems in silico, to emulate neural function, and to interface successfully with the brain, neuromorphic circuits need to encode information in a way compatible to that used by populations of neuron in the brain. To facilitate the cross-talk between neuromorphic engineering and neuroscience, in this review we first critically examine and summarize emerging recent findings about how population of neurons encode and transmit information. We examine the effects on encoding and readout of information for different features of neural population activity, namely the sparseness of neural representations, the heterogeneity of neural properties, the correlations among neurons, and the timescales (from short to long) at which neurons encode information and maintain it consistently over time. Finally, we critically elaborate on how these facts constrain the design of information coding in neuromorphic circuits. We focus primarily on the implications for designing neuromorphic circuits that communicate with the brain, as in this case it is essential that artificial and biological neurons use compatible neural codes. However, we also discuss implications for the design of neuromorphic systems for implementation or emulation of neural computation.
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